From be0a8417269db21766e52838c6fce1614c56cce9 Mon Sep 17 00:00:00 2001
From: Anas Khan <83116240+anxkhn@users.noreply.github.com>
Date: Mon, 20 Oct 2025 23:14:47 +0530
Subject: [PATCH 1/5] feat: add support for multiple LLM providers (ollama,
 gemini)

---
 .env.example                          |  22 ++++
 README.md                             |  79 ++++++++++--
 chat/components/navbar.py             |   2 +
 chat/components/provider_selector.py  | 171 ++++++++++++++++++++++++++
 chat/llm_providers/__init__.py        |   4 +
 chat/llm_providers/base.py            |  77 ++++++++++++
 chat/llm_providers/factory.py         | 105 ++++++++++++++++
 chat/llm_providers/google_provider.py | 165 +++++++++++++++++++++++++
 chat/llm_providers/ollama_provider.py | 159 ++++++++++++++++++++++++
 chat/llm_providers/openai_provider.py | 122 ++++++++++++++++++
 chat/state.py                         | 138 +++++++++++++--------
 requirements.txt                      |   3 +
 12 files changed, 989 insertions(+), 58 deletions(-)
 create mode 100644 .env.example
 create mode 100644 chat/components/provider_selector.py
 create mode 100644 chat/llm_providers/__init__.py
 create mode 100644 chat/llm_providers/base.py
 create mode 100644 chat/llm_providers/factory.py
 create mode 100644 chat/llm_providers/google_provider.py
 create mode 100644 chat/llm_providers/ollama_provider.py
 create mode 100644 chat/llm_providers/openai_provider.py

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..ffc89c1
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,22 @@
+# LLM Provider Configuration Examples
+# Copy this file to .env and fill in your actual API keys
+
+# Choose which LLM provider to use: openai, ollama, or gemini
+LLM_PROVIDER=openai
+
+# OpenAI Configuration (required for OpenAI provider)
+OPENAI_API_KEY=your_openai_api_key_here
+OPENAI_MODEL=gpt-5-mini
+
+# Ollama Configuration (for local models)
+OLLAMA_HOST=http://localhost:11434
+OLLAMA_MODEL=gemma3:4b
+
+# Google Gemini Configuration (required for Google provider)
+GEMINI_API_KEY=your_gemini_api_key_here
+GEMINI_MODEL=gemini-2.0-flash
+
+# Optional: Use Google Vertex AI instead of the direct API
+# GOOGLE_USE_VERTEXAI=true
+# GOOGLE_PROJECT_ID=your-gcp-project-id
+# GOOGLE_LOCATION=us-central1
\ No newline at end of file
diff --git a/README.md b/README.md
index 4f77749..ebab33c 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Reflex Chat App
 
-A user-friendly, highly customizable Python web app designed to demonstrate LLMs in a ChatGPT format.
+A user-friendly, highly customizable Python web app designed to demonstrate LLMs in a ChatGPT format with support for multiple LLM providers.
 
 <div align="center">
 <img src="./docs/demo.gif" alt="icon"/>
@@ -8,26 +8,69 @@ A user-friendly, highly customizable Python web app designed to demonstrate LLMs
 
 # Getting Started
 
-You'll need a valid OpenAI subscription - save your API key under the environment variable `OPENAI_API_KEY`:
+## Choose Your LLM Provider
+
+The app supports three LLM providers. Configure the one you want to use:
+
+### Option 1: OpenAI
+
+You'll need a valid OpenAI subscription. Set your API key and model:
 
 ```bash
-export OPENAI_API_KEY="YOUR_OPENAI_API_KEY" # replace me!
+export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
+export LLM_PROVIDER="openai"
+export OPENAI_MODEL="gpt-5-mini"
+```
+
+### Option 2: Ollama (Local Models)
+
+Run models locally using Ollama:
+
+1. [Install Ollama](https://ollama.ai/download)
+2. Start Ollama server: `ollama serve`
+3. Pull a model: `ollama pull gemma3:4b`
+4. Configure environment:
+
+```bash
+export LLM_PROVIDER="ollama"
+export OLLAMA_HOST="http://localhost:11434"  # optional, default
+export OLLAMA_MODEL="gemma3:4b"  # required: must be a model you have installed
+```
+
+### Option 3: Google Gemini
+
+Use Google's Gemini models:
+
+```bash
+export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
+export LLM_PROVIDER="gemini"
+export GEMINI_MODEL="gemini-2.0-flash"
+```
+
+For Vertex AI:
+
+```bash
+export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
+export GOOGLE_USE_VERTEXAI="true"
+export GOOGLE_PROJECT_ID="your-gcp-project-id"
+export GOOGLE_LOCATION="us-central1"  # optional
 ```
 
 ### 🧬 1. Clone the Repo
 
 ```bash
 git clone https://github.com/reflex-dev/reflex-chat.git
+cd reflex-chat
 ```
 
-### 📦 2. Install Reflex
+### 📦 2. Install Dependencies
 
 To get started with Reflex, you'll need:
 
 - Python 3.10+
-- Pip dependencies: `reflex`, `openai`
+- pip dependencies: `reflex`, `openai`, `ollama`, `google-genai`
 
-Install `pip` dependencies with the provided `requirements.txt`:
+Install all dependencies with the provided `requirements.txt`:
 
 ```bash
 pip install -r requirements.txt
@@ -37,11 +80,33 @@ pip install -r requirements.txt
 
 Initialize and run the app:
 
-```
+```bash
 reflex init
 reflex run
 ```
 
+#### Model Specification:
+
+- **OpenAI**: `OPENAI_MODEL` (your chosen model, e.g., gpt-5-mini, gpt-4o, etc.)
+- **Ollama**: `OLLAMA_MODEL` (your local model, e.g., gemma3:4b, llama3.2, etc.)
+  - Note: Ollama requests use `num_ctx=4096` by default for optimal context window
+- **Google**: `GEMINI_MODEL` (your chosen model, e.g., gemini-2.0-flash, gemini-2.5-pro, etc.)
+
+## Environment Variables Reference
+
+| Variable              | Required      | Default                  | Description                          |
+| --------------------- | ------------- | ------------------------ | ------------------------------------ |
+| `LLM_PROVIDER`        | No            | `openai`                 | Choose: `openai`, `ollama`, `gemini` |
+| `OPENAI_API_KEY`      | For OpenAI    | -                        | Your OpenAI API key                  |
+| `OPENAI_MODEL`        | For OpenAI    | -                        | OpenAI model to use                  |
+| `OLLAMA_HOST`         | No            | `http://localhost:11434` | Ollama server URL                    |
+| `OLLAMA_MODEL`        | For Ollama    | -                        | Ollama model to use                  |
+| `GEMINI_API_KEY`      | For Gemini    | -                        | Your Google API key                  |
+| `GEMINI_MODEL`        | For Gemini    | -                        | Gemini Model to use                  |
+| `GOOGLE_USE_VERTEXAI` | No            | `false`                  | Use Vertex AI instead                |
+| `GOOGLE_PROJECT_ID`   | For Vertex AI | -                        | GCP project ID                       |
+| `GOOGLE_LOCATION`     | No            | `us-central1`            | GCP region                           |
+
 # Features
 
