Update app.py

app.py

@@ -2,13 +2,22 @@ import gradio as gr
 from openai import OpenAI
 import base64
 import io
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 def solve_stem_problem(api_key, image, subject="math"):
     # Initialize OpenAI client with user-provided API key
-    client = OpenAI(
-        base_url="https://openrouter.ai/api/v1",
-        api_key=api_key,
-    )
+    try:
+        client = OpenAI(
+            base_url="https://openrouter.ai/api/v1",
+            api_key=api_key,
+        )
+    except Exception as e:
+        logger.error(f"Failed to initialize OpenAI client: {str(e)}")
+        return f"Error initializing API client: {str(e)}"
     
     # Define detective based on subject
     detectives = {
@@ -30,16 +39,18 @@ def solve_stem_problem(api_key, image, subject="math"):
         encoded_image = base64.b64encode(img_byte_arr).decode('utf-8')
         image_url_data = f"data:image/png;base64,{encoded_image}"
     except Exception as e:
+        logger.error(f"Image encoding error: {str(e)}")
         return f"Error encoding image: {str(e)}"
     
-    # Call the Gemini model
+    # Call the API with error handling
     try:
         completion = client.chat.completions.create(
             extra_headers={
                 "HTTP-Referer": "https://stem-sleuth.example.com",
                 "X-Title": "STEM Sleuth",
             },
-            model="google/gemini-2.0-flash-exp:free",
+            # Using a more stable model (adjust based on OpenRouter's available models)
+            model="google/gemini-flash-1.5",
             messages=[
                 {
                     "role": "user",
@@ -57,15 +68,22 @@ def solve_stem_problem(api_key, image, subject="math"):
             ]
         )
         
-        # Check for valid response
-        if completion.choices and len(completion.choices) > 0 and completion.choices[0].message:
-            solution = completion.choices[0].message.content
-        else:
-            solution = "Could not retrieve a solution from the API."
+        # Detailed response checking
+        if not completion.choices:
+            logger.warning("API returned no choices")
+            return "API returned no choices. Please check model availability or API key permissions."
+        
+        if not completion.choices[0].message:
+            logger.warning("API returned no message content")
+            return "API returned no message content. Please try again or check the model."
+        
+        solution = completion.choices[0].message.content
+        logger.info("Successfully retrieved solution")
+        return solution
+        
     except Exception as e:
-        solution = f"Error calling API: {str(e)}"
-    
-    return solution
+        logger.error(f"API call failed: {str(e)}")
+        return f"Error calling API: {str(e)}. Please verify model availability or try again later."
 
 # Create Gradio interface
 with gr.Blocks() as app: