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Added a New Python File for Working with the Gemini API #1

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d7de484
Gemini version Added
pythonicshariful Nov 11, 2025
f839e8e
gemini code added
pythonicshariful Nov 11, 2025
479123a
Delete venv directory
pythonicshariful Nov 11, 2025
e521083
Delete __pycache__ directory
pythonicshariful Nov 11, 2025
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2 changes: 1 addition & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ build/
.venv/
.env.local
.env.*.local

venv
# IDE and editor files
.idea/
.vscode/
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Binary file removed customer_info.xlsx
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102 changes: 102 additions & 0 deletions extractGemini.py
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@@ -0,0 +1,102 @@
import google.generativeai as genai
import base64
import json
from PIL import Image
import pandas as pd
import os
from io import BytesIO
from config import GEMINI_API_KEY # <-- store your Gemini API key in config.py

# Configure Gemini
genai.configure(api_key=GEMINI_API_KEY)

def extract_customer_info(image_path):
try:
# Load and encode the image
with Image.open(image_path) as img:
if img.mode != "RGB":
img = img.convert("RGB")

buffered = BytesIO()
img.save(buffered, format="JPEG")
img_bytes = buffered.getvalue()

# Define model
model = genai.GenerativeModel("gemini-2.5-flash") # or "gemini-1.5-pro"

# Define prompt
prompt = (
"Extract customer information from this image and return ONLY a valid JSON object "
"with this exact structure:\n"
"{\n"
" \"Name\": \"\",\n"
" \"Phone Number\": \"\",\n"
" \"Mobile Number\": \"\",\n"
" \"Email\": \"\",\n"
" \"Street\": \"\",\n"
" \"Street Number\": \"\",\n"
" \"City\": \"\",\n"
" \"ZIP Code\": \"\",\n"
" \"State\": \"\",\n"
" \"Country\": \"\",\n"
" \"Latitude\": \"\",\n"
" \"Longitude\": \"\"\n"
"}"
)

# Send image + prompt to Gemini
response = model.generate_content(
[
prompt,
{"mime_type": "image/jpeg", "data": img_bytes},
]
)

# Extract text
customer_info_text = response.text.strip()
print(f"Extracted Customer Information from {image_path}:\n", customer_info_text)

# Try to extract valid JSON from the response
if customer_info_text.startswith("{") and customer_info_text.endswith("}"):
json_str = customer_info_text
else:
start = customer_info_text.find("{")
end = customer_info_text.rfind("}") + 1
if start != -1 and end != 0:
json_str = customer_info_text[start:end]
else:
raise json.JSONDecodeError("No valid JSON found", customer_info_text, 0)

customer_info = json.loads(json_str)
return customer_info

except json.JSONDecodeError as e:
print(f"Could not convert to JSON. Error: {str(e)}")
print("Raw response:", customer_info_text)
return None
except Exception as e:
print(f"An error occurred while processing {image_path}: {str(e)}")
return None


def process_image_folder(folder_path):
image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
all_customer_info = []

for image_file in image_files:
image_path = os.path.join(folder_path, image_file)
customer_info = extract_customer_info(image_path)
if customer_info:
all_customer_info.append(customer_info)

try:
df = pd.DataFrame(all_customer_info)
df.to_excel("customer_info.xlsx", index=False)
print("\n✅ Excel file 'customer_info.xlsx' has been created successfully!")
except Exception as e:
print(f"An error occurred while creating the Excel file: {str(e)}")


