Update app.py

app.py

@@ -1,211 +1,49 @@
 import os
-import re
-import pathlib
-import tempfile
-from pathlib import Path
-from typing import Union, Optional
-
-import openai
-from openai import OpenAI
-import requests
 import gradio as gr
+import requests
+import inspect
 import pandas as pd
-from tabulate import tabulate
-from smolagents import OpenAIServerModel,DuckDuckGoSearchTool,CodeAgent, WikipediaSearchTool
-from smolagents.tools import PipelineTool, Tool
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
-client = OpenAI(api_key = os.environ.get("OPENAI_API_KEY"))
-# (Keep Constants as is)
+# (Keep Constants and BasicAgent class as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-
-class SpeechToTextTool(PipelineTool):
-    """
-    Transcribes an audio file to text using the OpenAI Whisper API.
-    Only local file paths are supported.
-    """
-
-    default_checkpoint = "openai/whisper-1"  # purely informational here
-    description = (
-        "This tool sends an audio file to OpenAI Whisper and returns the "
-        "transcribed text."
-    )
-    name = "transcriber"
-    inputs = {
-        "audio": {
-            "type": "string",
-            "description": "Absolute or relative path to a local audio file.",
-        }
-    }
-    output_type = "string"
-
-    # ──────────────────────────────────────────────────────────────────────────
-    # Public interface
-    # ──────────────────────────────────────────────────────────────────────────
-    def __call__(self, audio: str) -> str:
-        """
-        Convenience wrapper so the tool can be used like a regular function:
-            text = SpeechToTextTool()(path_to_audio)
-        """
-        return self._transcribe(audio)
-
-    # ──────────────────────────────────────────────────────────────────────────
-    # Internal helpers
-    # ──────────────────────────────────────────────────────────────────────────
-    @staticmethod
-    def _transcribe(audio_path: str) -> str:
-        # ----- validation ----------------------------------------------------
-        if not isinstance(audio_path, str):
-            raise TypeError(
-                "Parameter 'audio' must be a string containing the file path."
-            )
-        path = Path(audio_path).expanduser().resolve()
-        if not path.is_file():
-            raise FileNotFoundError(f"No such audio file: {path}")
-
-        # ----- API call ------------------------------------------------------
-        with path.open("rb") as fp:
-            response = openai.audio.transcriptions.create(
-                file=fp,
-                model="whisper-1",  # currently the only Whisper model
-                response_format="text",  # returns plain text instead of JSON
-            )
-
-        # For response_format="text", `response` is already the raw transcript
-        return response
-
-
-class ExcelToTextTool(Tool):
-    """Render an Excel worksheet as Markdown text."""
-
-    # ------------------------------------------------------------------
-    # Required smol‑agents metadata
-    # ------------------------------------------------------------------
-    name = "excel_to_text"
-    description = (
-        "Read an Excel file and return a Markdown table of the requested sheet. "
-        "Accepts either the sheet name or the zero-based index."
-    )
-
-    inputs = {
-        "excel_path": {
-            "type": "string",
-            "description": "Path to the Excel file (.xlsx / .xls).",
-        },
-        "sheet_name": {
-            "type": "string",
-            "description": (
-                "Worksheet name or zero-based index *as a string* (optional; default first sheet)."
-            ),
-            "nullable": True,
-        },
-    }
-
-    output_type = "string"
-
-    def forward(
-        self,
-        excel_path: str,
-        sheet_name: Optional[str] = None,
-    ) -> str:
-        """Load *excel_path* and return the sheet as a Markdown table."""
-
-        path = pathlib.Path(excel_path).expanduser().resolve()
-        if not path.exists():
-            return f"Error: Excel file not found at {path}"
-
-        try:
-            # Interpret sheet identifier -----------------------------------
-            sheet: Union[str, int]
-            if sheet_name is None or sheet_name == "":
-                sheet = 0  # first sheet
-            else:
-                # If the user passed a numeric string (e.g. "1"), cast to int
-                sheet = int(sheet_name) if sheet_name.isdigit() else sheet_name
-
-            # Load worksheet ----------------------------------------------
-            df = pd.read_excel(path, sheet_name=sheet)
-
-            # Render to Markdown; fall back to tabulate if needed ---------
-            if hasattr(pd.DataFrame, "to_markdown"):
-                return df.to_markdown(index=False)
-
-            return tabulate(df, headers="keys", tablefmt="github", showindex=False)
-
-        except Exception as exc:  # pylint: disable=broad-except
-            return f"Error reading Excel file: {exc}"
-
-
-def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
-    """
-    Try GET /files/{task_id}.
-    • On HTTP 200 → save to a temp dir and return local path.
-    • On 404 → return None.
-    • On other errors → raise so caller can log / handle.
-    """
-    url = f"{base_api_url}/files/{task_id}"
-    try:
-        resp = requests.get(url, timeout=30)
-        if resp.status_code == 404:
-            return None  # no file
-        resp.raise_for_status()  # raise on 4xx/5xx ≠ 404
-    except requests.exceptions.HTTPError as e:
-        # propagate non-404 errors (403, 500, …)
-        raise e
-
-    # ▸ Save bytes to a named file inside the system temp dir
-    #    Try to keep original extension from Content-Disposition if present.
-    cdisp = resp.headers.get("content-disposition", "")
-    filename = task_id  # default base name
-    if "filename=" in cdisp:
-        m = re.search(r'filename="([^"]+)"', cdisp)
-        if m:
-            filename = m.group(1)  # keep provided name
-
-    tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
-    tmp_dir.mkdir(exist_ok=True)
-    file_path = tmp_dir / filename
-    with open(file_path, "wb") as f:
-        f.write(resp.content)
-    return str(file_path)
-
-
+# --- Basic Agent Definition ---
 class BasicAgent:
-    """Basic Agent for the evaluation task."""
-
     def __init__(self):
-        self.agent = CodeAgent(
-            model=OpenAIServerModel(model_id="gpt-4o"),
-            tools=[
-                DuckDuckGoSearchTool(),
-                WikipediaSearchTool(),
-                SpeechToTextTool(),
-                ExcelToTextTool(),
-            ],
-            add_base_tools=True,
-            additional_authorized_imports=["pandas", "numpy", "csv", "subprocess"],
-        )
-
         print("BasicAgent initialized.")
-
+        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
+
+        SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and
+        finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
+        list of numbers and/or strings.
+        If you are asked for a number, don't use comma to write your number neither use units such as $ or
+        percent sign unless specified otherwise.
+        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the
+        digits in plain text unless specified otherwise.
+        If you are asked for a comma separated list, apply the above rules depending of whether the element
+        to be put in the list is a number or a string.
+        """
+        self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + SYSTEM_PROMPT
+        
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = self.agent.run(question)
-        print(f"Agent returning answer: {fixed_answer}")
-        return fixed_answer
-
+        final_answer = self.agent.run(question)
+        print(f"Agent returning final answer: {final_answer}")
+        return final_answer
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+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")
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username = f"{profile.username}"
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -215,9 +53,10 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     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:  # pylint: disable=broad-except
+    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)
@@ -231,17 +70,17 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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("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.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 requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-    except Exception as e:  # pylint: disable=broad-except
+    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
 
