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.gitignore

@@ -0,0 +1,41 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+.env
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+*.log
+logs/
+
+# Local development
+.DS_Store
+Thumbs.db 

README.md

@@ -1,13 +1,57 @@
----
-title: WebSearch
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+---
+title: Web Search Tool
+emoji: 🔍
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+---
+
+# Web Search Tool
+
+This Hugging Face Space provides an interactive web interface for searching information from the web using the smolagents library.
+
+## Features
+
+- **Web Search**: Searches the web for information using DuckDuckGo
+- **Interactive Interface**: Easy-to-use web interface powered by Gradio
+- **Simple Design**: Straightforward search and results display
+
+## Usage
+
+1. Enter your query in the text box
+2. Click the "Submit" button
+3. Get information from the web
+
+## Example Queries
+
+- "Latest developments in quantum computing"
+- "Current trends in artificial intelligence"
+- "Recent breakthroughs in renewable energy"
+
+## Technical Details
+
+Built with:
+- Python 3.9+
+- Gradio for the web interface
+- smolagents for web searching
+
+## Local Development
+
+To run this project locally:
+
+1. Clone the repository
+2. Install dependencies: `pip install -r requirements.txt`
+3. Run the application: `python app.py`
+
+## License
+
+This project is open-source and available under the MIT License.
+
+## Acknowledgments
+
+- Built using Hugging Face Spaces
+- Powered by smolagents library
+- Interface created with Gradio 

app.py

@@ -1,111 +1,54 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, VisitWebpageTool, HfApiModel
-
-# Initialize HuggingFace client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Create the smolagents agent (without UserInputTool since we'll get input from Gradio)
-agent = CodeAgent(
-    tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), FinalAnswerTool()],
-    model=HfApiModel(),
-    max_steps=8,
-    verbosity_level=1
-)
-
-# Function to perform web research with a provided query
-def research_with_query(query):
-    result = agent.run(f"""
-    Think step by step:
-
-    1. The user has asked about: "{query}"
-    2. Use the DuckDuckGoSearchTool to search the web for information about this query.
-    3. From the search results, identify 1-2 relevant webpage URLs that might contain detailed information.
-    4. Use the VisitWebpageTool to visit each identified webpage and extract its content.
-    5. Combine the information from the search results and webpage visits.
-    6. Create a comprehensive bullet point summary of all collected information.
-    7. Each bullet point should start with "• " and be on a new line.
-    8. Use the FinalAnswerTool to present your bullet-point summary as the final answer.
-
-    Make sure your bullet points are clear, well-organized, and directly relevant to the user's query.
-    Include the most important and factual information from your research.
-    """)
-    return result
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    use_web_search,
-):
-    # Check if web search is enabled and message starts with a research request
-    if use_web_search and message.strip().lower().startswith(("search:", "research:", "find info:")):
-        query = message.split(":", 1)[1].strip()
-        
-        yield "Searching the web for information about your query. This may take a moment..."
-        
-        try:
-            # Perform the web search and get bullet point summary
-            research_results = research_with_query(query)
-            
-            # Return the research results
-            yield f"Here's what I found about '{query}':\n\n{research_results}"
-        except Exception as e:
-            yield f"Sorry, I encountered an error while searching the web: {str(e)}"
-    else:
-        # Regular chat completion for normal messages
-        messages = [{"role": "system", "content": system_message}]
-
-        for val in history:
-            if val[0]:
-                messages.append({"role": "user", "content": val[0]})
-            if val[1]:
-                messages.append({"role": "assistant", "content": val[1]})
-
-        messages.append({"role": "user", "content": message})
-
-        response = ""
-
-        for message in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            token = message.choices[0].delta.content
-            response += token
-            yield response
-
-# Create the Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(
-            value="You are a helpful assistant. When users ask you to search for information with 'search:', 'research:', or 'find info:', you will search the web for them.", 
-            label="System message"
-        ),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-        gr.Checkbox(value=True, label="Enable web search (use 'search:', 'research:', or 'find info:' to search)")
-    ],
-    examples=[
-        ["search: latest developments in quantum computing"],
-        ["research: climate change impacts in 2023"],
-        ["find info: benefits of meditation"],
-        ["Hello! How are you today?"]
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, HfApiModel, Tool, tool, VisitWebpageTool
+
+@tool
+def search_web(query: str) -> str:
+    """
+    Searches the web for information.
+    
+    Args:
+        query: The search query string
+        
+    Returns:
+        str: Search results summary
+    """
+    search_tool = DuckDuckGoSearchTool()
+    return search_tool.forward(query)
+
+# Create the agent
+agent = CodeAgent(
+    tools=[
+        DuckDuckGoSearchTool(),
+        VisitWebpageTool(),
+        search_web
+    ],
+    model=HfApiModel(),
+    max_steps=5,
+    verbosity_level=2
+)
+
+def process_query(query):
+    """Process the user query using the agent."""
+    try:
+        result = agent.run(query)
+        return result
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_query,
+    inputs=gr.Textbox(label="Enter your query", placeholder="What would you like to know about?"),
+    outputs=gr.Textbox(label="Results"),
+    title="Web Search Tool",
+    description="Enter any topic to get information from the web.",
+    examples=[
+        ["Latest developments in quantum computing"],
+        ["Current trends in artificial intelligence"],
+        ["Recent breakthroughs in renewable energy"]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch() 

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.25.2
+gradio>=4.0.0
+smolagents>=0.1.0