# === Run ===
folder_path = "images" # Change to your folder path
process_image_folder(folder_path)
244 changes: 122 additions & 122 deletions extract.py → extractOpenAi.py
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@@ -1,123 +1,123 @@
import openai
import base64
import json
from PIL import Image
from io import BytesIO
import pandas as pd
import os
from config import OPENAI_API_KEY
# Set your OpenAI API key
openai.api_key = OPENAI_API_KEY
def extract_customer_info(image_path):
# Step 1: Load and encode the image
with Image.open(image_path) as img:
# Convert image to RGB if it's not
if img.mode != "RGB":
img = img.convert("RGB")
# Convert image to base64
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode()
# Step 2: Create the message with the image
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Extract customer information from this image and return ONLY a valid JSON object with this exact structure:\n"
"{\n"
" \"Name\": \"\",\n"
" \"Phone Number\": \"\",\n"
" \"Mobile Number\": \"\",\n"
" \"Email\": \"\",\n"
" \"Street\": \"\",\n"
" \"Street Number\": \"\",\n"
" \"City\": \"\",\n"
" \"ZIP Code\": \"\",\n"
" \"State\": \"\",\n"
" \"Country\": \"\",\n"
" \"Latitude\": \"\",\n"
" \"Longitude\": \"\"\n"
"}"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{img_str}"
# "detail": "low"
},
}
]
}
]
# Step 3: Send the image to OpenAI's Vision model
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=messages,
max_tokens=1000
)
# Step 4: Extract and process the response
customer_info_text = response['choices'][0]['message']['content'].strip()
print(f"Extracted Customer Information from {image_path}:\n", customer_info_text)
try:
# Try to clean the response if it contains additional text
if customer_info_text.startswith("{") and customer_info_text.endswith("}"):
json_str = customer_info_text
else:
# Try to find JSON object between curly braces
start = customer_info_text.find("{")
end = customer_info_text.rfind("}") + 1
if start != -1 and end != 0:
json_str = customer_info_text[start:end]
else:
raise json.JSONDecodeError("No valid JSON found", customer_info_text, 0)
# Convert text to JSON
customer_info = json.loads(json_str)
return customer_info
except json.JSONDecodeError as e:
print(f"Could not convert to JSON. Error: {str(e)}")
print("Raw response:", customer_info_text)
return None
except Exception as e:
print(f"An error occurred: {str(e)}")
return None
def process_image_folder(folder_path):
# Get all image files from the folder
image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
all_customer_info = []
# Process each image
for image_file in image_files:
image_path = os.path.join(folder_path, image_file)
customer_info = extract_customer_info(image_path)
if customer_info:
all_customer_info.append(customer_info)
try:
# Create DataFrame with all information
df = pd.DataFrame(all_customer_info)
# Save to Excel
df.to_excel('customer_info.xlsx', index=False)
print("\nExcel file 'customer_info.xlsx' has been created successfully!")
except Exception as e:
print(f"An error occurred while creating the Excel file: {str(e)}")
# Path to your image folder
folder_path = "images" # Change this to your folder path
import openai
import base64
import json
from PIL import Image
from io import BytesIO
import pandas as pd
import os
from config import OPENAI_API_KEY

# Set your OpenAI API key
openai.api_key = OPENAI_API_KEY

def extract_customer_info(image_path):
# Step 1: Load and encode the image
with Image.open(image_path) as img:
# Convert image to RGB if it's not
if img.mode != "RGB":
img = img.convert("RGB")

# Convert image to base64
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode()

# Step 2: Create the message with the image
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Extract customer information from this image and return ONLY a valid JSON object with this exact structure:\n"
"{\n"
" \"Name\": \"\",\n"
" \"Phone Number\": \"\",\n"
" \"Mobile Number\": \"\",\n"
" \"Email\": \"\",\n"
" \"Street\": \"\",\n"
" \"Street Number\": \"\",\n"
" \"City\": \"\",\n"
" \"ZIP Code\": \"\",\n"
" \"State\": \"\",\n"
" \"Country\": \"\",\n"
" \"Latitude\": \"\",\n"
" \"Longitude\": \"\"\n"
"}"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{img_str}"
# "detail": "low"
},

}
]
}
]

# Step 3: Send the image to OpenAI's Vision model
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=messages,
max_tokens=1000
)

# Step 4: Extract and process the response
customer_info_text = response['choices'][0]['message']['content'].strip()
print(f"Extracted Customer Information from {image_path}:\n", customer_info_text)

try:
# Try to clean the response if it contains additional text
if customer_info_text.startswith("{") and customer_info_text.endswith("}"):
json_str = customer_info_text
else:
# Try to find JSON object between curly braces
start = customer_info_text.find("{")
end = customer_info_text.rfind("}") + 1
if start != -1 and end != 0:
json_str = customer_info_text[start:end]
else:
raise json.JSONDecodeError("No valid JSON found", customer_info_text, 0)

# Convert text to JSON
customer_info = json.loads(json_str)
return customer_info

except json.JSONDecodeError as e:
print(f"Could not convert to JSON. Error: {str(e)}")
print("Raw response:", customer_info_text)
return None
except Exception as e:
print(f"An error occurred: {str(e)}")
return None

def process_image_folder(folder_path):
# Get all image files from the folder
image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]

all_customer_info = []

# Process each image
for image_file in image_files:
image_path = os.path.join(folder_path, image_file)
customer_info = extract_customer_info(image_path)

if customer_info:
all_customer_info.append(customer_info)

try:
# Create DataFrame with all information
df = pd.DataFrame(all_customer_info)

# Save to Excel
df.to_excel('customer_info.xlsx', index=False)
print("\nExcel file 'customer_info.xlsx' has been created successfully!")

except Exception as e:
print(f"An error occurred while creating the Excel file: {str(e)}")

# Path to your image folder
folder_path = "images" # Change this to your folder path
process_image_folder(folder_path)