Update app.py

app.py

@@ -59,19 +59,34 @@ class HuggingFaceSpaceSearcherTool(Tool):
     # Define attributes as class variables
     name = "huggingface_space_searcher"
     description = "Searches for Hugging Face Spaces that can perform a specific task. Input is a search query string (e.g., 'text to image', 'speech recognition'). Returns a list of Space IDs, their descriptions, and instructions on how to try using them."
-    # Define input schema if needed, for now, we'll rely on type hinting in forward
-    # inputs = { "query": {"type": "string", "description": "The search query for Hugging Face Spaces."} } 
-    # output_type = "string" # Optional: define output type
+    
+    # Define input schema as required by smolagents.Tool base class
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The search query for Hugging Face Spaces."
+        },
+        "top_k": {
+            "type": "integer",
+            "description": "The number of top results to return (default is 3).",
+            "optional": True # Marking as optional; the forward method has a default.
+        }
+    }
+    output_type = "string" # Optional: define output type
 
     # The core logic goes into the forward method
+    # The Tool base class will likely call this with arguments unpacked from a dictionary matching the 'inputs' schema.
     def forward(self, query: str, top_k: int = 3) -> str:
         """
         Searches Hugging Face Spaces for a given query and returns the top_k results.
         Provides repo_id, description, likes, and last modified date for each space found.
         """
         try:
-            print(f"Searching spaces with query: {query}, top_k: {top_k}")
-            spaces_found = list(list_spaces(search=query, full=True, limit=top_k, sort="likes", direction=-1))
+            # top_k will be provided by the Tool base class mechanism, potentially from kwargs if optional
+            # or directly if the agent passes it. The default in the signature is a fallback.
+            actual_top_k = top_k if top_k is not None else 3 # Ensure top_k has a value
+            print(f"Searching spaces with query: {query}, top_k: {actual_top_k}")
+            spaces_found = list(list_spaces(search=query, full=True, limit=actual_top_k, sort="likes", direction=-1))
             if not spaces_found:
                 return "No Spaces found for your query."
 
@@ -104,14 +119,15 @@ space_search_tool = HuggingFaceSpaceSearcherTool()
 # ---- Debug print for the refactored tool ----
 try:
     print(f"\nDEBUG: 'space_search_tool' (refactored class) immediately after creation.")
-    print(f"DEBUG: Name: {space_search_tool.name}") # Should now correctly access the class attribute
+    print(f"DEBUG: Name: {space_search_tool.name}") 
+    print(f"DEBUG: Inputs: {space_search_tool.inputs}") # Check if inputs are set
     print(f"DEBUG: Type: {type(space_search_tool)}")
-    print(f"DEBUG: All attributes: {dir(space_search_tool)}\n")
+    # print(f"DEBUG: All attributes: {dir(space_search_tool)}\n") # Can be verbose
 except AttributeError as e:
     print(f"\nDEBUG: 'space_search_tool' (refactored class) immediately after creation.")
-    print(f"DEBUG: Name attribute STILL MISSING. Error: {e}")
+    print(f"DEBUG: Attribute MISSING. Error: {e}")
     print(f"DEBUG: Type: {type(space_search_tool)}")
-    print(f"DEBUG: All attributes: {dir(space_search_tool)}\n")
+    # print(f"DEBUG: All attributes: {dir(space_search_tool)}\n")
 # ---- END Debug print ----
 tools.append(space_search_tool)
 
@@ -124,7 +140,7 @@ for i, t in enumerate(tools):
         continue
     try:
         tool_name = t.name
-        print(f"Tool {i}: Name='{tool_name}', Type={type(t)}")
+        print(f"Tool {i}: Name='{tool_name}', Type={type(t)}, Inputs: {getattr(t, 'inputs', 'Not defined')}")
     except AttributeError:
         print(f"!!! CRITICAL: Tool at index {i} (Type={type(t)}) is missing 'name' attribute.")
     except Exception as e:
@@ -147,8 +163,9 @@ AGENT_INSTRUCTIONS = """You are a highly capable AI assistant. Your primary goal
 
 Follow these steps:
 1.  **Understand the Request:** Carefully analyze the user's prompt (which will follow these instructions). Identify the core task and any specific requirements or inputs.
-2.  **Check Predefined Tools:** Review your list of available tools. If a predefined tool can directly address the request, use it. For the 'huggingface_space_searcher' tool, the input should be a dictionary like `{"query": "your search term"}` if you defined an inputs schema, or directly as arguments like `huggingface_space_searcher(query="your search term")` if using type hints in the forward method. The refactored `HuggingFaceSpaceSearcherTool` uses type hints in its `forward(self, query: str, top_k: int = 3)` method, so call it like `huggingface_space_searcher(query="your search term")`.
-3.  **Search for Spaces (If Needed):** If no predefined tool is suitable, use the `huggingface_space_searcher` tool. Provide a concise search query related to the task (e.g., "image classification", "voice cloning", "document question answering").
+2.  **Check Predefined Tools:** Review your list of available tools. If a predefined tool can directly address the request, use it.
+    * For the 'huggingface_space_searcher' tool, you MUST provide its arguments as a dictionary. For example: `huggingface_space_searcher(arguments={"query": "your search term", "top_k": 3})`. The `query` is mandatory. `top_k` is optional and defaults to 3 if not provided.
+3.  **Search for Spaces (If Needed):** If no predefined tool is suitable, use the `huggingface_space_searcher` tool as described above.
 4.  **Select and Instantiate a Space Tool:** From the search results, choose the most promising Space. Attempt to create a tool from it using `Tool.from_space(repo_id='SELECTED_SPACE_ID', name='a_unique_tool_name')`. You might need to give it a unique name. If `Tool.from_space` fails, the Space might not be compatible, or you could try another one from the search results.
 5.  **Execute the Tool:** Call the tool (either predefined or dynamically created) with the necessary arguments.
     * **File Inputs:** If the user uploads files, their paths will be available as global string variables: `input_image_path`, `input_audio_path`, `input_video_path`, `input_3d_model_path`, `input_file_path`. Before using these variables, check if they exist and are not None. Pass these file paths as arguments to tools that require them.
@@ -160,7 +177,7 @@ Follow these steps:
 
 Example of dynamically using a Space after searching:
 ```python
-# search_results = huggingface_space_searcher(query="text to image cat")
+# search_results = huggingface_space_searcher(arguments={"query": "text to image cat", "top_k": 1}) # Note the arguments dictionary
 # print(search_results)
 # try:
 #     cat_image_tool = Tool.from_space(repo_id="user/cat-generator", name="cat_generator_tool")