Sentiment-Textgen/app.py at main · Subhajitdas99/Sentiment-Textgen · GitHub

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# app.py
import streamlit as st
from sentiment import detect_sentiment, get_sentiment_pipeline
from generator import generate_text
from transformers import pipeline

# ---------------------------
# Streamlit Page Setup
# ---------------------------
st.set_page_config(
    page_title="Sentiment-Aligned Text Generator",
    page_icon="🧠",
    layout="wide",
)

st.title("🧠 AI Text Generator — Sentiment-Aligned")
st.markdown(
    "This tool analyzes the **sentiment** of your input and generates text "
    "that matches the detected emotion using a GPT model."
)

st.divider()

# ---------------------------
# Cache Models
# ---------------------------
@st.cache_resource
def load_sentiment():
    return get_sentiment_pipeline()

@st.cache_resource
def load_model(model_name="gpt2"):
    return pipeline("text-generation", model=model_name)

# ---------------------------
# Sidebar Controls
# ---------------------------
with st.sidebar:
    st.header("⚙️ Settings")
    model_choice = st.selectbox("Model", ["gpt2", "distilgpt2"], index=0)
    max_words = st.slider("Max words (approx.)", 30, 400, 120, step=10)
    seed_value = st.number_input("Seed (for reproducibility)", min_value=0, value=42)
    manual_override = st.checkbox("Manually select sentiment")
    manual_sentiment = None
    if manual_override:
        manual_sentiment = st.radio("Select sentiment", ["positive", "neutral", "negative"])

# ---------------------------
# User Input
# ---------------------------
st_prompt = st.text_area(
    "💬 Enter your prompt",
    height=150,
    placeholder="e.g. A small town by the sea, childhood memory, or the value of teamwork...",
)

# ---------------------------
# Main Action
# ---------------------------
if st.button("🚀 Generate Text"):
    if not st_prompt.strip():
        st.warning("Please enter a prompt before generating.")
    else:
        try:
            # Sentiment Detection
            with st.spinner("🔍 Detecting sentiment..."):
                nlp = load_sentiment()
                detection = detect_sentiment(st_prompt, nlp=nlp)
                detected = manual_sentiment if manual_override else detection["sentiment"]

            st.info(
                f"**Detected Sentiment:** `{detection['label']}` "
                f"(score: {detection['score']:.2f}) → **Using:** `{detected}`"
            )

            # Text Generation
            with st.spinner("✍️ Generating text (this may take a few seconds)..."):
                text = generate_text(
                    st_prompt,
                    detected,
                    max_new_tokens=max_words,
                    model_name=model_choice,
                    seed=seed_value,
                )

            # Output
            st.success("✅ Generation Complete!")
            st.subheader("🧾 Generated Text")
            st.write(text)

            st.download_button(
                "📥 Download Result (.txt)",
                data=text,
                file_name="generated_text.txt",
            )

        except Exception as e:
            st.error(f"⚠️ Something went wrong: {e}")

# ---------------------------
# Footer
# ---------------------------
st.divider()
st.caption("Built with ❤️ using Streamlit and Hugging Face Transformers.")