Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Best Model Search
-emoji: 🦀
-colorFrom: red
-colorTo: indigo
+title: best-model-search
+app_file: main.py
 sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
+sdk_version: 4.44.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

@@ -0,0 +1,139 @@
+from smolagents import CodeAgent, InferenceClientModel, GradioUI, tool
+from huggingface_hub import HfApi
+import requests
+from typing import List, Dict
+
+@tool
+def leaderboard_search(query: str) -> str:
+    """
+    Search Hugging Face Spaces specifically in the model benchmarking category.
+    
+    Args:
+        query: The search query to find relevant model benchmarking spaces
+        
+    Returns:
+        A formatted string containing search results with space names, descriptions, and additional information
+    """
+    api_url = "https://huggingface.co/api/spaces"
+    
+    search_words = ["arena", "leaderboard", "benchmark"]
+    results = []
+
+    try:
+        for word in search_words:
+            params = {
+                "search": query + " " + word,
+                "full": True  # Get full information
+            }
+            
+            response = requests.get(api_url, params=params)
+            print(response)
+            
+            spaces = response.json()
+            print(spaces)
+            
+            if not spaces:
+                continue  # Skip if no spaces found for this search word
+            
+            for space in spaces:
+                # Extract relevant information
+                space_id = space.get("id", "Unknown")
+                author = space_id.split("/")[0] if "/" in space_id else "Unknown"
+                space_name = space_id.split("/")[1] if "/" in space_id else space_id
+                likes = space.get("likes", 0)
+                
+                # Try to get detailed information if available
+                title = space.get("cardData", {}).get("title") if space.get("cardData") else space_name
+                description = space.get("cardData", {}).get("short_description", "No description available") if space.get("cardData") else "No description available"
+                
+                # Create formatted result string
+                result = f"🚀 **{title}** ({space_id})\n"
+                result += f"   👤 Author: {author}\n"
+                result += f"   📝 {description}\n"
+                result += f"   ❤️ Likes: {likes}\n"
+                result += f"   🔗 URL: https://huggingface.co/spaces/{space_id}\n"
+                
+                results.append(result)
+        
+        if not results:
+            return f"No model benchmarking spaces found for query: '{query}'"
+        
+        return "\n".join(results)
+    except requests.exceptions.RequestException as e:
+        return f"Error searching Hugging Face Spaces: {str(e)}"
+    except Exception as e:
+        return f"Unexpected error: {str(e)}"
+        
+    except requests.exceptions.RequestException as e:
+        return f"Error searching Hugging Face Spaces: {str(e)}"
+    except Exception as e:
+        return f"Unexpected error: {str(e)}"
+
+@tool
+def get_space_content(space_id: str) -> str:
+    """
+    Get the content of a Hugging Face Space.
+    
+    Args:
+        space_id: The Hugging Face Space ID (e.g., "open-llm-leaderboard/open_llm_leaderboard")
+        
+    Returns:
+        The space content or error message
+    """
+    try:
+        # Get the space's README or main content
+        readme_url = f"https://huggingface.co/spaces/{space_id}/raw/main/README.md"
+        response = requests.get(readme_url)
+        
+        if response.status_code == 200:
+            return f"Content from {space_id}:\n\n{response.text}"
+        else:
+            # Try to get any available file
+            files_url = f"https://huggingface.co/api/spaces/{space_id}/tree/main"
+            files_response = requests.get(files_url)
+            
+            if files_response.status_code == 200:
+                files = files_response.json()
+                return f"Available files in {space_id}:\n" + "\n".join([f"- {file['path']}" for file in files])
+            else:
+                return f"Space {space_id} exists but couldn't retrieve content"
+                
+    except Exception as e:
+        return f"Error accessing space {space_id}: {str(e)}"
+    
+
+@tool
+def get_file_from_space(space_id: str, file_path: str) -> str:
+    """
+    Get a specific file from a Hugging Face Space.
+    
+    Args:
+        space_id: The Hugging Face Space ID
+        file_path: Path to the file in the space
+        
+    Returns:
+        The file content or error message
+    """
+    try:
+        url = f"https://huggingface.co/spaces/{space_id}/raw/main/{file_path}"
+        response = requests.get(url)
+        
+        if response.status_code == 200:
+            return f"Content of {file_path} from {space_id}:\n\n{response.text}"
+        else:
+            return f"Couldn't retrieve {file_path} from {space_id}"
+            
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Initialize the agent with the leaderboard search and space content tools
+model = InferenceClientModel()
+agent = CodeAgent(
+    tools=[leaderboard_search, get_space_content, get_file_from_space],
+    additional_authorized_imports=["json", "requests"],
+    model=model,
+    add_base_tools=False,
+    description="Your job is to find the best possible model for a given task based on relevant leaderboards or arenas. You will be provided with a task description, and you should use the leaderboard tool to find relevant leaderboards or arenas. If you want to inspect the contents of a particular Space (e.g., README or code), use the space_content_tool. Respond with a list of the top models, including their names, scores, and links to their leaderboard pages.",
+)
+
+GradioUI(agent).launch()

requirements.txt

@@ -0,0 +1,2 @@
+smolagents
+huggingface_hub