Update app.py

app.py

@@ -35,15 +35,77 @@ def extract_text_from_pdf(pdf_file):
     finally:
         gc.collect()
 
-def analyze_resume(pdf_file, job_desc=None, role_type="general", inference_fn=None):
+# Placeholder inference function to match your example output
+def dummy_inference_fn(prompt):
+    """Temporary inference function - replace with your actual AI model"""
+    # Simulate response based on your resume and job description in prompt
+    resume_text = prompt.split("Analyze this resume: '")[1].split("' against job description")[0]
+    job_desc = prompt.split("against job description: '")[1].split("'")[0]
+    
+    # Default response mimicking your example
+    response = {
+        "score": {
+            "total": 85,
+            "breakdown": {
+                "competency": 25,
+                "experience": 15,
+                "impact": 20,
+                "potential": 5,
+                "leadership": 0,
+                "adaptability": 20
+            }
+        },
+        "analysis": {
+            "strengths": [
+                "Strong frontend skills (React.js, JavaScript, UI components)",
+                "Proven performance impact (e.g., 30% code redundancy reduction, 20% efficiency boost)",
+                "Matches experience requirement (3+ years with relevant projects)"
+            ],
+            "improvements": [
+                "Emphasize UI/UX contributions in projects",
+                "Add leadership or teamwork examples for well-roundedness"
+            ],
+            "missing_skills": [],
+            "flags": []
+        }
+    }
+    
+    # Adjust slightly if no job description
+    if job_desc == "None":
+        response["score"]["adaptability"] = 10
+        response["score"]["total"] = 75
+        response["analysis"]["strengths"] = [
+            "Strong technical skills (MERN stack, blockchain)",
+            "Proven project impact (e.g., 25% session time increase)",
+            "Solid experience (3+ years)"
+        ]
+        response["analysis"]["improvements"] = [
+            "Add leadership or teamwork examples",
+            "Highlight learning initiatives"
+        ]
+    
+    return json.dumps(response)  # Return as JSON string
+
+# Real inference function example (uncomment and configure if you have a model)
+"""
+from transformers import pipeline
+def inference_fn(prompt):
+    model = pipeline("text-generation", model="gpt2", token=os.getenv("HF_TOKEN"))  # Replace with your model
+    response = model(prompt, max_length=2000, num_return_sequences=1)[0]["generated_text"]
+    start = response.find("[Return valid JSON]:") + len("[Return valid JSON]:")
+    return response[start:].strip()
+"""
+
+def analyze_resume(pdf_file, job_desc=None, inference_fn=dummy_inference_fn):
     """Smart ATS relying fully on AI for analysis"""
     try:
         resume_text = extract_text_from_pdf(pdf_file)
     except Exception as e:
         return f"Extraction failed: {str(e)}", {"error": str(e)}
     
-    # Fallback if no inference function (minimal manual analysis)
+    # Fallback if no inference function
     if not inference_fn:
+        print("No inference function provided - using fallback")
         basic_analysis = {
             "score": {"total": 10, "breakdown": {"competency": 10}},
             "analysis": {
@@ -57,7 +119,7 @@ def analyze_resume(pdf_file, job_desc=None, role_type="general", inference_fn=No
         return resume_text[:5000], basic_analysis
     
     # AI-driven analysis
-    prompt = f"""[Return valid JSON]: You are a smart ATS designed to evaluate resumes without rejecting worthy candidates. Analyze this resume: '{resume_text[:2000]}' against job description: '{job_desc or "None"}' (role type: {role_type}).
+    prompt = f"""[Return valid JSON]: You are a smart ATS designed to evaluate resumes without rejecting worthy candidates. Analyze this resume: '{resume_text[:2000]}' against job description: '{job_desc or "None"}'.
     Provide:
     - "score": {{total: X (0-100), breakdown: {{competency: X (technical/non-technical skills), experience: X (duration and depth), impact: X (achievements), potential: X (learning ability), leadership: X (influence), adaptability: X (fit to role or general)}}}}
     - "analysis": {{"strengths": [2-3 items, e.g., "Strong React skills"], "improvements": [2-3 items, e.g., "Add teamwork examples"], "missing_skills": [0-3 items, only if job_desc provided], "flags": [0-2 items, e.g., "High potential candidate"]}}
@@ -71,7 +133,9 @@ def analyze_resume(pdf_file, job_desc=None, role_type="general", inference_fn=No
     Return valid JSON only."""
     
     try:
+        print("Calling inference_fn with prompt:", prompt[:200])  # Debug
         result = inference_fn(prompt)
+        print("Inference result:", result)  # Debug
         if result and result.strip():
             analysis = json.loads(result)
             analysis["raw_text_sample"] = resume_text[:200]
@@ -82,7 +146,7 @@ def analyze_resume(pdf_file, job_desc=None, role_type="general", inference_fn=No
         print(f"AI analysis error: {str(e)}")
         return resume_text[:5000], {
             "score": {"total": 10, "breakdown": {"competency": 10}},
-            "analysis": {"strengths": ["Text processed"], "improvements": ["Analysis failed, retry"], "missing_skills": [], "flags": []},
+            "analysis": {"strengths": ["Text processed"], "improvements": [f"Analysis failed: {str(e)}"], "missing_skills": [], "flags": []},
             "raw_text_sample": resume_text[:200]
         }
 
@@ -92,7 +156,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         with gr.Column(scale=1):
             pdf_input = gr.File(label="PDF Resume", type="binary")
             job_desc_input = gr.Textbox(label="Job Description (Optional)", lines=3)
-            role_type_input = gr.Dropdown(label="Role Type", choices=["General", "Frontend", "Backend", "Non-Technical"], value="General")
             submit_btn = gr.Button("Analyze")
         with gr.Column(scale=2):
             extracted_text = gr.Textbox(label="Extracted Text", lines=10, interactive=False)
@@ -100,7 +163,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     submit_btn.click(
         fn=analyze_resume,
-        inputs=[pdf_input, job_desc_input, role_type_input],
+        inputs=[pdf_input, job_desc_input],
         outputs=[extracted_text, analysis_output]
     )