Update app.py

app.py

@@ -80,6 +80,7 @@ def parse_ingredients(text):
     ingredients = [i.strip().lower() for i in ingredients if i.strip()]
     return ingredients
 
+# Function to analyze ingredients with Gemini
 # Function to analyze ingredients with Gemini
 def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
     """
@@ -90,7 +91,11 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
 
     # Prepare the list of ingredients for the prompt
     ingredients_text = ", ".join(ingredients_list)
-
+    
+    # Check if Gemini API is available
+    if not genai or not os.getenv("GEMINI_API_KEY"):
+        return dummy_analyze(ingredients_list, health_conditions)
+    
     # Create a prompt for Gemini
     if health_conditions and health_conditions.strip():
         prompt = f"""
@@ -115,19 +120,49 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
         """
 
     try:
-        # Check if Gemini API is available
-        if not genai or not os.getenv("GEMINI_API_KEY"):
-            return dummy_analyze(ingredients_list, health_conditions)
-            
         # Call the Gemini API
-        model = genai.GenerativeModel('gemini-pro')
-        response = model.generate_content(prompt)
-
-        # Check if response is valid
-        if hasattr(response, 'text') and response.text:
-            analysis = response.text
-        else:
-            return "Error: Received empty response from analysis service. Please try again."
+        try:
+            model = genai.GenerativeModel('gemini-pro')
+            response = model.generate_content(prompt)
+            
+            # Check if response is valid
+            if hasattr(response, 'text') and response.text:
+                analysis = response.text
+            else:
+                # Fall back to alternative model if available
+                try:
+                    models = genai.list_models()
+                    available_models = [m.name for m in models]
+                    if 'gemini-1.0-pro' in available_models:
+                        model = genai.GenerativeModel('gemini-1.0-pro')
+                    elif 'gemini-1.5-pro' in available_models:
+                        model = genai.GenerativeModel('gemini-1.5-pro')
+                    else:
+                        # If no alternative model is available, use dummy analysis
+                        return dummy_analyze(ingredients_list, health_conditions) + "\n\n(Using fallback analysis due to API model availability issues)"
+                    
+                    response = model.generate_content(prompt)
+                    analysis = response.text if hasattr(response, 'text') else "Error: Received empty response"
+                except Exception as model_e:
+                    return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(model_e)})"
+        except Exception as e:
+            if "404 models/gemini-pro is not found" in str(e):
+                # Try listing available models and use an alternative if possible
+                try:
+                    models = genai.list_models()
+                    available_models = [m.name for m in models]
+                    if not available_models:
+                        return dummy_analyze(ingredients_list, health_conditions) + "\n\n(Using fallback analysis due to API model availability issues)"
+                    
+                    # Use first available model
+                    model = genai.GenerativeModel(available_models[0])
+                    response = model.generate_content(prompt)
+                    analysis = response.text if hasattr(response, 'text') else "Error: Received empty response"
+                except Exception as model_e:
+                    return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(model_e)})"
+            else:
+                # Handle other exceptions
+                return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(e)})"
 
         # Add disclaimer
         disclaimer = """
@@ -140,7 +175,7 @@ def analyze_ingredients_with_gemini(ingredients_list, health_conditions=None):
 
     except Exception as e:
         # Fallback to basic analysis if API call fails
-        return f"Error connecting to analysis service: {str(e)}\n\nPlease try again later."
+        return dummy_analyze(ingredients_list, health_conditions) + f"\n\n(Using fallback analysis: {str(e)})"
 
 # Dummy analysis function for when API is not available
 def dummy_analyze(ingredients_list, health_conditions=None):