Update app.py

app.py

@@ -1,17 +1,142 @@
 import os
-import gradio as gr
+import re
+import openai
+import pathlib
 import requests
-import inspect
+import tempfile
+import gradio as gr
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
+from pathlib import Path
+from tabulate import tabulate
+from typing import Union, Optional
+from smolagents.tools import PipelineTool, Tool
+from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
+
+
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+#-------------------------------------------------
+#                      TOOLS
+#-------------------------------------------------
+
+#SpeeachToText
+class SpeechToTextTool(PipelineTool):
+    """
+    Transcribes an audio file to text using the OpenAI Whisper API.
+    Only local file paths are supported.
+    """
+
+    name = "transcriber"
+    description = (
+        "This tool sends an audio file to OpenAI Whisper and returns the "
+        "transcribed text."
+    )
+    inputs = {
+        "audio": {
+            "type": "string",
+            "description": "Absolute or relative path to a local audio file."
+        }
+    }
+    output_type = "string"
+
+    def __call__(self, audio: str) -> str:
+        """
+        Allows the tool to be called like a regular function:
+            text = SpeechToTextTool()(path_to_audio)
+        """
+        return self._transcribe(audio)
+
+    @staticmethod
+    def _transcribe(audio_path: str) -> str:
+        """
+        Validates the file path and sends the audio to OpenAI Whisper API.
+        Returns the transcribed text.
+        """
+        if not isinstance(audio_path, str):
+            raise TypeError("Parameter 'audio' must be a string with the file path.")
+
+        path = Path(audio_path).expanduser().resolve()
+        if not path.is_file():
+            raise FileNotFoundError(f"No such audio file: {path}")
+
+        with path.open("rb") as file:
+            response = openai.audio.transcriptions.create(
+                file=file,
+                model="whisper-1",
+                response_format="text"
+            )
+
+        return response 
+
+# ExcelToText
+class ExcelToTextTool(Tool):
+    """Render an Excel worksheet as Markdown text."""
+
+    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 = self._resolve_path(excel_path)
+        if not path.exists():
+            return f"Error: Excel file not found at {path}"
+
+        try:
+            sheet = self._parse_sheet_identifier(sheet_name)
+            df = pd.read_excel(path, sheet_name=sheet)
+            return self._render_dataframe(df)
+
+        except Exception as exc:
+            return f"Error reading Excel file: {exc}"
+
+    def _resolve_path(self, path_str: str) -> pathlib.Path:
+        return pathlib.Path(path_str).expanduser().resolve()
+
+    def _parse_sheet_identifier(self, name: Optional[str]) -> Union[str, int]:
+        if not name:
+            return 0
+        return int(name) if name.isdigit() else name
+
+    def _render_dataframe(self, df: pd.DataFrame) -> str:
+        if hasattr(df, "to_markdown"):
+            return df.to_markdown(index=False)
+
+        from tabulate import tabulate
+        return tabulate(df, headers="keys", tablefmt="github", showindex=False)
+
+
+#-------------------------------------------------
+#               AGENT DEFINITION
+#-------------------------------------------------
 class BasicAgent:
     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]}...")
@@ -19,13 +144,14 @@ class BasicAgent:
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
+    
 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 = os.getenv("SPACE_ID")
 
     if profile:
         username= f"{profile.username}"