Create app.py

app.py

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+import os
+import gradio as gr
+import requests
+import pandas as pd
+import traceback
+import time 
+
+# Import smol-agent and tool components
+from smolagents import CodeAgent, LiteLLMModel, tool
+from smolagents import DuckDuckGoSearchTool
+from unstructured.partition.auto import partition
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Tool Definition ---
+@tool
+def file_reader(file_path: str) -> str:
+    """Reads the content of a file and returns its text content.
+
+    This tool supports various file types like PDF, TXT, CSV, etc., from either
+    a local path or a web URL.
+
+    Args:
+        file_path (str): The local path or web URL of the file to be read.
+    """
+    try:
+        if file_path.startswith("http://") or file_path.startswith("https://"):
+            response = requests.get(file_path, timeout=20)
+            response.raise_for_status()
+            with open("temp_file", "wb") as f:
+                f.write(response.content)
+            elements = partition("temp_file")
+            os.remove("temp_file") # Clean up
+        else:
+            elements = partition(file_path)
+        return "\n\n".join([str(el) for el in elements])
+    except Exception as e:
+        return f"Error reading or processing file '{file_path}': {e}"
+
+# --- Agent Class (Now using a free Open-Source LLM) ---
+class GaiaSmolAgent:
+    def __init__(self):
+        #print("Initializing GaiaSmolAgent with a free Open-Source LLM via Groq...")
+        api_key = os.getenv("GEMINI_API_KEY")
+        if not api_key:
+            raise ValueError("API key 'GEMINI_API_KEY' not found in environment secrets.")
+
+        #model = InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together")
+
+
+        self.planner_model = LiteLLMModel(
+            #model_id="groq/llama3-8b-8192",
+            model_id="gemini/gemini-1.5-pro-latest", 
+            api_key=api_key,
+            temperature=0.0,
+        )
+
+        # Initialize the agent with the tools it can use.
+        self.executor_agent = CodeAgent(
+            model=self.planner_model,
+            tools=[file_reader, DuckDuckGoSearchTool()],
+            add_base_tools=True, # Provides a python interpreter
+        )
+        print("GaiaSmolAgent initialized successfully.")
+
+    def _generate_script(self, question: str) -> str:
+        """Generates a self-contained Python script to answer the question."""
+        print(f"Generating script for question: {question[:100]}...")
+        
+        prompt = f"""
+        You are an expert Python programmer. Your task is to write a single, self-contained Python script to answer the user's question.
+
+        You have access to the following functions which are pre-imported and ready to use:
+        - `duck_duck_go_search(query: str) -> str`: Searches the web and returns a string with the results.
+        - `file_reader(file_path: str) -> str`: Reads a file and returns its contents as a string.
+
+        CRITICAL INSTRUCTIONS:
+        1.  Your output must be ONLY the Python code for the script. Do not add any explanation or markdown formatting like ```python.
+        2.  The script MUST end with a call to a function `final_answer(answer: str)`.
+        3.  The `answer` passed to `final_answer` must be a single, concise string.
+        4.  All logic, including processing the string outputs from the tools, must be included in this single script. State is preserved within the script.
+
+        Question: "{question}"
+
+        Example for "What is the capital of France?":
+        search_result = duck_duck_go_search("capital of France")
+        # In a real scenario, you would parse this string to find the answer.
+        # For this example, we'll just summarize the string.
+        answer = "Based on the search, the capital is likely Paris." # Replace with actual logic
+        final_answer(answer)
+        
+        Now, write the Python script to answer the user's question.
+        """
+        messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
+        response_object = self.planner_model.generate(messages)
+        
+        # --- THIS IS THE FIX ---
+        # The response is an object, not a string. We need to access its .content attribute.
+        response_content = response_object.content
+        
+        if "```python" in response_content:
+            response_content = response_content.split("```python")[1].split("```")[0].strip()
+        
+        print(f"--- Generated Script ---\n{response_content}\n------------------------")
+        return response_content
+
+    def __call__(self, question: str) -> str:
+        """Generates and executes a single script to answer the question."""
+        print(f"Agent received question: {question[:100]}...")
+        
+        try:
+            script_to_execute = self._generate_script(question)
+            final_answer = self.executor_agent.run(script_to_execute)
+
+        except Exception as e:
+            print(f"FATAL AGENT ERROR: An exception occurred during agent execution: {e}")
+            print(traceback.format_exc()) # Print the full traceback for debugging
+            return f"FATAL AGENT ERROR: {e}"
+
+        print(f"Agent returning final answer: {final_answer}")
+        return str(final_answer)
+
+# --- Main Application Logic (Unchanged) ---
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    if profile:
+        username= f"{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 ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(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 requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for 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 your 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 requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        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
+
+
+# --- Gradio Interface (Updated Instructions) ---
+with gr.Blocks() as demo:
+    gr.Markdown("# GAIA Agent Evaluation Runner (smol-agent)")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1.  Ensure you have added your **GEMINI API key** (as `GEMINI_API_KEY`) in the Space's secrets.
+        2.  Log in to your Hugging Face account using the button below.
+        3.  Click 'Run Evaluation & Submit All Answers' to run your agent and see the score.
+        """
+    )
+    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]
+    )
+
+if __name__ == "__main__":
+    print("Launching Gradio Interface for GAIA Agent Evaluation...")
+    demo.launch(debug=True, share=False)