Update app.py

app.py

@@ -1,169 +1,341 @@
 import os
+import re
+import json
+import tempfile
+from pathlib import Path
+
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
+from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool, tool
+from smolagents.models import InferenceClientModel
+
+
+# ============================================================
+# Constants
+# ============================================================
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+# ============================================================
+# Helper tools
+# ============================================================
+
+@tool
+def download_task_file(task_id: str) -> str:
+    """
+    Download the file attached to a GAIA task and return the local file path.
+    Use this when the question references an attached file/document/image/data file.
+    Args:
+        task_id: The task id of the GAIA question.
+    Returns:
+        Local file path of the downloaded file, or a message if no file is available.
+    """
+    api_url = os.getenv("SCORING_API_URL", DEFAULT_API_URL)
+    file_url = f"{api_url}/files/{task_id}"
+
+    try:
+        response = requests.get(file_url, timeout=60)
+        if response.status_code != 200:
+            return f"No downloadable file found for task {task_id}. HTTP {response.status_code}"
+
+        content_type = response.headers.get("content-type", "").lower()
+
+        # Try to infer extension
+        ext = ""
+        if "pdf" in content_type:
+            ext = ".pdf"
+        elif "json" in content_type:
+            ext = ".json"
+        elif "csv" in content_type:
+            ext = ".csv"
+        elif "text" in content_type:
+            ext = ".txt"
+        elif "html" in content_type:
+            ext = ".html"
+        elif "png" in content_type:
+            ext = ".png"
+        elif "jpeg" in content_type or "jpg" in content_type:
+            ext = ".jpg"
+        elif "excel" in content_type or "spreadsheet" in content_type:
+            ext = ".xlsx"
+
+        tmp_dir = tempfile.mkdtemp(prefix="gaia_task_")
+        file_path = os.path.join(tmp_dir, f"{task_id}{ext}")
+
+        with open(file_path, "wb") as f:
+            f.write(response.content)
+
+        return file_path
+    except Exception as e:
+        return f"Error downloading file for task {task_id}: {e}"
+
+
+@tool
+def read_local_text_file(file_path: str) -> str:
+    """
+    Read a local text-like file and return its contents.
+    Use this only for local TXT/JSON/CSV/HTML-like files after downloading them.
+    Args:
+        file_path: Path to a local file.
+    Returns:
+        File contents as text.
+    """
+    try:
+        path = Path(file_path)
+        if not path.exists():
+            return f"File not found: {file_path}"
+
+        # Try UTF-8 first, then fallback
+        try:
+            return path.read_text(encoding="utf-8")
+        except Exception:
+            return path.read_text(errors="ignore")
+    except Exception as e:
+        return f"Error reading file {file_path}: {e}"
+
+
+# ============================================================
+# Agent
+# ============================================================
+
+SYSTEM_PROMPT = """
+You are solving a GAIA benchmark question.
+
+Rules:
+1. Think carefully and use tools when needed.
+2. If the question mentions an attached file, download it using the download_task_file tool.
+3. If a downloaded file is text/csv/json/html-like, inspect it with read_local_text_file.
+4. If web information is needed, use the search/browser tools.
+5. Return ONLY the final answer.
+6. Do NOT return explanations.
+7. Do NOT return the words "FINAL ANSWER".
+8. Do NOT add markdown, bullet points, or surrounding quotes unless the answer itself requires quotes.
+9. Keep the answer as short and exact as possible.
+"""
+
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+        # You can change the model if needed, but this works well on HF Spaces
+        # and avoids the old HfApiModel import issue.
+        model_id = os.getenv("MODEL_ID", "Qwen/Qwen2.5-72B-Instruct")
+
+        self.model = InferenceClientModel(
+            model_id=model_id,
+            token=os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN"),
+        )
+
+        self.agent = CodeAgent(
+            tools=[
+                DuckDuckGoSearchTool(),
+                VisitWebpageTool(),
+                download_task_file,
+                read_local_text_file,
+            ],
+            model=self.model,
+            additional_authorized_imports=[
+                "json",
+                "re",
+                "math",
+                "statistics",
+                "csv",
+                "pandas",
+                "pathlib",
+            ],
+            max_steps=12,
+            verbosity_level=1,
+        )
+
+        print(f"BasicAgent initialized with model: {model_id}")
+
+    def clean_final_answer(self, answer: str) -> str:
+        """
+        Clean the model output for exact-match scoring.
+        """
+        if answer is None:
+            return ""
+
+        answer = str(answer).strip()
+
+        # Remove common prefixes the model may add
+        answer = re.sub(r"^\s*FINAL ANSWER\s*[:\-]?\s*", "", answer, flags=re.IGNORECASE)
+        answer = re.sub(r"^\s*Answer\s*[:\-]?\s*", "", answer, flags=re.IGNORECASE)
+        answer = re.sub(r"^\s*The answer is\s*", "", answer, flags=re.IGNORECASE)
+
+        # Remove enclosing markdown/code fences if any
+        answer = answer.strip().strip("`").strip()
+
+        # If it returns quoted answer like "Paris", remove only outer quotes
+        if len(answer) >= 2 and (
+            (answer.startswith('"') and answer.endswith('"')) or
+            (answer.startswith("'") and answer.endswith("'"))
+        ):
+            answer = answer[1:-1].strip()
+
+        return answer.strip()
+
+    def __call__(self, question: str, task_id: str | None = None) -> str:
+        """
+        Run the agent on a question and return a clean final answer.
+        """
+        prompt = f"{SYSTEM_PROMPT}\n\nTask ID: {task_id}\nQuestion:\n{question}\n"
+        print(f"Running agent for task_id={task_id}")
+
+        try:
+            result = self.agent.run(prompt)
+            cleaned = self.clean_final_answer(result)
+            print(f"Agent raw result: {result}")
+            print(f"Agent cleaned result: {cleaned}")
+            return cleaned
+        except Exception as e:
+            print(f"Agent failed on task {task_id}: {e}")
+            return f"ERROR: {e}"
+
+
+# ============================================================
+# Main runner
+# ============================================================
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetch all questions, run the agent, submit answers, and display 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
+    space_id = os.getenv("SPACE_ID")
+    api_url = os.getenv("SCORING_API_URL", DEFAULT_API_URL)
 
