Add application file

app.py

@@ -1,11 +1,197 @@
+import os
 import gradio as gr
-from smolagents import CodeAgent, LiteLLMModel
+import requests
+import pandas as pd
+from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel
+from huggingface_hub import login
 
-model = LiteLLMModel(model_id="ollama/gemma3:12b", api_base="http://localhost:11434")
-agent = CodeAgent(tools=[], model=model, add_base_tools=True)
+# Constants
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+HF_TOKEN = os.environ.get("HF_TOKEN", "")  # Hugging Face token'ı buraya veya environment variable olarak ekle")  # Hugging Face token
 
-def run_agent(prompt):
-    return agent.run(prompt)
+# Login if token is provided
+if HF_TOKEN:
+    login(token=HF_TOKEN)
 
-iface = gr.Interface(fn=run_agent, inputs="text", outputs="text", title="Code Agent")
-iface.launch()
+class GAIACodeAgent:
+    def __init__(self):
+        """Initialize the advanced agent with tools and capabilities"""
+        model = InferenceClientModel()
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=model
+        )
+    
+    def __call__(self, question: str) -> str:
+        """Process a question and return an answer"""
+        try:
+            print(f"Agent received question: {question[:50]}...")
+            # Improve the prompt to get better accuracy on exact match questions
+            enriched_prompt = (
+                f"Answer the following question accurately and concisely. "
+                f"Provide a straightforward answer without unnecessary elaboration. "
+                f"The answer will be evaluated for exact match accuracy.\n\n"
+                f"Question: {question}\n\n"
+                f"Answer: "
+            )
+            
+            # Run the agent with the enriched prompt
+            response = self.agent.run(enriched_prompt)
+            
+            # Clean up response to improve exact match chances
+            cleaned_response = response.strip()
+            print(f"Agent returning answer: {cleaned_response[:50]}...")
+            return cleaned_response
+        except Exception as e:
+            error_msg = f"Error: {str(e)}"
+            print(error_msg)
+            return error_msg
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the GAIACodeAgent on them, submits all answers,
+    and displays the results.
+    """
+    # Determine HF Space Runtime URL and Repo URL
+    space_id = os.getenv("SPACE_ID")
+
+    if profile:
+        username = profile.username
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent
+    try:
+        agent = GAIACodeAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"Agent code URL: {agent_code}")
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+def query_single_agent(question):
+    """Run agent on a single question for testing"""
+    try:
+        agent = GAIACodeAgent()
+        response = agent(question)
+        return response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Build Gradio Interface
+with gr.Blocks(title="GAIA Code Agent Evaluation") as demo:
+    gr.Markdown("# GAIA Code Agent Evaluation")
+    gr.Markdown(
+        """
+        This application helps you evaluate a code agent on the GAIA benchmark.
+        
+        ## Instructions:
+        1. Log in to your Hugging Face account using the button below
+        2. You can test the agent with a single question in the "Test Agent" tab
+        3. Use the "Run Evaluation" tab to run the agent on all GAIA questions and submit answers
+        """
+    )
+    
+    with gr.Tab("Test Agent"):
+        question_input = gr.Textbox(
+            label="Enter a question", 
+            placeholder="How many seconds would it take for a leopard at full speed to run through Pont des Arts?"
+        )
+        query_button = gr.Button("Get Answer")
+        response_output = gr.Textbox(label="Agent Response", lines=10)
+        query_button.click(query_single_agent, inputs=question_input, outputs=response_output)
+    
+    with gr.Tab("Run Evaluation"):
+        gr.LoginButton()
+        run_button = gr.Button("Run Evaluation & Submit All Answers")
+        status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+        results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+        run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+
+# Start the app
+if __name__ == "__main__":
+    demo.launch()