 - 100% Python-based, including the UI, using Reflex
diff --git a/chat/components/navbar.py b/chat/components/navbar.py
index e71f91f..2229e2f 100644
--- a/chat/components/navbar.py
+++ b/chat/components/navbar.py
@@ -1,5 +1,6 @@
 import reflex as rx
 from chat.state import State
+from chat.components.provider_selector import provider_status_bar
 
 
 def sidebar_chat(chat: str) -> rx.Component:
@@ -98,6 +99,7 @@ def navbar():
             variant="soft",
             margin_inline_end="auto",
         ),
+        provider_status_bar(),
         modal(
             rx.icon_button("message-square-plus"),
         ),
diff --git a/chat/components/provider_selector.py b/chat/components/provider_selector.py
new file mode 100644
index 0000000..d93a8c4
--- /dev/null
+++ b/chat/components/provider_selector.py
@@ -0,0 +1,171 @@
+import reflex as rx
+from chat.state import State
+
+
+def provider_selector() -> rx.Component:
+    """Create a provider selection component."""
+    return rx.card(
+        rx.vstack(
+            rx.heading("LLM Provider Settings", size="4", weight="bold"),
+            rx.text(
+                "Choose your LLM provider and model",
+                size="2",
+                color=rx.color("mauve", 10),
+            ),
+            # Provider Selection
+            rx.hstack(
+                rx.text("Provider:", weight="medium"),
+                rx.select(
+                    State.available_providers,
+                    placeholder="Select provider",
+                    value=State.current_provider,
+                    on_change=State.set_provider,
+                    width="200px",
+                ),
+                rx.spinner(
+                    size="2",
+                    loading=State.processing,
+                ),
+                align="center",
+                spacing="3",
+            ),
+            # Model Selection
+            rx.hstack(
+                rx.text("Model:", weight="medium"),
+                rx.select(
+                    State.available_models,
+                    placeholder="Select model",
+                    value=State.current_model,
+                    on_change=State.set_model,
+                    width="250px",
+                ),
+                rx.cond(
+                    State.available_models.length() == 0,
+                    rx.tooltip(
+                        rx.icon("info", size=16),
+                        content="No models available. Check your provider configuration.",
+                    ),
+                ),
+                align="center",
+                spacing="3",
+            ),
+            # Error Display
+            rx.cond(
+                State.provider_error != "",
+                rx.callout(
+                    rx.icon("triangle_alert", size=4),
+                    rx.text(State.provider_error),
+                    color_scheme="red",
+                    variant="soft",
+                ),
+            ),
+            # Status Indicator
+            rx.hstack(
+                rx.cond(
+                    State._provider_instance != None,
+                    rx.hstack(
+                        rx.icon("check-circle", size=4, color=rx.color("green", 10)),
+                        rx.text(
+                            "Provider ready", size="2", color=rx.color("green", 10)
+                        ),
+                    ),
+                    rx.hstack(
+                        rx.icon("x-circle", size=4, color=rx.color("red", 10)),
+                        rx.text(
+                            "Provider not configured",
+                            size="2",
+                            color=rx.color("red", 10),
+                        ),
+                    ),
+                ),
+                rx.spacer(),
+                rx.button(
+                    "Refresh Models",
+                    size="2",
+                    variant="outline",
+                    on_click=State.update_available_models,
+                    loading=State.processing,
+                ),
+                align="center",
+                width="100%",
+            ),
+            # Configuration Help
+            rx.divider(),
+            rx.accordion.root(
+                rx.accordion.item(
+                    header="Configuration Help",
+                    content=rx.vstack(
+                        rx.text("Environment variables needed:", weight="medium"),
+                        rx.code_block(
+                            """# OpenAI
+OPENAI_API_KEY=your_openai_api_key
+OPENAI_MODEL=gpt-5-mini
+
+# Ollama (local models)
+OLLAMA_HOST=http://localhost:11434
+OLLAMA_MODEL=gemma3:4b
+
+# Google Gemini
+GEMINI_API_KEY=your_GEMINI_API_KEY
+GEMINI_MODEL=gemini-2.0-flash
+
+# Provider Selection
+LLM_PROVIDER=openai  # or ollama, gemini
+""",
+                            language="bash",
+                            width="100%",
+                        ),
+                        rx.text(
+                            "Note: Models are dynamically fetched when possible. If no models appear, ensure your MODEL environment variable is set.",
+                            size="1",
+                        ),
+                        align="start",
+                        spacing="2",
+                    ),
+                    value="config-help",
+                ),
+                collapsible=True,
+                width="100%",
+            ),
+            spacing="4",
+            align="stretch",
+            width="100%",
+        ),
+        padding="20px",
+        radius="12px",
+        background_color=rx.color("mauve", 1),
+        border=f"1px solid {rx.color('mauve', 3)}",
+        width="100%",
+        max_width="600px",
+    )
+
+
+def provider_status_bar() -> rx.Component:
+    """Create a compact provider status bar for the navbar."""
+    return rx.hstack(
+        rx.icon("bot", size=4, color=rx.color("accent", 10)),
+        rx.text(
+            f"{State.current_provider}: {State.current_model}",
+            size="2",
+            weight="medium",
+            color=rx.color("mauve", 11),
+        ),
+        rx.cond(
+            State.provider_error != "",
+            rx.tooltip(
+                rx.icon("triangle_alert", size=3, color=rx.color("red", 10)),
+                content=State.provider_error,
+            ),
+        ),
+        rx.cond(
+            State.processing,
+            rx.spinner(size="2", color=rx.color("accent", 10)),
+        ),
+        align="center",
+        spacing="2",
+        padding_x="12px",
+        padding_y="6px",
+        radius="8px",
+        background_color=rx.color("mauve", 2),
+        border=f"1px solid {rx.color('mauve', 4)}",
+    )
diff --git a/chat/llm_providers/__init__.py b/chat/llm_providers/__init__.py
new file mode 100644
index 0000000..034cf55
--- /dev/null
+++ b/chat/llm_providers/__init__.py
@@ -0,0 +1,4 @@
+from .base import LLMProvider, Message
+from .factory import LLMProviderFactory
+
+__all__ = ["LLMProvider", "Message", "LLMProviderFactory"]
diff --git a/chat/llm_providers/base.py b/chat/llm_providers/base.py
new file mode 100644
index 0000000..27c7e3a
--- /dev/null
+++ b/chat/llm_providers/base.py
@@ -0,0 +1,77 @@
+from abc import ABC, abstractmethod
+from typing import AsyncGenerator, Dict, List, Any
+import asyncio
+
+
+class Message:
+    """Standardized message format for all providers."""
+
+    def __init__(self, role: str, content: str):
+        self.role = role  # "system", "user", or "assistant"
+        self.content = content
+
+    def to_dict(self) -> Dict[str, str]:
+        return {"role": self.role, "content": self.content}
+
+
+class LLMProvider(ABC):
+    """Abstract base class for LLM providers."""
+
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+
+    @abstractmethod
+    async def initialize(self) -> None:
+        """Initialize the provider (setup clients, validate configuration)."""
+        pass
+
+    @abstractmethod
+    async def get_available_models(self) -> List[str]:
+        """Get list of available models for this provider."""
+        pass
+
+    @abstractmethod
+    async def stream_chat(
+        self, messages: List[Message], model: str = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream chat completion response.
+
+        Args:
+            messages: List of Message objects representing conversation history
+            model: Model name to use (optional, uses default if not provided)
+
+        Yields:
+            str: Response content chunks
+        """
+        pass
+
+    @abstractmethod
+    def get_default_model(self) -> str:
+        """Get the default model for this provider."""
+        pass
+
+    @abstractmethod
+    def validate_config(self) -> List[str]:
+        """
+        Validate provider configuration.
+
+        Returns:
+            List[str]: List of validation errors, empty if valid
+        """
+        pass
+
+    def convert_to_provider_format(
+        self, messages: List[Message]
+    ) -> List[Dict[str, str]]:
+        """Convert standardized messages to provider-specific format."""
+        return [msg.to_dict() for msg in messages]
+
+    async def health_check(self) -> bool:
+        """Check if the provider is accessible and properly configured."""
+        try:
+            await self.initialize()
+            models = await self.get_available_models()
+            return len(models) > 0
+        except Exception:
+            return False
diff --git a/chat/llm_providers/factory.py b/chat/llm_providers/factory.py
new file mode 100644
index 0000000..60e8eb4
--- /dev/null
+++ b/chat/llm_providers/factory.py
@@ -0,0 +1,105 @@
+import os
+from typing import Dict, Any, List
+from .base import LLMProvider
+from .openai_provider import OpenAIProvider
+from .ollama_provider import OllamaProvider
+from .google_provider import GoogleGenAIProvider
