main.py

import os
import json
import base64
import google.generativeai as genai

from fastapi import FastAPI, HTTPException, File, UploadFile

app = FastAPI(title="Business Card Analyzer")
GOOGLE_API_KEY = 'AIzaSyDcYyq3w21iwipYn17wCAQo3AYWhUIGDSI'
if not GOOGLE_API_KEY:
    raise Exception("Please set the GOOGLE_API_KEY environment variable.")

genai.configure(api_key=GOOGLE_API_KEY)

# Define the prompt to instruct the model.
PROMPT = """
Analyze the business card and extract the following information in JSON format:
{
  "name": "Full Name",
  "mobile_number": "Mobile Number",
  "company_name": "Company Name",
  "email": "Email Address",
  "job_title": "Job Title",
  "address": "Address",
  "website": "Website URL"
}

Ensure that the response is strictly in JSON format and includes all the fields mentioned above. If any information is missing or unclear, set the corresponding field to null.
"""

@app.post("/analyze", summary="Analyze a business card image file")
async def analyze_business_card(file: UploadFile = File(...)):
    # Validate the file type. Only JPEG and PNG are supported.
    if file.content_type not in ["image/jpeg", "image/png"]:
        raise HTTPException(
            status_code=400,
            detail="Invalid file type. Only JPEG and PNG images are supported."
        )
    
    # Read the uploaded file
    try:
        file_content = await file.read()
    except Exception as e:
        raise HTTPException(
            status_code=400,
            detail=f"Failed to read the uploaded file: {e}"
        )
    
    # Encode the image in base64
    encoded_image = base64.b64encode(file_content).decode("utf-8")

    # Create an instance of the Gemini model
    model = genai.GenerativeModel(model_name="gemini-1.5-pro")

    # Call the model and process its response
    try:
        result = model.generate_content([
            {
                "mime_type": file.content_type,
                "data": encoded_image,
            },
            PROMPT,
        ])
        # Get the raw text response
        raw = result.text.strip()

        # Remove markdown formatting if present (e.g., ```json ... ```)
        if raw.startswith("```"):
            lines = raw.splitlines()
            if lines[0].startswith("```"):
                lines = lines[1:]
            if lines and lines[-1].startswith("```"):
                lines = lines[:-1]
            raw = "\n".join(lines).strip()

        data = json.loads(raw)
    except json.JSONDecodeError as jde:
        raise HTTPException(
            status_code=500,
            detail=f"JSON decoding error: {jde}. Response text was: {raw}"
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"An error occurred while processing the image: {e}"
        )

    return data

if __name__ == "__main__":
    import uvicorn

    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)