| # 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,Query | |
| # 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 | |
| # import fitz # PyMuPDF | |
| # import os | |
| # from dotenv import load_dotenv | |
| # # Load the environment variables from the .env file | |
| # load_dotenv() | |
| # # Configure Gemini API | |
| # secret = os.environ["GEMINI"] | |
| # genai.configure(api_key=secret) | |
| # model_vision = genai.GenerativeModel('gemini-1.5-flash') | |
| # model_text = genai.GenerativeModel('gemini-pro') | |
| # app = FastAPI() | |
| # app.add_middleware( | |
| # CORSMiddleware, | |
| # allow_origins=["*"], | |
| # allow_credentials=True, | |
| # allow_methods=["*"], | |
| # allow_headers=["*"], | |
| # ) | |
| # def vision(file_content): | |
| # # Open the PDF | |
| # pdf_document = fitz.open("pdf",file_content) | |
| # gemini_input = ["extract the whole text"] | |
| # # Iterate through the pages | |
| # for page_num in range(len(pdf_document)): | |
| # # Select the page | |
| # page = pdf_document.load_page(page_num) | |
| # # Render the page to a pixmap (image) | |
| # pix = page.get_pixmap() | |
| # print(type(pix)) | |
| # # Convert the pixmap to bytes | |
| # img_bytes = pix.tobytes("png") | |
| # # Convert bytes to a PIL Image | |
| # img = Image.open(io.BytesIO(img_bytes)) | |
| # gemini_input.append(img) | |
| # # # Save the image if needed | |
| # # img.save(f'page_{page_num + 1}.png') | |
| # print("PDF pages converted to images successfully!") | |
| # # Now you can pass the PIL image to the model_vision | |
| # response = model_vision.generate_content(gemini_input).text | |
| # return response | |
| # @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() | |
| # if len(text)<10: | |
| # print("vision called") | |
| # text = vision(file_content) | |
| # 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()} | |
| # IMPORTANT: The output should be a JSON array! Make Sure the JSON is valid. | |
| # Example Output: | |
| # [ | |
| # "firstname" : "firstname", | |
| # "lastname" : "lastname", | |
| # "gender" : "gender", | |
| # "email" : "email", | |
| # "contact_number" : "contact number", | |
| # "age" : "age", | |
| # "home_address" : "full home address", | |
| # "home_town" : "home town or city", | |
| # "total_years_of_experience" : "total years of experience", | |
| # "LinkedIn_link" : "LinkedIn link", | |
| # "positions": [ "Job title 1", "Job title 2", "Job title 3" ], | |
| # "industry": "industry of work", | |
| # "experience" : "experience", | |
| # "skills" : Skills(Identify and list specific skills mentioned in both the skills section and inferred from the experience section) | |
| # ] | |
| # """ | |
| # response = model_text.generate_content(prompt) | |
| # print(response.text) | |
| # data = json.loads(response.text.replace("JSON", "").replace("json", "").replace("```", "")) | |
| # return {"data": data} | |
| # #except Exception as e: | |
| # #raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}") | |
| from fastapi import FastAPI, HTTPException, File, UploadFile, Query | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from PyPDF2 import PdfReader | |
| import google.generativeai as genai | |
| import json | |
| from PIL import Image | |
| import io | |
| import fitz # PyMuPDF | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| secret = os.environ["GEMINI"] | |
| genai.configure(api_key=secret) | |
| model_vision = genai.GenerativeModel('gemini-1.5-flash') | |
| model_text = genai.GenerativeModel('gemini-pro') | |
| app = FastAPI() | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| def process_pdf_text(file_content): | |
| """Extract text from PDF using PyPDF2.""" | |
| pdf_reader = PdfReader(io.BytesIO(file_content)) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def process_pdf_images(file_content): | |
| """Extract images from PDF and pass to Gemini Vision.""" | |
| pdf_document = fitz.open("pdf", file_content) | |
| gemini_input = [] | |
| for page_num in range(len(pdf_document)): | |
| page = pdf_document.load_page(page_num) | |
| pix = page.get_pixmap() | |
| img_bytes = pix.tobytes("png") | |
| img = Image.open(io.BytesIO(img_bytes)) | |
| gemini_input.append(img) | |
| # Call Gemini Vision with extracted images | |
| response = model_vision.generate_content(["extract the whole text", *gemini_input]) | |
| return response.text | |
| async def get_data(user_id: str = Query(...), input_file: UploadFile = File(...)): | |
| try: | |
| file_content = await input_file.read() | |
| file_type = input_file.content_type | |
| if file_type != "application/pdf": | |
| raise HTTPException(status_code=400, detail="Unsupported file type") | |
| # Process PDF | |
| text = process_pdf_text(file_content) | |
| if len(text.strip()) < 10: # Fallback to image-based OCR if text is minimal | |
| text = process_pdf_images(file_content) | |
| # Call Gemini Text model | |
| prompt = f""" | |
| This is CV data: {text.strip()} | |
| IMPORTANT: The output should be a JSON array! Make sure the JSON is valid. | |
| Example Output: | |
| [ | |
| "firstname" : "firstname", | |
| "lastname" : "lastname", | |
| "email" : "email", | |
| "contact_number" : "contact number", | |
| "home_address" : "full home address", | |
| "home_town" : "home town or city", | |
| "total_years_of_experience" : "total years of experience", | |
| "education": "Institution Name, Degree Name", | |
| "LinkedIn_link" : "LinkedIn link", | |
| "experience" : "experience", | |
| "industry": "industry of work", | |
| "skills" : skills(Identify and list specific skills mentioned in both the skills section and inferred from the experience section), | |
| "positions": [ "Job title 1", "Job title 2", "Job title 3" ], | |
| "summary": "Generate a summary of the CV, including key qualifications, notable experiences, and relevant skills." | |
| ] | |
| """ | |
| response = model_text.generate_content(prompt) | |
| data = json.loads(response.text.replace("```", "")) # Sanitize response | |
| return {"data": data} | |
| # except Exception as e: | |
| # raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}") | |