+
+
+class LLMProviderFactory:
+    """Factory class for creating LLM provider instances."""
+
+    SUPPORTED_PROVIDERS = {
+        "openai": OpenAIProvider,
+        "ollama": OllamaProvider,
+        "gemini": GoogleGenAIProvider,
+    }
+
+    @classmethod
+    def create_provider(cls, provider_name: str = None) -> LLMProvider:
+        """
+        Create an LLM provider instance.
+
+        Args:
+            provider_name: Name of the provider (openai, ollama, google).
+                         If None, uses LLM_PROVIDER environment variable or defaults to "openai".
+
+        Returns:
+            LLMProvider: Configured provider instance
+
+        Raises:
+            ValueError: If provider is not supported or configuration is invalid
+        """
+        if provider_name is None:
+            provider_name = os.getenv("LLM_PROVIDER", "openai").lower()
+
+        if provider_name not in cls.SUPPORTED_PROVIDERS:
+            raise ValueError(
+                f"Unsupported provider: {provider_name}. "
+                f"Supported providers: {list(cls.SUPPORTED_PROVIDERS.keys())}"
+            )
+
+        provider_class = cls.SUPPORTED_PROVIDERS[provider_name]
+        config = cls._get_provider_config(provider_name)
+
+        provider = provider_class(config)
+        validation_errors = provider.validate_config()
+        if validation_errors:
+            raise ValueError(
+                f"Configuration errors for {provider_name}: {validation_errors}"
+            )
+
+        return provider
+
+    @classmethod
+    def _get_provider_config(cls, provider_name: str) -> Dict[str, Any]:
+        """Get configuration for a specific provider from environment variables."""
+        config = {}
+
+        if provider_name == "openai":
+            config = {
+                "api_key": os.getenv("OPENAI_API_KEY"),
+                "model": os.getenv("OPENAI_MODEL"),
+            }
+
+        elif provider_name == "ollama":
+            config = {
+                "host": os.getenv("OLLAMA_HOST", "http://localhost:11434"),
+                "model": os.getenv("OLLAMA_MODEL"),
+            }
+
+        elif provider_name == "gemini":
+            config = {
+                "api_key": os.getenv("GEMINI_API_KEY"),
+                "model": os.getenv("GEMINI_MODEL"),
+                "project_id": os.getenv("GOOGLE_PROJECT_ID"),
+                "location": os.getenv("GOOGLE_LOCATION", "us-central1"),
+                "use_vertexai": os.getenv("GOOGLE_USE_VERTEXAI", "false").lower()
+                == "true",
+            }
+
+        return config
+
+    @classmethod
+    def get_supported_providers(cls) -> List[str]:
+        """Get list of supported provider names."""
+        return list(cls.SUPPORTED_PROVIDERS.keys())
+
+    @classmethod
+    async def get_available_providers(cls) -> Dict[str, bool]:
+        """
+        Check which providers are available and properly configured.
+
+        Returns:
+            Dict[str, bool]: Mapping of provider names to availability status
+        """
+        availability = {}
+
+        for provider_name in cls.SUPPORTED_PROVIDERS.keys():
+            try:
+                provider = cls.create_provider(provider_name)
+                availability[provider_name] = await provider.health_check()
+            except Exception:
+                availability[provider_name] = False
+
+        return availability
diff --git a/chat/llm_providers/google_provider.py b/chat/llm_providers/google_provider.py
new file mode 100644
index 0000000..e73c56b
--- /dev/null
+++ b/chat/llm_providers/google_provider.py
@@ -0,0 +1,165 @@
+from typing import AsyncGenerator, List, Dict, Any
+import asyncio
+from .base import LLMProvider, Message
+
+
+class GoogleGenAIProvider(LLMProvider):
+    """Google GenAI provider implementation for Gemini models."""
+
+    def __init__(self, config: Dict[str, Any]):
+        super().__init__(config)
+        self.client = None
+
+    async def initialize(self) -> None:
+        """Initialize the Google GenAI client."""
+        try:
+            from google import genai
+        except ImportError:
+            raise ImportError(
+                "Google GenAI package is not installed. Install it with: pip install google-genai"
+            )
+
+        if not self.config.get("api_key"):
+            raise ValueError("Google API key is required")
+
+        # Initialize client based on configuration
+        if self.config.get("use_vertexai", False):
+            # Use Vertex AI
+            if not self.config.get("project_id"):
+                raise ValueError("Google project ID is required for Vertex AI")
+
+            self.client = genai.Client(
+                vertexai=True,
+                project=self.config["project_id"],
+                location=self.config.get("location", "us-central1"),
+                api_key=self.config["api_key"],
+            )
+        else:
+            # Use Developer API
+            self.client = genai.Client(api_key=self.config["api_key"])
+
+    async def get_available_models(self) -> List[str]:
+        """Get list of available Google Gemini models."""
+        if self.client is None:
+            await self.initialize()
+
+        try:
+            # Try to get models from the API if available
+            # Note: Google GenAI may not have a public list models API
+            # For now, return empty list - user must specify model manually
+            return []
+        except Exception:
+            return []
+
+    async def stream_chat(
+        self, messages: List[Message], model: str = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream chat completion from Google GenAI.
+
+        Args:
+            messages: List of Message objects
+            model: Model name to use
+
+        Yields:
+            str: Response content chunks
+        """
+        if self.client is None:
+            await self.initialize()
+
+        if model is None:
+            model = self.get_default_model()
+
+        # Convert messages to Google format
+        gemini_messages = self._convert_messages_to_gemini_messages(messages)
+
+        try:
+            # Use async client for streaming
+            async with self.client.aio as aclient:
+                response = await aclient.models.generate_content(
+                    model=model,
+                    contents=gemini_messages,
+                )
+
+                # Stream the response
+                if hasattr(response, "text"):
+                    # If response is complete, yield all at once
+                    yield response.text
+                else:
+                    # Try to stream if available
+                    async for chunk in response:
+                        if hasattr(chunk, "text") and chunk.text:
+                            yield chunk.text
+
+        except Exception as e:
+            # Fallback to sync client
+            try:
+                response = self.client.models.generate_content(
+                    model=model,
+                    contents=gemini_messages,
+                )
+                if hasattr(response, "text"):
+                    yield response.text
+            except Exception as fallback_error:
+                raise Exception(
+                    f"Google GenAI streaming error: {str(e)} (fallback: {str(fallback_error)})"
+                )
+
+    def get_default_model(self) -> str:
+        """Get the configured Google GenAI model."""
+        model = self.config.get("model")
+        if not model:
+            raise ValueError("GEMINI_MODEL environment variable is required")
+        return model
+
+    def validate_config(self) -> List[str]:
+        """Validate Google GenAI configuration."""
+        errors = []
+
+        if not self.config.get("api_key"):
+            errors.append("GEMINI_API_KEY environment variable is required")
+
+        if self.config.get("use_vertexai", False):
+            if not self.config.get("project_id"):
+                errors.append("GOOGLE_PROJECT_ID is required when using Vertex AI")
+
+        model = self.config.get("model")
+        if not model:
+            errors.append("GEMINI_MODEL environment variable is required")
+        elif not isinstance(model, str):
+            errors.append("GEMINI_MODEL must be a valid string")
+
+        return errors
+
+    def _convert_messages_to_gemini_messages(
+        self, messages: List[Message]
+    ) -> List[str]:
+        """
+        Convert standardized Message objects to Google GenAI format.
+
+        Google GenAI uses a simpler content format where each message is a string.
+        We'll combine system messages with user messages.
+        """
+        gemini_messages = []
+        system_message = ""
+
+        # Extract system message
+        for message in messages:
+            if message.role == "system":
+                system_message = message.content
+                break
+
+        # Build conversation history
+        for i, message in enumerate(messages):
+            if message.role == "system":
+                continue  # Skip system messages here
+            elif message.role == "user":
+                content = message.content
+                if system_message and i == 0:
+                    # Add system message to first user message
+                    content = f"{system_message}\n\n{content}"
+                gemini_messages.append(content)
+            elif message.role == "assistant":
+                gemini_messages.append(f"Assistant: {message.content}")
+
+        return gemini_messages
diff --git a/chat/llm_providers/ollama_provider.py b/chat/llm_providers/ollama_provider.py
new file mode 100644
index 0000000..c0168bb
--- /dev/null
+++ b/chat/llm_providers/ollama_provider.py
@@ -0,0 +1,159 @@
+from typing import AsyncGenerator, List, Dict, Any
