Update app.py

app.py

@@ -1,34 +1,217 @@
+"""app.py"""
+
 import os
-import gradio as gr
+import re
+import pathlib
+import tempfile
+from pathlib import Path
+from typing import Union, Optional
+
+import openai
 import requests
-import inspect
+import gradio as gr
 import pandas as pd
+from tabulate import tabulate
+from smolagents import (
+    OpenAIServerModel,
+    DuckDuckGoSearchTool,
+    CodeAgent,
+    WikipediaSearchTool,
+)
+from smolagents.tools import PipelineTool, Tool
+
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+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)
+
+
 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.")
+
     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}")
+        fixed_answer = self.agent.run(question)
+        print(f"Agent returning answer: {fixed_answer}")
         return fixed_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") # Get the SPACE_ID for sending link to the code
+    space_id = "l3xv/Final_Assignment_Template"
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -38,10 +221,9 @@ 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:
+    except Exception as e:  # pylint: disable=broad-except
         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)
@@ -55,17 +237,17 @@ 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 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:
+    except Exception as e:  # pylint: disable=broad-except
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
@@ -76,23 +258,62 @@ 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(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}"})
+            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}",
+                }
+            )
 
     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)
 
@@ -133,7 +354,7 @@ 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:
+    except Exception as e:  # pylint: disable=broad-except
         status_message = f"An unexpected error occurred during submission: {e}"
         print(status_message)
         results_df = pd.DataFrame(results_log)
@@ -146,11 +367,9 @@ with gr.Blocks() as demo:
     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).
@@ -162,20 +381,19 @@ with gr.Blocks() as demo:
 
     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}")
@@ -183,14 +401,18 @@ 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)
+    demo.launch(debug=True, share=False)