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 mimetypes
+from tempfile import NamedTemporaryFile
+
+# Import smol-agent and tool components
+from smolagents import CodeAgent, LiteLLMModel, tool
+from smolagents import DuckDuckGoSearchTool
+from unstructured.partition.auto import partition
+
+# Imports for advanced file processing
+import speech_recognition as sr
+from pydub import AudioSegment
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Tool Definition (Upgraded for Full Multimodality with pydub) ---
+@tool
+def file_reader(file_path: str) -> str:
+    """
+    Reads and analyzes the content of a file and returns relevant text-based information.
+    Supports:
+      - Text files (PDF, TXT, CSV)
+      - Images (PNG, JPG) with OCR
+      - Audio (MP3, WAV) via speech recognition
+      - Video (MP4, MOV) via speech recognition on audio track
+    Can be used with a local file path or a web URL.
+
+    Args:
+        file_path (str): The local path or web URL of the file to be read.
+    Returns:
+        str: Extracted or transcribed content as text.
+    """
+    temp_file_path = None
+    audio_temp_path = None
+    try:
+        # Download the file if it's a URL
+        if file_path.startswith("http://") or file_path.startswith("https://"):
+            temp_file_path = NamedTemporaryFile(delete=False).name
+            response = requests.get(file_path, timeout=20)
+            response.raise_for_status()
+            with open(temp_file_path, "wb") as f:
+                f.write(response.content)
+            local_path = temp_file_path
+        else:
+            local_path = file_path
+
+        mime_type, _ = mimetypes.guess_type(local_path)
+        recognizer = sr.Recognizer()
+
+        if mime_type:
+            # Handle audio files
+            if mime_type.startswith("audio/"):
+                with sr.AudioFile(local_path) as source:
+                    audio = recognizer.record(source)
+                    return recognizer.recognize_whisper(audio)
+
+            # Handle video files by extracting audio with pydub
+            elif mime_type.startswith("video/"):
+                with NamedTemporaryFile(suffix=".wav", delete=False) as audio_temp:
+                    audio_temp_path = audio_temp.name
+                
+                # Extract audio using pydub
+                video_audio = AudioSegment.from_file(local_path, format=mime_type.split('/')[1])
+                video_audio.export(audio_temp_path, format="wav")
+                
+                with sr.AudioFile(audio_temp_path) as source:
+                    audio = recognizer.record(source)
+                
+                return recognizer.recognize_whisper(audio)
+
+        # Default to handling text and images with OCR if not audio/video
+        elements = partition(local_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}"
+    finally:
+        # Clean up the downloaded file if it exists
+        if temp_file_path and os.path.exists(temp_file_path):
+            os.remove(temp_file_path)
+        # Clean up the temporary audio file
+        if audio_temp_path and os.path.exists(audio_temp_path):
+            os.remove(audio_temp_path)
+
+
+
+# --- Agent Class (Updated with More Powerful Model and Tools) ---
+class GaiaSmolAgent:
+    def __init__(self):
+        """
+        Initializes the optimized agent.
+        Now uses a more powerful model and the agent's native conversation memory.
+        """
+        print("Initializing Optimized GaiaSmolAgent...")
+        api_key = os.getenv("GEMINI_API_KEY")
+        if not api_key:
+            raise ValueError("API key 'GEMINI_API_KEY' not found in environment secrets.")
+        
+        # Use a more powerful, "clever" model for better reasoning.
+        model = LiteLLMModel(
+            model_id="gemini/gemini-1.5-pro-latest",
+            api_key=api_key,
+            temperature=0.0,
+            timeout=120.0, # Add a timeout to prevent hanging
+        )
+
+        # --- CHANGE 1: ENHANCED SYSTEM PROMPT ---
+        # A more detailed prompt that guides the agent on how to handle GAIA-specific challenges,
+        # such as precise data extraction, calculations, and structured reasoning.
