Update app.py

app.py

@@ -1,151 +1,9 @@
+from transformers import pipeline
 import gradio as gr
-import PyPDF2
-import io
-import os
-from dotenv import load_dotenv
 
-def extract_text_from_pdf(pdf_file):
-    """
-    Robust PDF text extraction with comprehensive error handling
-    
-    Args:
-        pdf_file (str/bytes): PDF file path or bytes
-    
-    Returns:
-        str: Extracted text from PDF
-    """
-    if pdf_file is None:
-        raise ValueError("No PDF file uploaded")
-    
-    try:
-        # Handle different input types
-        if isinstance(pdf_file, str):
-            with open(pdf_file, 'rb') as f:
-                file_bytes = f.read()
-        elif isinstance(pdf_file, bytes):
-            file_bytes = pdf_file
-        else:
-            raise TypeError(f"Unsupported file type: {type(pdf_file)}")
-        
-        # Advanced PDF text extraction
-        pdf_reader = PyPDF2.PdfReader(io.BytesIO(file_bytes))
-        
-        # Extract text from all pages, handle potential encoding issues
-        pages_text = []
-        for page in pdf_reader.pages:
-            try:
-                page_text = page.extract_text() or ""
-                pages_text.append(page_text.strip())
-            except Exception as page_error:
-                print(f"Error extracting page text: {page_error}")
-        
-        # Join pages, handle empty extraction
-        full_text = "\n".join(pages_text)
-        
-        if not full_text.strip():
-            raise ValueError("No text could be extracted from the PDF")
-        
-        # Limit text to prevent overwhelming AI
-        return full_text[:15000]  # Increased limit for more comprehensive analysis
-    
-    except Exception as e:
-        raise ValueError(f"PDF Extraction Error: {str(e)}")
+pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
 
-def prepare_resume_prompt(resume_text, job_description=None):
-    """
-    Prepare a structured, clear prompt for AI analysis
-    
-    Args:
-        resume_text (str): Extracted resume text
-        job_description (str, optional): Job description for context
-    
-    Returns:
-        str: Formatted prompt for AI analysis
-    """
-    prompt = f"""Professional Resume Analysis:
+def analyze(text):
+    return pipe(f"Analyze this resume: {text}")[0]["generated_text"]
 
-Resume Content:
-{resume_text[:10000]}
-
-{'Job Description: ' + job_description if job_description else 'No specific job description provided'}
-
-Instructions for Analysis:
-1. Perform a comprehensive assessment of the resume
-2. Evaluate professional skills, experience, and potential
-3. Provide a structured JSON response with:
-   - Overall Score (0-100)
-   - Skill Match Percentage
-   - Key Strengths
-   - Areas for Improvement
-   - Potential Red Flags
-   - Recommended Next Steps
-
-Output Format (JSON):
-{{
-  "total_score": int,
-  "skill_match_percentage": int,
-  "strengths": [str],
-  "improvements": [str],
-  "red_flags": [str],
-  "recommended_actions": [str]
-}}"""
-
-    return prompt
-
-def analyze_resume(pdf_file, job_description=None):
-    """
-    Main resume analysis function
-    
-    Args:
-        pdf_file (bytes): Uploaded PDF file
-        job_description (str, optional): Job description for context
-    
-    Returns:
-        tuple: Extracted text and AI analysis
-    """
-    try:
-        # Extract text from PDF
-        resume_text = extract_text_from_pdf(pdf_file)
-        
-        # Prepare prompt for AI
-        ai_prompt = prepare_resume_prompt(resume_text, job_description)
-        
-        # Note: Replace this with actual Mistral-7B inference
-        # This is a placeholder - you'll need to integrate your actual AI model
-        print("AI Prompt Prepared. Replace this with actual model inference.")
-        
-        return resume_text, {
-            "total_score": 75,
-            "skill_match_percentage": 80,
-            "strengths": ["Robust text extraction", "Structured prompt generation"],
-            "improvements": ["Integrate actual AI model inference"],
-            "red_flags": [],
-            "recommended_actions": ["Connect Mistral-7B model"]
-        }
-    
-    except Exception as e:
-        return str(e), {
-            "error": str(e),
-            "total_score": 0,
-            "skill_match_percentage": 0
-        }
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=1):
-            pdf_input = gr.File(label="Upload Resume PDF", type="binary")
-            job_desc_input = gr.Textbox(label="Job Description (Optional)", lines=3)
-            analyze_btn = gr.Button("Analyze Resume")
-        
-        with gr.Column(scale=2):
-            extracted_text = gr.Textbox(label="Extracted Text", lines=10)
-            analysis_output = gr.JSON(label="AI Analysis")
-    
-    analyze_btn.click(
-        fn=analyze_resume, 
-        inputs=[pdf_input, job_desc_input],
-        outputs=[extracted_text, analysis_output]
-    )
-
-demo.launch(share=True)
+gr.Interface(fn=analyze, inputs="text", outputs="text").launch()