Update app.py

app.py

@@ -423,10 +423,16 @@ def create_consistency_hash(resume_text, job_description, prompt_type):
     content = f"{resume_text[:1000]}{job_description[:1000]}{prompt_type}"
     return hashlib.md5(content.encode()).hexdigest()
 
-def get_available_model():
+def get_available_model(force_primary=True):
     """Get the first available model from the fallback chain"""
+    if force_primary:
+        # Force use of primary model without testing
+        return MODEL_FALLBACK_CHAIN[0]  # gemini-2.5-flash
+    
     for model in MODEL_FALLBACK_CHAIN:
         try:
+            st.info(f"🔍 Testing model: {model}")
+            
             test_model = genai.GenerativeModel(
                 model,
                 safety_settings={
@@ -436,6 +442,7 @@ def get_available_model():
                     genai.types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: genai.types.HarmBlockThreshold.BLOCK_NONE,
                 }
             )
+            
             # Test with a simple prompt
             test_response = test_model.generate_content(
                 "Say 'OK'",
@@ -444,9 +451,15 @@ def get_available_model():
                     max_output_tokens=10
                 )
             )
+            
             if test_response.text:
+                st.success(f"✅ {model} - Test successful!")
                 return model
-        except Exception:
+            else:
+                st.warning(f"⚠️ {model} - No response text")
+                
+        except Exception as e:
+            st.error(f"❌ {model} - Failed: {str(e)}")
             continue
     
     raise Exception("No available Gemini models found")
@@ -675,7 +688,7 @@ def load_system_status():
     issues = validate_configuration()
     return issues
 
-def perform_enhanced_analysis(resume_text, job_description, analysis_type, custom_query=None):
+def perform_enhanced_analysis(resume_text, job_description, analysis_type, custom_query=None, force_primary=True):
     """Main analysis function with all improvements"""
     
     # Initialize cache
@@ -688,7 +701,7 @@ def perform_enhanced_analysis(resume_text, job_description, analysis_type, custo
     consistency_hash = create_consistency_hash(optimized_resume, optimized_job, analysis_type)
     
     # Try to get from cache first
-    model_id = get_available_model()
+    model_id = get_available_model(force_primary=force_primary)
     cached_response = get_cached_response(consistency_hash, model_id)
     
     if cached_response:
@@ -802,10 +815,14 @@ with st.sidebar:
     api_key = st.text_input("🔐 Google API Key", type="password", help="Your Gemini API key for AI analysis")
     st.session_state["api_key"] = api_key
     
+    # Model selection option
+    force_primary = st.checkbox("🎯 Force Primary Model", value=True, help="Use gemini-2.5-flash directly without testing")
+    st.session_state["force_primary"] = force_primary
+    
     if api_key:
         try:
             genai.configure(api_key=api_key)
-            model_id = get_available_model()
+            model_id = get_available_model(force_primary=force_primary)
             st.success(f"✅ Connected to {model_id}")
         except Exception as e:
             st.error(f"❌ API Key Error: {str(e)}")
@@ -902,9 +919,12 @@ if analysis_triggered:
                 analysis_type = "custom"
             
             try:
+                # Get force_primary setting
+                force_primary_setting = st.session_state.get("force_primary", True)
+                
                 # Perform analysis
                 response, consistency_hash = perform_enhanced_analysis(
-                    pdf_content, input_text, analysis_type, custom_query
+                    pdf_content, input_text, analysis_type, custom_query, force_primary_setting
                 )
                 
                 # Display results