+        self.system_prompt = """
+        You are an expert-level research assistant AI, specifically designed to solve challenging questions from the GAIA benchmark. Your goal is to provide a precise and accurate final answer by meticulously following a step-by-step plan.
+
+        **Available Tools:**
+        - `duck_duck_go_search(query: str) -> str`: Use this for web searches to find information, URLs, facts, etc.
+        - `file_reader(file_path: str) -> str`: Use this to read content from local files or web URLs. It handles text, PDFs, images (OCR), audio, and video.
+
+        **Your Thought Process & Execution Strategy:**
+        1.  **Analyze the Question:** First, break down the user's question to fully understand all its components, constraints, and the exact type of information required for the answer (e.g., a number, a date, a name).
+        2.  **Formulate a Step-by-Step Plan:** Before using any tools, you MUST outline your plan in your thoughts. For example: "Step 1: Search for the document URL. Step 2: Use the file_reader to read the document. Step 3: Extract the specific data point. Step 4: Perform calculation if needed. Step 5: Provide the final answer."
+        3.  **Execute and Verify:** Execute your plan one step at a time. After each tool call, review the output. Verify if the information obtained is sufficient and accurate. If a step fails or the result is not what you expected, REVISE your plan.
+        4.  **Synthesize the Answer:** Once you have gathered and verified all necessary information, formulate the final answer. Use the Python interpreter for any calculations, data sorting, or text processing to ensure accuracy.
+
+        **CRITICAL INSTRUCTIONS:**
+        - **Precision is Key:** Pay close attention to the requested format of the final answer. If a question asks for a number, your final answer must be only that number.
+        - **Code for Calculations:** ALWAYS use the Python interpreter for any calculations, date comparisons, or data manipulation. Do not perform calculations in your head.
+        - **Autonomous Operation:** You must work autonomously. Make the most logical deduction based on the information you gather. Do not ask for clarification.
+        - **Final Answer:** Your final output MUST be a single call to the `final_answer(answer: str)` function with the precise answer.
+        """
+
+        # Initialize the agent with the updated file_reader tool and memory settings.
+        self.agent = CodeAgent(
+            model=model,
+            tools=[file_reader, DuckDuckGoSearchTool()],
+            add_base_tools=True,  # Provides python interpreter and final_answer
+            
+            # --- CHANGE 2: MORE REACTIVE PLANNING ---
+            # By setting planning_interval=1, the agent re-evaluates its plan
+            # after every single tool execution. This allows it to immediately course-correct
+            # based on new information, which is vital for complex, multi-step tasks.
+            planning_interval=1
+        )
+        
+        print("Optimized GaiaSmolAgent initialized successfully with enhanced prompt and reactive planning.")
+
+    def __call__(self, question: str, reset_memory: bool = False) -> str:
+        """
+        Directly runs the agent to generate and execute a plan to answer the question.
+        It leverages the agent's built-in memory, controlled by the `reset` parameter.
+
+        Args:
+            question (str): The user's question.
+            reset_memory (bool): If True, the agent's conversation memory will be cleared
+                                 before running. Maps to the agent's `reset` parameter.
+        """
+        print(f"Optimized Agent received question: {question[:100]}...")
+        
+        try:
+            # Combine the system prompt with the current question. The agent will handle the history.
+            full_prompt = f"{self.system_prompt}\n\nCURRENT TASK:\nUser Question: \"{question}\""
+            
+            # Use the agent's `reset` parameter to control conversation memory.
+            # `reset=False` keeps the memory from previous calls.
+            final_answer = self.agent.run(full_prompt, reset=reset_memory)
+        except Exception as e:
+            print(f"FATAL AGENT ERROR: An exception occurred during agent execution: {e}")
+            print(traceback.format_exc()) # Print full traceback for easier debugging
+            return f"FATAL AGENT ERROR: {e}"
+            
+        print(f"Optimized 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 = GaiaSmolAgent()
+    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)