Update parser_logic.py

parser_logic.py

@@ -28,17 +28,51 @@ def extract_text_from_stream(file_bytes: bytes) -> str:
         raise ValueError("Failed to extract text from PDF.")
     return text
 
-def analyze_resume(resume_text: str, job_description: str = None) -> dict:
-    """
-    Analyzes resume. If JD is provided, performs matching.
+def get_available_model_name():
     """
-    
-    # Base prompt (Extraction only)
-    base_instructions = """
-    Extract structured data from the resume.
+    Dynamically finds a working model from the user's account.
     """
+    try:
+        available_models = []
+        for m in genai.list_models():
+            if 'generateContent' in m.supported_generation_methods:
+                available_models.append(m.name)
+        
+        if not available_models:
+            logger.error("No models found.")
+            return None
+
+        # Priority list: Try to find these specific powerful models first
+        preferred_order = [
+            "models/gemini-1.5-flash",
+            "models/gemini-1.5-pro",
+            "models/gemini-pro",
+            "models/gemini-1.0-pro"
+        ]
+        
+        # 1. Check if any preferred model is in the available list
+        for preferred in preferred_order:
+            if preferred in available_models:
+                logger.info(f"Selected Preferred Model: {preferred}")
+                return preferred
+
+        # 2. If none of the preferred ones exist, take the first available one
+        fallback = available_models[0]
+        logger.warning(f"Preferred models missing. Falling back to: {fallback}")
+        return fallback
+
+    except Exception as e:
+        logger.error(f"Error listing models: {e}")
+        return None
+
+def analyze_resume(resume_text: str, job_description: str = None) -> dict:
     
-    # Extended prompt (Matching)
+    # 1. FIND A WORKING MODEL (The Critical Fix)
+    model_name = get_available_model_name()
+    if not model_name:
+        return {"error": "CRITICAL: No available AI models found for this API Key."}
+
+    # 2. CONSTRUCT PROMPT
     if job_description:
         prompt = f"""
         Act as a strict AI Recruiter. Compare the Resume against the Job Description.
@@ -68,7 +102,6 @@ def analyze_resume(resume_text: str, job_description: str = None) -> dict:
         {resume_text[:10000]}
         """
     else:
-        # Fallback to simple extraction if no JD
         prompt = f"""
         Extract structured data from the resume. Return JSON:
         {{
@@ -85,21 +118,15 @@ def analyze_resume(resume_text: str, job_description: str = None) -> dict:
         {resume_text[:10000]}
         """
 
-    # Model Strategy: Try Flash first, fallback to Pro
-    models = ['gemini-1.5-flash', 'gemini-pro']
-    
-    for model_name in models:
-        try:
-            model = genai.GenerativeModel(model_name)
-            response = model.generate_content(prompt)
-            
-            # Clean JSON
-            raw = response.text.strip()
-            clean_json = re.sub(r'```json\s*|```', '', raw, flags=re.MULTILINE).strip()
-            return json.loads(clean_json)
-            
-        except Exception as e:
-            logger.warning(f"Model {model_name} failed: {e}")
-            if model_name == models[-1]:
-                return {"error": f"Analysis failed. Detail: {str(e)}"}
-            continue
+    # 3. GENERATE CONTENT
+    try:
+        model = genai.GenerativeModel(model_name)
+        response = model.generate_content(prompt)
+        
+        raw = response.text.strip()
+        clean_json = re.sub(r'```json\s*|```', '', raw, flags=re.MULTILINE).strip()
+        return json.loads(clean_json)
+        
+    except Exception as e:
+        logger.error(f"Analysis failed with model {model_name}: {e}")
+        return {"error": f"Analysis failed using {model_name}. Detail: {str(e)}"}