Update app.py

app.py

@@ -58,7 +58,12 @@ for space_info in spaces:
     @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
     def create_tool_with_retry(repo_id, name, description, api_name):
         # If api_name is None, Tool.from_space will try to find a public API endpoint.
-        return Tool.from_space(repo_id, name=name, description=description, api_name=api_name)
+        print(f"Attempting to create tool: '{name}' from space: {repo_id} with api_name: {api_name}")
+        new_tool = Tool.from_space(repo_id, name=name, description=description, api_name=api_name)
+        # Explicitly check if name attribute is set after creation by Tool.from_space
+        if not hasattr(new_tool, 'name') or new_tool.name != name:
+            print(f"WARNING: Tool '{name}' from space {repo_id} might have a name mismatch or missing name attribute after creation. Actual name: {getattr(new_tool, 'name', 'MISSING')}")
+        return new_tool
 
     try:
         tool = create_tool_with_retry(repo_id, name, description, api_name)
@@ -67,10 +72,9 @@ for space_info in spaces:
     except Exception as e:
         print(f"Failed to load predefined tool from {repo_id}. Error: {str(e)}. Continuing with available tools.")
 
-# Load tools from a Hugging Face Collection
+# Load tools from a Hugging Face Collection (User has this commented out)
 #collection_slug = "jkorstad/tools-680127d17eed47e759549ff4"
 #try:
-    # Added trust_remote_code=True
 #    collection = ToolCollection.from_hub(collection_slug=collection_slug, trust_remote_code=True)
 #    tools.extend(collection.tools)
 #    print(f"Successfully loaded tools from collection: {collection_slug}")
@@ -89,19 +93,16 @@ def search_hf_spaces(query: str, top_k: int = 3) -> str:
     """
     try:
         print(f"Searching spaces with query: {query}, top_k: {top_k}")
-        # Using list_spaces, ensure it's imported: from huggingface_hub import list_spaces
-        # full=True gives more metadata, sort by likes, direction=-1 for descending
         spaces_found = list(list_spaces(search=query, full=True, limit=top_k, sort="likes", direction=-1))
         if not spaces_found:
             return "No Spaces found for your query."
 
         results = "Found the following Spaces (sorted by likes):\n"
         for i, space_data in enumerate(spaces_found):
-            # Safely access attributes, as they might not always be present
             description = "No description provided."
             if hasattr(space_data, 'cardData') and space_data.cardData and 'description' in space_data.cardData:
                 description = space_data.cardData['description']
-            elif hasattr(space_data, 'title') and space_data.title: # Fallback to title
+            elif hasattr(space_data, 'title') and space_data.title:
                 description = space_data.title
 
             results += (
@@ -118,7 +119,6 @@ def search_hf_spaces(query: str, top_k: int = 3) -> str:
         return results
     except Exception as e:
         print(f"Error searching Spaces: {str(e)}")
-        # traceback.print_exc() # Uncomment for detailed search error debugging
         return f"Error searching Spaces: {str(e)}"
 
 space_search_tool = Tool(
@@ -128,6 +128,23 @@ space_search_tool = Tool(
 )
 tools.append(space_search_tool)
 
+# --- Debugging: Inspect tools before CodeAgent initialization ---
+print("\n--- Inspecting tools before CodeAgent initialization ---")
+for i, t in enumerate(tools):
+    if t is None:
+        print(f"Tool at index {i} is None!")
+        # This would cause an error later, but the current error is 'Tool' object has no attribute 'name'
+        continue
+    try:
+        # Attempt to access the name attribute
+        tool_name = t.name
+        print(f"Tool {i}: Name='{tool_name}', Type={type(t)}")
+    except AttributeError:
+        print(f"!!! CRITICAL: Tool at index {i} (Type={type(t)}) is missing 'name' attribute.")
+    except Exception as e:
+        print(f"!!! ERROR inspecting tool at index {i} (Type={type(t)}): {str(e)}")
+print("-------------------------------------------------------\n")
+
 
