Update app.py

app.py

@@ -1,190 +1,29 @@
 import os
 import gradio as gr
 import requests
-import openai
-from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
-from pathlib import Path
-import tempfile
-from smolagents.tools import PipelineTool, Tool
-import pathlib
-from typing import Union, Optional
 import pandas as pd
-from tabulate import tabulate  # pragma: no cover – fallback path
-import re
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HFApiModel
+# Test comment
 
 # (Keep Constants 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"
-
-    # ------------------------------------------------------------------
-    # Core logic
-    # ------------------------------------------------------------------
-    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)
-            from tabulate import tabulate  # pragma: no cover – fallback path
-
-            return tabulate(df, headers="keys", tablefmt="github", showindex=False)
-
-        except Exception as exc:  # broad catch keeps the agent chat‑friendly
-            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 ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 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.")
-
+        token_key = os.getenv("HFAPI_KEY")
+        # Initialize the search tool
+        model = HFApiModel(model_id="Qwen/Qwen2.5-7B-Instruct", api_key=token_key)
+        search_tool = DuckDuckGoSearchTool()
+        
+        self.agent = CodeAgent(model=model, tools=[search_tool])
     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}")
+        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):
@@ -193,7 +32,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = "l3xv/Final_Assignment_Template"
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
         username= f"{profile.username}"
@@ -244,31 +83,11 @@ 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:
-            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)
+            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:
@@ -361,7 +180,7 @@ 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 = "l3xv/Final_Assignment_Template"
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")