+import asyncio
+from .base import LLMProvider, Message
+
+
+class OllamaProvider(LLMProvider):
+    """Ollama LLM provider implementation for local models.
+
+    Default options applied to all requests:
+    - num_ctx: 4096 (context window size)
+    """
+
+    def __init__(self, config: Dict[str, Any]):
+        super().__init__(config)
+        self.client = None
+
+    async def initialize(self) -> None:
+        """Initialize the Ollama client."""
+        try:
+            # Import ollama here to avoid import errors if not installed
+            import ollama
+
+            self.client = ollama.Client(
+                host=self.config.get("host", "http://localhost:11434")
+            )
+        except ImportError:
+            raise ImportError(
+                "Ollama package is not installed. Install it with: pip install ollama"
+            )
+        except Exception as e:
+            raise Exception(
+                f"Failed to connect to Ollama at {self.config.get('host')}: {str(e)}"
+            )
+
+    async def get_available_models(self) -> List[str]:
+        """Get list of available local Ollama models."""
+        if self.client is None:
+            await self.initialize()
+
+        try:
+            # Run in thread pool to avoid blocking
+            models = await asyncio.get_event_loop().run_in_executor(
+                None, self.client.list
+            )
+            return [model["name"] for model in models.get("models", [])]
+        except Exception:
+            # If API call fails, return empty list - user must specify model manually
+            return []
+
+    async def stream_chat(
+        self, messages: List[Message], model: str = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream chat completion from Ollama.
+
+        Args:
+            messages: List of Message objects
+            model: Model name to use
+
+        Yields:
+            str: Response content chunks
+        """
+        if self.client is None:
+            await self.initialize()
+
+        if model is None:
+            model = self.get_default_model()
+
+        # Convert messages to Ollama format
+        ollama_messages = self._convert_messages_to_ollama_format(messages)
+
+        # Default options with context size set to 4096
+        default_options = {"num_ctx": 4096}
+
+        try:
+            # Use AsyncClient for streaming
+            import ollama
+
+            async_client = ollama.AsyncClient(
+                host=self.config.get("host", "http://localhost:11434")
+            )
+
+            # Stream the response with default options
+            async for chunk in await async_client.chat(
+                model=model,
+                messages=ollama_messages,
+                stream=True,
+                options=default_options,
+            ):
+                if chunk.get("message", {}).get("content"):
+                    yield chunk["message"]["content"]
+
+        except Exception as e:
+            # Fallback to sync client if async fails
+            try:
+                stream = self.client.chat(
+                    model=model,
+                    messages=ollama_messages,
+                    stream=True,
+                    options=default_options,
+                )
+                for chunk in stream:
+                    if chunk.get("message", {}).get("content"):
+                        yield chunk["message"]["content"]
+            except Exception as fallback_error:
+                raise Exception(
+                    f"Ollama streaming error: {str(e)} (fallback: {str(fallback_error)})"
+                )
+
+    def get_default_model(self) -> str:
+        """Get the configured Ollama model."""
+        model = self.config.get("model")
+        if not model:
+            raise ValueError("OLLAMA_MODEL environment variable is required")
+        return model
+
+    def validate_config(self) -> List[str]:
+        """Validate Ollama configuration."""
+        errors = []
+
+        host = self.config.get("host", "http://localhost:11434")
+        if not host:
+            errors.append(
+                "OLLAMA_HOST must be a valid URL (defaults to http://localhost:11434)"
+            )
+
+        model = self.config.get("model")
+        if not model:
+            errors.append("OLLAMA_MODEL environment variable is required")
+        elif not isinstance(model, str):
+            errors.append("OLLAMA_MODEL must be a valid string")
+
+        return errors
+
+    async def health_check(self) -> bool:
+        """Check if Ollama is accessible and has models."""
+        try:
+            await self.initialize()
+            models = await self.get_available_models()
+            return len(models) > 0
+        except Exception:
+            return False
+
+    def _convert_messages_to_ollama_format(
+        self, messages: List[Message]
+    ) -> List[Dict[str, str]]:
+        """Convert standardized Message objects to Ollama format."""
+        ollama_messages = []
+
+        for message in messages:
+            # Ollama supports the same format as OpenAI
+            ollama_messages.append(
+                {
+                    "role": message.role,
+                    "content": message.content,
+                }
+            )
+
+        return ollama_messages
diff --git a/chat/llm_providers/openai_provider.py b/chat/llm_providers/openai_provider.py
new file mode 100644
index 0000000..2648863
--- /dev/null
+++ b/chat/llm_providers/openai_provider.py
@@ -0,0 +1,122 @@
+from typing import AsyncGenerator, List, Dict, Any
+import asyncio
+from openai import OpenAI
+from openai.types.chat import ChatCompletionMessageParam
+from .base import LLMProvider, Message
+
+
+class OpenAIProvider(LLMProvider):
+    """OpenAI LLM provider implementation."""
+
+    def __init__(self, config: Dict[str, Any]):
+        super().__init__(config)
+        self.client = None
+
+    async def initialize(self) -> None:
+        """Initialize the OpenAI client."""
+        if not self.config.get("api_key"):
+            raise ValueError("OpenAI API key is required")
+
+        self.client = OpenAI(api_key=self.config["api_key"])
+
+    async def get_available_models(self) -> List[str]:
+        """Get list of available OpenAI models."""
+        if self.client is None:
+            await self.initialize()
+
+        try:
+            # Fetch available models from OpenAI API
+            models = await asyncio.get_event_loop().run_in_executor(
+                None, lambda: self.client.models.list()
+            )
+
+            # Filter for chat completion models
+            chat_models = [
+                model.id
+                for model in models.data
+                if model.id.startswith(("gpt-", "chatgpt-"))
+                and "chat" in model.id.lower()
+            ]
+            return sorted(chat_models)
+        except Exception:
+            # If API call fails, return empty list - user must specify model manually
+            return []
+
+    async def stream_chat(
+        self, messages: List[Message], model: str = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream chat completion from OpenAI.
+
+        Args:
+            messages: List of Message objects
+            model: Model name to use
+
+        Yields:
+            str: Response content chunks
+        """
+        if self.client is None:
+            await self.initialize()
+
+        if model is None:
+            model = self.get_default_model()
+
+        # Convert messages to OpenAI format
+        openai_messages = self._convert_messages_to_openai_format(messages)
+
+        try:
+            # Create streaming chat completion
+            stream = self.client.chat.completions.create(
+                model=model,
+                messages=openai_messages,
+                stream=True,
+            )
+
+            # Stream the response
+            for chunk in stream:
+                if (
+                    hasattr(chunk.choices[0].delta, "content")
+                    and chunk.choices[0].delta.content is not None
+                ):
+                    yield chunk.choices[0].delta.content
+
+        except Exception as e:
+            raise Exception(f"OpenAI API error: {str(e)}")
+
+    def get_default_model(self) -> str:
+        """Get the configured OpenAI model."""
+        model = self.config.get("model")
+        if not model:
+            raise ValueError("OPENAI_MODEL environment variable is required")
+        return model
+
+    def validate_config(self) -> List[str]:
+        """Validate OpenAI configuration."""
+        errors = []
+
+        if not self.config.get("api_key"):
+            errors.append("OPENAI_API_KEY environment variable is required")
+
+        model = self.config.get("model")
+        if not model:
+            errors.append("OPENAI_MODEL environment variable is required")
+        elif not isinstance(model, str):
+            errors.append("OPENAI_MODEL must be a valid string")
+
+        return errors
+
+    def _convert_messages_to_openai_format(
+        self, messages: List[Message]
+    ) -> List[ChatCompletionMessageParam]:
+        """Convert standardized Message objects to OpenAI format."""
+        openai_messages = []
+
+        for message in messages:
+            openai_messages.append(
+                {
+                    "role": message.role,
+                    "content": message.content,
+                }
+            )
+
+        return openai_messages
diff --git a/chat/state.py b/chat/state.py
index a7bd678..4618ff1 100644
--- a/chat/state.py
+++ b/chat/state.py
@@ -1,12 +1,12 @@
 import os
 from typing import Any, TypedDict
 import reflex as rx
-from openai import OpenAI
-from openai.types.chat import ChatCompletionMessageParam
+from chat.llm_providers import LLMProviderFactory, Message
 