 # Initialize the model - Use InferenceClientModel
 model = InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct") # Or your preferred model
@@ -191,11 +208,9 @@ def gradio_interface(user_prompt, input_image_path, input_audio_path, input_vide
         full_prompt_with_instructions = f"{AGENT_INSTRUCTIONS}\n\nUSER PROMPT: {user_prompt}"
 
         # Prepare a dictionary of potential inputs for the agent's execution scope
-        # These will be available as global variables in the agent's Python execution environment
-        # when agent.run is called with keyword arguments.
         agent_kwargs = {}
         if input_image_path:
-            agent_kwargs["input_image_path"] = str(input_image_path) # Ensure it's a string path
+            agent_kwargs["input_image_path"] = str(input_image_path)
         if input_audio_path:
             agent_kwargs["input_audio_path"] = str(input_audio_path)
         if input_video_path:
@@ -206,19 +221,16 @@ def gradio_interface(user_prompt, input_image_path, input_audio_path, input_vide
             agent_kwargs["input_file_path"] = str(input_file_path)
 
         progress(0.2, desc="Agent processing request...")
-        # The first argument to agent.run is the main prompt.
-        # Other kwargs are set as global variables in the agent's execution context.
         result = agent.run(full_prompt_with_instructions, **agent_kwargs)
 
         progress(0.8, desc="Processing result...")
-        # Default all outputs to invisible and None
         outputs = {
             "image": gr.update(value=None, visible=False),
             "file": gr.update(value=None, visible=False),
             "path": gr.update(value=None, visible=False),
             "audio": gr.update(value=None, visible=False),
             "model3d": gr.update(value=None, visible=False),
-            "text": gr.update(value=None, visible=True), # Text output is often default
+            "text": gr.update(value=None, visible=True),
         }
 
         if isinstance(result, str):
@@ -226,21 +238,20 @@ def gradio_interface(user_prompt, input_image_path, input_audio_path, input_vide
                 file_path = result
                 outputs["file"] = gr.update(value=file_path, visible=True)
                 outputs["path"] = gr.update(value=file_path, visible=True)
-                ext = os.path.splitext(file_path.lower())[1] # Get extension like .png
+                ext = os.path.splitext(file_path.lower())[1]
                 if ext in ('.png', '.jpg', '.jpeg', '.gif', '.webp'):
                     outputs["image"] = gr.update(value=file_path, visible=True)
                 elif ext in ('.mp3', '.wav', '.ogg', '.flac'):
                     outputs["audio"] = gr.update(value=file_path, visible=True)
-                elif ext == '.glb': # Common format for Model3D
+                elif ext == '.glb':
                     outputs["model3d"] = gr.update(value=file_path, visible=True)
-                else: # Other file types like PDF, TXT - user can download via file component
+                else:
                     outputs["text"] = gr.update(value=f"Output is a file: {os.path.basename(file_path)}. Download it using the 'Download File Output' component.", visible=True)
             else:
-                # Result is a string (e.g., text output from a tool or an error message from the agent)
                 outputs["text"] = gr.update(value=result, visible=True)
         elif result is None:
             outputs["text"] = gr.update(value="Agent returned no result (None). This might indicate an issue or that the task didn't produce a specific output string/file.", visible=True)
-        else: # Other types (e.g. if agent returns a dict or list by mistake)
+        else:
             outputs["text"] = gr.update(value=f"Unexpected result type from agent: {type(result)}. Content: {str(result)}", visible=True)
 
         progress(1, desc="Done!")
@@ -251,12 +262,12 @@ def gradio_interface(user_prompt, input_image_path, input_audio_path, input_vide
 
     except Exception as e:
         error_msg = f"An error occurred in the Gradio interface or agent execution: {str(e)}"
-        print(error_msg) # Also print to console for server-side logs
-        traceback.print_exc() # For more detailed debugging
+        print(error_msg)
+        traceback.print_exc()
         return (
             gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False),
             gr.update(value=None, visible=False), gr.update(value=None, visible=False),
-            gr.update(value=error_msg, visible=True) # Show error in the text output
+            gr.update(value=error_msg, visible=True)
         )
 
