Created agent.py

agent.py

@@ -0,0 +1,73 @@
+
+
+import os
+import re
+import requests
+from smolagents import CodeAgent, ToolCallingAgent, WebSearchTool, tool
+from smolagents.models.huggingface import HuggingFaceModel
+from dotenv import load_dotenv
+from markdownify import markdownify
+from requests.exceptions import RequestException
+
+def initialize_agent():
+    # Load environment variables from .env file
+    load_dotenv()
+
+    # 1. Load the model
+    # Make sure to set HF_TOKEN in your Hugging Face Space secrets
+    model_name = "HuggingFaceH4/zephyr-7b-beta"
+    try:
+        model = HuggingFaceModel(model=model_name, token=os.environ.get("HF_TOKEN"))
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        model = None
+
+    # 2. Define the tools
+    @tool
+    def visit_webpage(url: str) -> str:
+        """Visits a webpage at the given URL and returns its content as a markdown string.
+
+        Args:
+            url: The URL of the webpage to visit.
+
+        Returns:
+            The content of the webpage converted to Markdown, or an error message if the request fails.
+        """
+        try:
+            # Send a GET request to the URL
+            response = requests.get(url)
+            response.raise_for_status()  # Raise an exception for bad status codes
+
+            # Convert the HTML content to Markdown
+            markdown_content = markdownify(response.text).strip()
+
+            # Remove multiple line breaks
+            markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
+
+            return markdown_content
+
+        except RequestException as e:
+            return f"Error fetching the webpage: {str(e)}"
+        except Exception as e:
+            return f"An unexpected error occurred: {str(e)}"
+
+    # 3. Define the agents
+    if model:
+        web_agent = ToolCallingAgent(
+            tools=[WebSearchTool(), visit_webpage],
+            model=model,
+            max_steps=10,
+            name="web_search_agent",
+            description="Runs web searches for you.",
+        )
+
+        manager_agent = CodeAgent(
+            tools=[],
+            model=model,
+            managed_agents=[web_agent],
+            additional_authorized_imports=["time", "numpy", "pandas"],
+        )
+        return manager_agent
+    else:
+        return None
+

app.py

@@ -1,7 +1,75 @@
 import gradio as gr
+import requests
+from agent import initialize_agent
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 1. Initialize the agent
+manager_agent = initialize_agent()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 2. Define the API URL
+API_URL = "https://gaia-benchmark-dev.hf.space"
+
+# 3. Gradio interface
+def run_evaluation(hf_username, space_url):
+    if not manager_agent:
+        return "Agent could not be initialized. Please check the model and token."
+    if not hf_username or not space_url:
+        return "Please provide your Hugging Face username and Space URL."
+
+    try:
+        # Get questions
+        print("Fetching questions...")
+        response = requests.get(f"{API_URL}/questions")
+        response.raise_for_status()
+        questions = response.json()
+        print(f"Found {len(questions)} questions.")
+
+        # Run agent on questions
+        answers = []
+        for i, question in enumerate(questions):
+            prompt = question["question"]
+            print(f"Answering question {i+1}/{len(questions)}: {prompt}")
+            try:
+                answer = manager_agent.run(prompt)
+                answers.append({"question_id": question["id"], "answer": answer})
+                print(f"Got answer: {answer}")
+            except Exception as e:
+                print(f"Error running agent on question {question['id']}: {e}")
+                answers.append({"question_id": question["id"], "answer": "Error: Could not generate an answer."})
+
+        # Submit answers
+        print("Submitting answers...")
+        submission_response = requests.post(
+            f"{API_URL}/submit",
+            json={
+                "hf_username": hf_username,
+                "answers": answers,
+                "space_url": space_url
+            }
+        )
+        submission_response.raise_for_status()
+        print("Submission successful!")
+        return submission_response.json()
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return f"An error occurred: {e}"
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return f"An unexpected error occurred: {e}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# GAIA Benchmark Evaluation with smolagent")
+    gr.Markdown("Enter your Hugging Face username and the URL of your Space to submit your agent's answers to the GAIA benchmark.")
+    with gr.Row():
+        hf_username_input = gr.Textbox(label="Hugging Face Username")
+        space_url_input = gr.Textbox(label="Hugging Face Space URL")
+    run_button = gr.Button("Run Evaluation")
+    output = gr.JSON()
+
+    run_button.click(
+        run_evaluation,
+        inputs=[hf_username_input, space_url_input],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()