@@ -252,62 +91,23 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
-
-        # ----------fetch any attached file ----------
-        try:
-            file_path = download_file_if_any(api_url, task_id)
-        except Exception as e:  # pylint: disable=broad-except
-            file_path = None
-            print(f"[file fetch error] {task_id}: {e}")
-
-        # ---------- Build the prompt sent to the agent ----------
-        if file_path:
-            q_for_agent = (
-                f"{question_text}\n\n"
-                f"---\n"
-                f"A file was downloaded for this task and saved locally at:\n"
-                f"{file_path}\n"
-                f"---\n\n"
-            )
-        else:
-            q_for_agent = question_text
-
         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(q_for_agent)
-            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:  # pylint: disable=broad-except
-            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 = 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,
-    }
-
+    # 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)
 
@@ -348,7 +148,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print(status_message)
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
-    except Exception as e:  # pylint: disable=broad-except
+    except Exception as e:
         status_message = f"An unexpected error occurred during submission: {e}"
         print(status_message)
         results_df = pd.DataFrame(results_log)
@@ -359,35 +159,29 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 with gr.Blocks() as demo:
     gr.Markdown("# Basic 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.
-        """
+        "Please clone this space, then modify the code to define your agent's logic within the `BasicAgent` class. "
+        "Log in to your Hugging Face account using the button below. This uses your HF username for submission. "
+        "Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, 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
-    )
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    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
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -395,18 +189,16 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup:  # Print repo URLs if SPACE_ID is found
+    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"
-        )
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
-        print(
-            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
-        )
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-    print("-" * (60 + len(" App Starting ")) + "\n")
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)
+
+