try: from pip._internal.operations import freeze
except ImportError: # pip < 10.0
    from pip.operations import freeze

pkgs = freeze.freeze()
for pkg in pkgs: print(pkg)
import os 
from fastapi import FastAPI, HTTPException, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from PyPDF2 import PdfReader
import google.generativeai as genai
import json
import base64
from io import BytesIO
from PIL import Image
import io
import requests

from dotenv import load_dotenv
# Load the environment variables from the .env file
load_dotenv()

secret = os.environ["GEMINI"]
genai.configure(api_key=secret)
model_vision = genai.GenerativeModel('gemini-pro-vision')
model_text = genai.GenerativeModel('gemini-pro')

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)



def encode_image(image):
    # Convert image to BytesIO object (in memory)
    buffered = BytesIO()
    image.save(buffered, format=image.format)  # Use the original image format (e.g., PNG, JPEG)
    img_bytes = buffered.getvalue()

    # Encode image to base64
    base64_image = base64.b64encode(img_bytes).decode('utf-8')
    return base64_image



def vision(image):
    # OpenAI API Key
    api_key = os.environ["OPEN_AI"]
    

    # Getting the base64 string
    base64_image = encode_image(image)
    
    headers = {
      "Content-Type": "application/json",
      "Authorization": f"Bearer {api_key}"
    }
    
    payload = {
      "model": "gpt-4o-mini",
      "messages": [
        {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "extract all data from this image"
            },
            {
              "type": "image_url",
              "image_url": {
                "url": f"data:image/jpeg;base64,{base64_image}"
              }
            }
          ]
        }
      ],
      "max_tokens": 300
    }
    
    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
    
    return response.json()['choices'][0]['message']['content']


@app.post("/get_ocr_data/")
async def get_data(input_file: UploadFile = File(...)):
    try:
        # Determine the file type by reading the first few bytes
        file_content = await input_file.read()
        file_type = input_file.content_type
        
        text = ""

        if file_type == "application/pdf":
            # Read PDF file using PyPDF2
            pdf_reader = PdfReader(io.BytesIO(file_content))
            for page in pdf_reader.pages:
                text += page.extract_text()
        
        elif file_type in ["image/jpeg", "image/png", "image/jpg"]:
            # Read Image file using PIL and pytesseract
            image = Image.open(io.BytesIO(file_content))
            text = vision(image)
        
        else:
            raise HTTPException(status_code=400, detail="Unsupported file type")

        # Call Gemini (or another model) to extract required data
        prompt = f"""This is CV data: {text.strip()} 
         I want only:

         firstname, lastname, contact number, total years of experience, LinkedIn link, experience, skills

         in JSON format only"""
        
        response = model_text.generate_content(prompt)
        data = json.loads(response.text.replace("```json", "").replace("```", ""))
        return {"data": data}

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}")