-# Checking if the API key is set properly
-if not os.getenv("OPENAI_API_KEY"):
-    raise Exception("Please set OPENAI_API_KEY environment variable.")
+# Load environment variables from .env file
+from dotenv import load_dotenv
+
+load_dotenv()
 
 
 class QA(TypedDict):
@@ -33,6 +33,28 @@ class State(rx.State):
     # Whether the new chat modal is open.
     is_modal_open: bool = False
 
+    # LLM Provider configuration
+    current_provider: str = os.getenv("LLM_PROVIDER", "openai")
+    current_model: str = ""
+    provider_error: str = ""
+    _provider_instance = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._initialize_provider()
+
+    def _initialize_provider(self):
+        """Initialize the LLM provider."""
+        try:
+            self._provider_instance = LLMProviderFactory.create_provider(
+                self.current_provider
+            )
+            self.current_model = self._provider_instance.get_default_model()
+            self.provider_error = ""
+        except Exception as e:
+            self.provider_error = str(e)
+            self._provider_instance = None
+
     @rx.event
     def create_chat(self, form_data: dict[str, Any]):
         """Create a new chat."""
@@ -111,60 +133,74 @@ async def process_question(self, form_data: dict[str, Any]):
         if not question:
             return
 
-        async for value in self.openai_process_question(question):
+        async for value in self.llm_process_question(question):
             yield value
 
     @rx.event
-    async def openai_process_question(self, question: str):
-        """Get the response from the API.
-
-        Args:
-            form_data: A dict with the current question.
-        """
+    async def llm_process_question(self, question: str):
+        """Process the question using the configured LLM provider."""
+        try:
+            # Check if provider is available
+            if self._provider_instance is None:
+                self.provider_error = "No LLM provider configured. Please check your environment variables."
+                # Add error message to chat
+                qa = QA(question=question, answer=f"Error: {self.provider_error}")
+                self._chats[self.current_chat].append(qa)
+                yield
+                return
 
-        # Add the question to the list of questions.
-        qa = QA(question=question, answer="")
-        self._chats[self.current_chat].append(qa)
+            # Add the question to the list of questions.
+            qa = QA(question=question, answer="")
+            self._chats[self.current_chat].append(qa)
 
-        # Clear the input and start the processing.
-        self.processing = True
-        yield
+            # Clear the input and start the processing.
+            self.processing = True
+            self.provider_error = ""
+            yield
 
-        # Build the messages.
-        messages: list[ChatCompletionMessageParam] = [
-            {
-                "role": "system",
-                "content": "You are a friendly chatbot named Reflex. Respond in markdown.",
-            }
-        ]
-        for qa in self._chats[self.current_chat]:
-            messages.append({"role": "user", "content": qa["question"]})
-            messages.append({"role": "assistant", "content": qa["answer"]})
+            # Build the messages for the provider.
+            messages = self._build_messages()
 
-        # Remove the last mock answer.
-        messages = messages[:-1]
+            # Initialize provider if needed
+            await self._provider_instance.initialize()
 
-        # Start a new session to answer the question.
-        session = OpenAI().chat.completions.create(
-            model=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
-            messages=messages,
-            stream=True,
-        )
+            # Stream the response from the provider
+            async for chunk in self._provider_instance.stream_chat(
+                messages=messages, model=self.current_model
+            ):
+                self._chats[self.current_chat][-1]["answer"] += chunk
+                self._chats = self._chats  # Trigger reactivity
+                yield
 
