Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
 from flask import Flask, request, jsonify
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 from werkzeug.utils import secure_filename
 from pdf2image import convert_from_path
 import pytesseract
@@ -16,17 +16,17 @@ app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 
 # Load AI Pipelines
 ocr_pipeline = pipeline("image-to-text", model="microsoft/trocr-small-printed")  # OCR Model
-text_gen_pipeline = pipeline("text-generation", model="distilbert/distilgpt2")   # Text Generation Model
+text_gen_pipeline = pipeline("text-generation", model="gpt2")  # GPT-2 for text generation
 
 # Function to extract text from a PDF resume
 def extract_text_from_pdf(pdf_path):
     images = convert_from_path(pdf_path)
     extracted_text = ""
-    
+
     for img in images:
         text = pytesseract.image_to_string(img)  # OCR extraction
         extracted_text += text + "\n"
-    
+
     return extracted_text.strip()
 
 # Route: Upload Resume & Generate Report
@@ -34,11 +34,11 @@ def extract_text_from_pdf(pdf_path):
 def upload_resume():
     if 'file' not in request.files:
         return jsonify({"error": "No file uploaded"}), 400
-    
+
     file = request.files['file']
     if file.filename == '':
         return jsonify({"error": "No file selected"}), 400
-    
+
     # Save uploaded file
     filename = secure_filename(file.filename)
     file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
@@ -56,7 +56,7 @@ def upload_resume():
         "resume_text": extracted_text[:1000],  # Limit to 1000 chars for display
         "ai_evaluation": ai_evaluation
     }
-    
+
     return jsonify(response)
 
 # Run Flask App