 # Create the Gradio app
@@ -277,8 +288,8 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
             input_image = gr.Image(label="Image Input", type="filepath", sources=["upload", "clipboard"], elem_id="input_image_upload")
             input_audio = gr.Audio(label="Audio Input", type="filepath", sources=["upload", "microphone"], elem_id="input_audio_upload")
         with gr.Row():
-            input_video = gr.Video(label="Video Input", type="filepath", sources=["upload"], elem_id="input_video_upload") # Gradio Video component might have limitations
-            input_model3d = gr.Model3D(label="3D Model Input (.glb, .obj, etc.)", type="filepath", elem_id="input_model3d_upload") # Check Gradio docs for supported Model3D types
+            input_video = gr.Video(label="Video Input", type="filepath", sources=["upload"], elem_id="input_video_upload")
+            input_model3d = gr.Model3D(label="3D Model Input (.glb, .obj, etc.)", type="filepath", elem_id="input_model3d_upload")
         with gr.Row():
             input_file = gr.File(label="Generic File Input (PDF, TXT, etc.)", type="filepath", elem_id="input_file_upload")
 
@@ -290,12 +301,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         audio_output = gr.Audio(label="Audio Output", interactive=False, visible=False, show_download_button=True, elem_id="output_audio_display")
     with gr.Row():
         model3d_output = gr.Model3D(label="3D Model Output", interactive=False, visible=False, show_download_button=True, elem_id="output_model3d_display")
-        text_output = gr.Textbox(label="Text / Log Output", interactive=False, visible=True, lines=5, max_lines=20, elem_id="output_text_log") # Start visible for logs/text
+        text_output = gr.Textbox(label="Text / Log Output", interactive=False, visible=True, lines=5, max_lines=20, elem_id="output_text_log")
     with gr.Row():
         file_output = gr.File(label="Download File Output", interactive=False, visible=False, elem_id="output_file_download")
-        path_output = gr.Textbox(label="Output File Path (Copyable)", interactive=False, visible=False, elem_id="output_file_path_text") # Keep for copying if needed
+        path_output = gr.Textbox(label="Output File Path (Copyable)", interactive=False, visible=False, elem_id="output_file_path_text")
 
-    # Link button click to the interface function
     submit_button.click(
         fn=gradio_interface,
         inputs=[prompt_input, input_image, input_audio, input_video, input_model3d, input_file],
@@ -307,16 +317,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
             ["Generate an image of a happy robot coding on a laptop, cyberpunk style.", None, None, None, None, None],
             ["Convert the following text to speech: 'Smolagents are amazing for building AI applications.'", None, None, None, None, None],
             ["Search for a Hugging Face Space that can perform image captioning. Describe the first result.", None, None, None, None, None],
-            # For examples with file inputs, the user needs to upload a file manually.
-            # The string path here is just a placeholder for the example text.
             ["I have an image of a cat. Find a space that can make it look like a painting and apply it. You will need to use the 'input_image_path' variable which will contain the path to the uploaded cat image.", "path/to/your/cat_image.png", None, None, None, None],
         ],
         inputs=[prompt_input, input_image, input_audio, input_video, input_model3d, input_file],
         label="Example Prompts (Note: For examples with file inputs, you'll need to upload a relevant file first using the 'Optional File Inputs' section)"
     )
 
-# Launch the app
 if __name__ == "__main__":
-    # share=True can be used to create a public link if you're running this locally and want to test from another device.
-    # debug=True provides more detailed Gradio logs.
-    app.launch(debug=True)
+    app.launch(debug=True)