-        # Stream the results, yielding after every word.
-        for item in session:
-            if hasattr(item.choices[0].delta, "content"):
-                answer_text = item.choices[0].delta.content
-                # Ensure answer_text is not None before concatenation
-                if answer_text is not None:
-                    self._chats[self.current_chat][-1]["answer"] += answer_text
-                else:
-                    # Handle the case where answer_text is None, perhaps log it or assign a default value
-                    # For example, assigning an empty string if answer_text is None
-                    answer_text = ""
-                    self._chats[self.current_chat][-1]["answer"] += answer_text
+        except Exception as e:
+            self.provider_error = f"LLM processing error: {str(e)}"
+            # Add error message to the chat
+            if self._chats[self.current_chat]:
+                self._chats[self.current_chat][-1]["answer"] = f"Error: {str(e)}"
                 self._chats = self._chats
-                yield
+            yield
+
+        finally:
+            # Toggle the processing flag.
+            self.processing = False
+
+    def _build_messages(self) -> list[Message]:
+        """Build the message list from chat history."""
+        messages = [
+            Message(
+                role="system",
+                content="You are a friendly chatbot named Reflex. Respond in markdown.",
+            )
+        ]
+
+        # Add conversation history
+        for qa in self._chats[self.current_chat]:
+            messages.append(Message(role="user", content=qa["question"]))
+            if qa["answer"]:  # Only add assistant messages that have content
+                messages.append(Message(role="assistant", content=qa["answer"]))
+
+        # Remove the last empty assistant message (the one we're currently filling)
+        if messages and messages[-1].role == "assistant" and not messages[-1].content:
+            messages = messages[:-1]
 
-        # Toggle the processing flag.
-        self.processing = False
+        return messages
diff --git a/requirements.txt b/requirements.txt
index d228dd6..e49589f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,5 @@
 reflex>=0.7.11
 openai>=1.78.1
+ollama>=0.1.0
+google-genai>=1.0.0
+python-dotenv>=1.0.0

From aad96e06e57a33103f49e7744586d4397a537ddc Mon Sep 17 00:00:00 2001
From: Anas Khan <83116240+anxkhn@users.noreply.github.com>
Date: Tue, 25 Nov 2025 12:10:10 +0530
Subject: [PATCH 2/5] refactor: response handling in GoogleGenAIProvider

---
 chat/llm_providers/google_provider.py | 39 +++++++++------------------
 1 file changed, 12 insertions(+), 27 deletions(-)

diff --git a/chat/llm_providers/google_provider.py b/chat/llm_providers/google_provider.py
index e73c56b..4e260e7 100644
--- a/chat/llm_providers/google_provider.py
+++ b/chat/llm_providers/google_provider.py
@@ -38,19 +38,6 @@ async def initialize(self) -> None:
             # Use Developer API
             self.client = genai.Client(api_key=self.config["api_key"])
 
-    async def get_available_models(self) -> List[str]:
-        """Get list of available Google Gemini models."""
-        if self.client is None:
-            await self.initialize()
-
-        try:
-            # Try to get models from the API if available
-            # Note: Google GenAI may not have a public list models API
-            # For now, return empty list - user must specify model manually
-            return []
-        except Exception:
-            return []
-
     async def stream_chat(
         self, messages: List[Message], model: str = None
     ) -> AsyncGenerator[str, None]:
@@ -76,30 +63,28 @@ async def stream_chat(
         try:
             # Use async client for streaming
             async with self.client.aio as aclient:
-                response = await aclient.models.generate_content(
+                response_stream = await aclient.models.generate_content_stream(
                     model=model,
                     contents=gemini_messages,
                 )
 
-                # Stream the response
-                if hasattr(response, "text"):
-                    # If response is complete, yield all at once
-                    yield response.text
-                else:
-                    # Try to stream if available
-                    async for chunk in response:
-                        if hasattr(chunk, "text") and chunk.text:
-                            yield chunk.text
+                # Stream the response chunks
+                async for chunk in response_stream:
+                    if hasattr(chunk, "text") and chunk.text:
+                        yield chunk.text
 
         except Exception as e:
-            # Fallback to sync client
+            # Fallback to sync streaming client
             try:
-                response = self.client.models.generate_content(
+                response_stream = self.client.models.generate_content_stream(
                     model=model,
                     contents=gemini_messages,
                 )
-                if hasattr(response, "text"):
-                    yield response.text
+
+                # Stream the response chunks
+                for chunk in response_stream:
+                    if hasattr(chunk, "text") and chunk.text:
+                        yield chunk.text
             except Exception as fallback_error:
                 raise Exception(
                     f"Google GenAI streaming error: {str(e)} (fallback: {str(fallback_error)})"

From 1cd933c7f8db34d991289c2767a1bcdaa0951ab5 Mon Sep 17 00:00:00 2001
From: Anas Khan <83116240+anxkhn@users.noreply.github.com>
Date: Tue, 25 Nov 2025 12:10:27 +0530
Subject: [PATCH 3/5] chore: initialize project configuration with
 pyproject.toml and update requirements.txt and uv.lock

---
 pyproject.toml   |   13 +
 requirements.txt |  165 +++++-
 uv.lock          | 1307 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 1480 insertions(+), 5 deletions(-)
 create mode 100644 pyproject.toml
 create mode 100644 uv.lock

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..fb22afb
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+[project]
+name = "reflex-chat"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "google-genai>=1.0.0",
+    "ollama>=0.1.0",
+    "openai>=1.78.1",
+    "python-dotenv>=1.0.0",
+    "reflex>=0.7.11",
+]
diff --git a/requirements.txt b/requirements.txt
index e49589f..0536adc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,160 @@
-reflex>=0.7.11
-openai>=1.78.1
-ollama>=0.1.0
-google-genai>=1.0.0
-python-dotenv>=1.0.0
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml --output-file=requirements.txt
+alembic==1.17.2
+    # via reflex
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.11.0
+    # via
+    #   google-genai
+    #   httpx
+    #   openai
+    #   starlette
+    #   watchfiles
+async-timeout==5.0.1
+    # via redis
+bidict==0.23.1
+    # via python-socketio
+cachetools==6.2.2
+    # via google-auth
+certifi==2025.11.12
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.4
+    # via requests
+click==8.3.1
+    # via
+    #   granian
+    #   reflex
+    #   reflex-hosting-cli
+distro==1.9.0
+    # via openai
+google-auth==2.43.0
+    # via google-genai
+google-genai==1.52.0
+    # via reflex-chat (pyproject.toml)
+granian==2.6.0
+    # via reflex
+h11==0.16.0
+    # via
+    #   httpcore
+    #   wsproto
+httpcore==1.0.9
+    # via httpx
+httpx==0.28.1
+    # via
+    #   google-genai
+    #   ollama
+    #   openai
+    #   reflex
+    #   reflex-hosting-cli
+idna==3.11
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+jiter==0.12.0
+    # via openai
+mako==1.3.10
+    # via alembic
+markdown-it-py==4.0.0
+    # via rich
+markupsafe==3.0.3
+    # via mako
+mdurl==0.1.2
+    # via markdown-it-py
+ollama==0.6.1
+    # via reflex-chat (pyproject.toml)
+openai==2.8.1
+    # via reflex-chat (pyproject.toml)
+packaging==25.0
+    # via
+    #   reflex
+    #   reflex-hosting-cli
+platformdirs==4.5.0
+    # via
+    #   reflex
+    #   reflex-hosting-cli
+pyasn1==0.6.1
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.4.2
+    # via google-auth
+pydantic==2.12.4
+    # via
+    #   google-genai
+    #   ollama
+    #   openai
+    #   reflex
+    #   sqlmodel
+pydantic-core==2.41.5
+    # via pydantic
+pygments==2.19.2
+    # via rich
+python-dotenv==1.2.1
+    # via reflex-chat (pyproject.toml)
+python-engineio==4.12.3
+    # via python-socketio
+python-multipart==0.0.20
+    # via reflex
+python-socketio==5.15.0
+    # via reflex
+redis==7.1.0
+    # via reflex
+reflex==0.8.21
+    # via reflex-chat (pyproject.toml)
+reflex-hosting-cli==0.1.59
+    # via reflex
+requests==2.32.5
+    # via google-genai
+rich==14.2.0
+    # via
+    #   reflex
+    #   reflex-hosting-cli
+rsa==4.9.1
+    # via google-auth
+simple-websocket==1.1.0
+    # via python-engineio
+sniffio==1.3.1
+    # via
+    #   anyio
+    #   openai
+sqlalchemy==2.0.44
+    # via
+    #   alembic
+    #   sqlmodel
+sqlmodel==0.0.27
+    # via reflex
+starlette==0.50.0
+    # via reflex
+tenacity==9.1.2
+    # via google-genai
+tqdm==4.67.1
+    # via openai
+typing-extensions==4.15.0
+    # via
+    #   alembic
+    #   anyio
+    #   google-genai
+    #   openai
+    #   pydantic
+    #   pydantic-core
+    #   reflex
+    #   sqlalchemy
+    #   starlette
+    #   typing-inspection
+typing-inspection==0.4.2
+    # via pydantic
+urllib3==2.5.0
+    # via requests
+watchfiles==1.1.1
+    # via granian
+websockets==15.0.1
+    # via google-genai
+wrapt==2.0.1
+    # via reflex
+wsproto==1.3.2
+    # via simple-websocket
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..97f22b8
--- /dev/null
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+    { url = "https://files.pythonhosted.org/packages/41/99/8a06b8e17dddbf321325ae4eb12465804120f699cd1b8a355718300c62da/wrapt-2.0.1-cp314-cp314t-win_arm64.whl", hash = "sha256:35cdbd478607036fee40273be8ed54a451f5f23121bd9d4be515158f9498f7ad", size = 60634, upload-time = "2025-11-07T00:45:02.087Z" },
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+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "h11" },
+]
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a4/f5/10b68b7b1544245097b2a1b8238f66f2fc6dcaeb24ba5d917f52bd2eed4f/wsproto-1.3.2-py3-none-any.whl", hash = "sha256:61eea322cdf56e8cc904bd3ad7573359a242ba65688716b0710a5eb12beab584", size = 24405, upload-time = "2025-11-20T18:18:00.454Z" },
+]

From 44a2b630f4d2e160745f3a3ec5c498952850499b Mon Sep 17 00:00:00 2001
From: Anas Khan <83116240+anxkhn@users.noreply.github.com>
Date: Tue, 25 Nov 2025 12:10:45 +0530
Subject: [PATCH 4/5] refactor: remove unused methods and health check from
 LLMProvider and its implementations

---
 chat/llm_providers/base.py            | 14 --------------
 chat/llm_providers/factory.py         | 19 -------------------
 chat/llm_providers/ollama_provider.py | 25 -------------------------
 chat/llm_providers/openai_provider.py | 24 ------------------------
 4 files changed, 82 deletions(-)

diff --git a/chat/llm_providers/base.py b/chat/llm_providers/base.py
index 27c7e3a..d5f2f69 100644
--- a/chat/llm_providers/base.py
+++ b/chat/llm_providers/base.py
@@ -25,11 +25,6 @@ async def initialize(self) -> None:
         """Initialize the provider (setup clients, validate configuration)."""
         pass
 
-    @abstractmethod
-    async def get_available_models(self) -> List[str]:
-        """Get list of available models for this provider."""
-        pass
-
     @abstractmethod
     async def stream_chat(
         self, messages: List[Message], model: str = None
@@ -66,12 +61,3 @@ def convert_to_provider_format(
     ) -> List[Dict[str, str]]:
         """Convert standardized messages to provider-specific format."""
         return [msg.to_dict() for msg in messages]
-
-    async def health_check(self) -> bool:
-        """Check if the provider is accessible and properly configured."""
-        try:
-            await self.initialize()
-            models = await self.get_available_models()
-            return len(models) > 0
-        except Exception:
-            return False
diff --git a/chat/llm_providers/factory.py b/chat/llm_providers/factory.py
index 60e8eb4..09690f1 100644
--- a/chat/llm_providers/factory.py
+++ b/chat/llm_providers/factory.py
@@ -84,22 +84,3 @@ def _get_provider_config(cls, provider_name: str) -> Dict[str, Any]:
     def get_supported_providers(cls) -> List[str]:
         """Get list of supported provider names."""
         return list(cls.SUPPORTED_PROVIDERS.keys())
-
-    @classmethod
-    async def get_available_providers(cls) -> Dict[str, bool]:
-        """
-        Check which providers are available and properly configured.
-
-        Returns:
-            Dict[str, bool]: Mapping of provider names to availability status
-        """
-        availability = {}
-
-        for provider_name in cls.SUPPORTED_PROVIDERS.keys():
-            try:
-                provider = cls.create_provider(provider_name)
-                availability[provider_name] = await provider.health_check()
-            except Exception:
-                availability[provider_name] = False
-
-        return availability
diff --git a/chat/llm_providers/ollama_provider.py b/chat/llm_providers/ollama_provider.py
index c0168bb..402a9f2 100644
--- a/chat/llm_providers/ollama_provider.py
+++ b/chat/llm_providers/ollama_provider.py
@@ -1,5 +1,4 @@
 from typing import AsyncGenerator, List, Dict, Any
-import asyncio
 from .base import LLMProvider, Message
 
 
@@ -32,21 +31,6 @@ async def initialize(self) -> None:
                 f"Failed to connect to Ollama at {self.config.get('host')}: {str(e)}"
             )
 
-    async def get_available_models(self) -> List[str]:
-        """Get list of available local Ollama models."""
-        if self.client is None:
-            await self.initialize()
-
-        try:
-            # Run in thread pool to avoid blocking
-            models = await asyncio.get_event_loop().run_in_executor(
-                None, self.client.list
-            )
-            return [model["name"] for model in models.get("models", [])]
-        except Exception:
-            # If API call fails, return empty list - user must specify model manually
-            return []
-
     async def stream_chat(
         self, messages: List[Message], model: str = None
     ) -> AsyncGenerator[str, None]:
@@ -132,15 +116,6 @@ def validate_config(self) -> List[str]:
 
         return errors
 
-    async def health_check(self) -> bool:
-        """Check if Ollama is accessible and has models."""
-        try:
-            await self.initialize()
-            models = await self.get_available_models()
-            return len(models) > 0
-        except Exception:
-            return False
-
     def _convert_messages_to_ollama_format(
         self, messages: List[Message]
     ) -> List[Dict[str, str]]:
diff --git a/chat/llm_providers/openai_provider.py b/chat/llm_providers/openai_provider.py
index 2648863..69af727 100644
--- a/chat/llm_providers/openai_provider.py
+++ b/chat/llm_providers/openai_provider.py
@@ -1,5 +1,4 @@
 from typing import AsyncGenerator, List, Dict, Any
-import asyncio
 from openai import OpenAI
 from openai.types.chat import ChatCompletionMessageParam
 from .base import LLMProvider, Message
@@ -19,29 +18,6 @@ async def initialize(self) -> None:
 
         self.client = OpenAI(api_key=self.config["api_key"])
 