     if profile:
-        username= f"{profile.username}"
+        username = profile.username.strip()
         print(f"User logged in: {username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        return "Please login to Hugging Face first.", None
+
+    if not space_id:
+        # Fallback so submission still works locally if needed
+        agent_code = "LOCAL_RUN_NO_SPACE_ID"
+        print("SPACE_ID not found. Using LOCAL_RUN_NO_SPACE_ID")
+    else:
+        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
-    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)
+    # 1) Build 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}")
+    # 2) Fetch questions
+    print(f"Fetching questions from {questions_url}")
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=60)
         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
+
+        if not isinstance(questions_data, list) or len(questions_data) == 0:
+            return "Questions endpoint returned empty/invalid data.", 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
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
-    results_log = []
+    # 3) Solve questions
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    results_log = []
+
     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}")
+        question_text = item.get("question", "")
+
+        if not task_id or not question_text:
+            results_log.append({
+                "Task ID": item.get("task_id", "UNKNOWN"),
+                "Question": item.get("question", ""),
+                "Submitted Answer": "SKIPPED: Missing task_id or question"
+            })
             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})
+            submitted_answer = agent(question_text, task_id=task_id)
         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}"})
+            submitted_answer = f"ERROR: {e}"
+
+        answers_payload.append({
+            "task_id": task_id,
+            "submitted_answer": str(submitted_answer).strip()
+        })
+
+        results_log.append({
+            "Task ID": task_id,
+            "Question": question_text,
+            "Submitted Answer": submitted_answer
+        })
 
     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)
+        return "No answers were generated.", pd.DataFrame(results_log)
+
+    # 4) Submit
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
 
-    # 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)
+    print("Submitting payload...")
+    print(json.dumps({
+        "username": username,
+        "agent_code": agent_code,
+        "answers_count": len(answers_payload)
+    }, indent=2))
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(submit_url, json=submission_data, timeout=180)
         response.raise_for_status()
         result_data = response.json()
+
         final_status = (
             f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
+            f"User: {result_data.get('username', 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
+
+        return final_status, pd.DataFrame(results_log)
+
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
+        detail = f"HTTP {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
+            detail_json = e.response.json()
+            detail += f" | {detail_json}"
+        except Exception:
+            detail += f" | {e.response.text[:1000]}"
+        return f"Submission failed: {detail}", pd.DataFrame(results_log)
+
     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
+        return f"Submission failed: {e}", pd.DataFrame(results_log)
+
 
+# ============================================================
+# Gradio UI
+# ============================================================
 
-# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# GAIA Unit 4 Agent Evaluation Runner")
     gr.Markdown(
         """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        **Instructions**
+        1. Login with your Hugging Face account.
+        2. Click **Run Evaluation & Submit All Answers**.
+        3. The app will fetch questions, run the agent, and submit the answers.
         """
     )
 
     gr.LoginButton()
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -172,25 +344,21 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    space_host = os.getenv("SPACE_HOST")
+    space_id = os.getenv("SPACE_ID")
+
+    if space_host:
+        print(f"SPACE_HOST: {space_host}")
     else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+        print("SPACE_HOST not found.")
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    if space_id:
+        print(f"SPACE_ID: {space_id}")
+        print(f"Repo Tree URL: https://huggingface.co/spaces/{space_id}/tree/main")
+    else:
+        print("SPACE_ID not found.")
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("Launching app...")
+    demo.launch(debug=True)