-    async def get_available_models(self) -> List[str]:
-        """Get list of available OpenAI models."""
-        if self.client is None:
-            await self.initialize()
-
-        try:
-            # Fetch available models from OpenAI API
-            models = await asyncio.get_event_loop().run_in_executor(
-                None, lambda: self.client.models.list()
-            )
-
-            # Filter for chat completion models
-            chat_models = [
-                model.id
-                for model in models.data
-                if model.id.startswith(("gpt-", "chatgpt-"))
-                and "chat" in model.id.lower()
-            ]
-            return sorted(chat_models)
-        except Exception:
-            # If API call fails, return empty list - user must specify model manually
-            return []
-
     async def stream_chat(
         self, messages: List[Message], model: str = None
     ) -> AsyncGenerator[str, None]:

From baa7c0b32e1d4b7e4a31903477449cf7a7fc10a8 Mon Sep 17 00:00:00 2001
From: Anas Khan <83116240+anxkhn@users.noreply.github.com>
Date: Tue, 25 Nov 2025 12:11:05 +0530
Subject: [PATCH 5/5] docs: update README to reflect new features and
 multi-provider support in the Reflex Chat App

---
 README.md | 100 ++++++++++++++++++++++++++++++++++--------------------
 1 file changed, 63 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index ebab33c..2a08762 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,26 @@
 # Reflex Chat App
 
-A user-friendly, highly customizable Python web app designed to demonstrate LLMs in a ChatGPT format with support for multiple LLM providers.
+**This is a fork of the original [Reflex Chat](https://github.com/reflex-dev/reflex-chat) with enhanced multi-provider support.**
+
+A user-friendly, highly customizable Python web app designed to demonstrate LLMs in a ChatGPT format with support for multiple LLM providers. This fork serves as an excellent example for developers who want to learn how to implement and integrate different LLM providers in their applications.
+
+## What's New in This Fork
+
+- **Added Ollama support** - Run local models with real-time streaming
+- **Added Google Gemini support** - Including Vertex AI integration with streaming
+- **Enhanced provider architecture** - Clean, extensible factory pattern for adding new providers
+- **Unified streaming** - All providers now support real-time response streaming
+- **uv support** - Lightning-fast dependency management
+
+## Learning Resource
+
+This fork is designed to help developers understand:
+
+- How to implement multiple LLM provider integrations
+- Async streaming patterns for real-time chat
+- Clean architecture patterns for provider management
+- Environment-based configuration for different services
+- Error handling and fallback mechanisms
 
 <div align="center">
 <img src="./docs/demo.gif" alt="icon"/>
@@ -14,12 +34,18 @@ The app supports three LLM providers. Configure the one you want to use:
 
 ### Option 1: OpenAI
 
-You'll need a valid OpenAI subscription. Set your API key and model:
+You'll need a valid OpenAI subscription. Copy `.env.example` to `.env` and configure:
 
 ```bash
-export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
-export LLM_PROVIDER="openai"
-export OPENAI_MODEL="gpt-5-mini"
+cp .env.example .env
+```
+
+Then edit `.env` with your OpenAI credentials:
+
+```env
+LLM_PROVIDER=openai
+OPENAI_API_KEY=your_openai_api_key_here
+OPENAI_MODEL=gpt-4o  # or gpt-4o-mini, gpt-5-mini, etc.
 ```
 
 ### Option 2: Ollama (Local Models)
@@ -29,31 +55,33 @@ Run models locally using Ollama:
 1. [Install Ollama](https://ollama.ai/download)
 2. Start Ollama server: `ollama serve`
 3. Pull a model: `ollama pull gemma3:4b`
-4. Configure environment:
+4. Configure your `.env`:
 
-```bash
-export LLM_PROVIDER="ollama"
-export OLLAMA_HOST="http://localhost:11434"  # optional, default
-export OLLAMA_MODEL="gemma3:4b"  # required: must be a model you have installed
+```env
+LLM_PROVIDER=ollama
+OLLAMA_HOST=http://localhost:11434  # optional, defaults to this
+OLLAMA_MODEL=gemma3:4b  # required: must be a model you have installed
 ```
 
 ### Option 3: Google Gemini
 
-Use Google's Gemini models:
+Use Google's Gemini models. Configure your `.env`:
 
-```bash
-export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
-export LLM_PROVIDER="gemini"
-export GEMINI_MODEL="gemini-2.0-flash"
+```env
+LLM_PROVIDER=gemini
+GEMINI_API_KEY=your_gemini_api_key_here
+GEMINI_MODEL=gemini-2.0-flash  # or gemini-1.5-flash, gemini-2.0-pro, etc.
 ```
 
-For Vertex AI:
+For Vertex AI (Enterprise Google Cloud):
 
-```bash
-export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
-export GOOGLE_USE_VERTEXAI="true"
-export GOOGLE_PROJECT_ID="your-gcp-project-id"
-export GOOGLE_LOCATION="us-central1"  # optional
+```env
+LLM_PROVIDER=gemini
+GEMINI_API_KEY=your_gemini_api_key_here
+GOOGLE_USE_VERTEXAI=true
+GOOGLE_PROJECT_ID=your-gcp-project-id
+GOOGLE_LOCATION=us-central1  # optional
+GEMINI_MODEL=gemini-2.0-flash
 ```
 
 ### 🧬 1. Clone the Repo
@@ -65,10 +93,16 @@ cd reflex-chat
 
 ### 📦 2. Install Dependencies
 
-To get started with Reflex, you'll need:
+To get started with Reflex, you'll need Python 3.10+. You can install dependencies using either **pip** (traditional) or **uv** (recommended for faster installs).
+
+#### Using uv 
 
-- Python 3.10+
-- pip dependencies: `reflex`, `openai`, `ollama`, `google-genai`
+2. **Install and sync dependencies**:
+   ```bash
+   uv sync
+   ```
+
+#### Using pip
 
 Install all dependencies with the provided `requirements.txt`:
 
@@ -109,20 +143,12 @@ reflex run
 
 # Features
 
-- 100% Python-based, including the UI, using Reflex
-- Create and delete chat sessions
-- The application is fully customizable and no knowledge of web dev is required to use it.
+- **100% Python-based**, including the UI, using Reflex
+- **Create and delete chat sessions**
+- **Real-time streaming** responses for all LLM providers (OpenAI, Ollama, Gemini)
+- **Multiple LLM support**: OpenAI, Ollama (local), Google Gemini with Vertex AI
+- **Fully customizable** - no web dev knowledge required
   - See https://reflex.dev/docs/styling/overview for more details
-- Easily swap out any LLM
-- Responsive design for various devices
-
-# Contributing
-
-We welcome contributions to improve and extend the LLM Web UI.
-If you'd like to contribute, please do the following:
-
-- Fork the repository and make your changes.
-- Once you're ready, submit a pull request for review.
 
 # License