Update app.py

app.py

@@ -1,24 +1,24 @@
+# app.py
 import os
-import re
-import tempfile
-from pathlib import Path
-from typing import Optional
-
+import gradio as gr
 import requests
+import openai
+from smolagents import  DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool , LiteLLMModel, CodeAgent 
+from pathlib import Path
+import tempfile
+from smolagents.tools import PipelineTool, Tool
+import pathlib
+from typing import Union, Optional
 import pandas as pd
-import PyPDF2                   
-import pytesseract             
-from PIL import Image          
-
-import gradio as gr            
-import litellm                 
-from opik import track
+from tabulate import tabulate  # pragma: no cover – fallback path
+import re
+import opik
 from litellm.integrations.opik.opik import OpikLogger
 
-from smolagents import tool, LiteLLMModel, CodeAgent , PythonInterpreterTool,DuckDuckGoSearchTool, WikipediaSearchTool, SpeechToTextTool
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-
-# ── 1) Read API keys from HF Spaces Secrets/Variables ─────────────────────────
 GROQ_API_KEY    = os.getenv("Grok_api")       # set as Secret in your Space
 OPIK_API_KEY    = os.getenv("OPIK_API_KEY")       # set as Secret in your Space
 OPIK_WORKSPACE  = os.getenv("OPIK_WORKSPACE")     # set as Variable in your Space
@@ -28,36 +28,129 @@ os.environ["GROQ_API_KEY"]   = GROQ_API_KEY
 os.environ["OPIK_API_KEY"]   = OPIK_API_KEY
 os.environ["OPIK_WORKSPACE"] = OPIK_WORKSPACE
 
-litellm.callbacks = [OpikLogger()]
+    # ──────────────────────────────────────────────────────────────────────────
+    # 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}")
 
-# ── 3) Create a Groq-backed LiteLLMModel ─────────────────────────────────────
-llm = LiteLLMModel(
-    model_id="groq/llama-3.3-70b-versatile",
-    client=litellm
-)
+        # ----- 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
+            )
 
-# ── 4) Define function-based tools with @tool ─────────────────────────────────
-@tool
-def excel_to_text_tool(excel_path: str, sheet_name: Optional[str] = None) -> str:
-    """
-    Read an Excel file and return a Markdown table of the specified sheet.
+        # 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"
     
-    Args:
-    excel_path: Path to the Excel file
-    sheet_name: sheets about the data
-        
-    Returns:
-    Analysis result or error message
+
+    # ------------------------------------------------------------------
+    # 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:
     """
-    path = Path(excel_path).expanduser().resolve()
-    if not path.exists():
-        return f"Error: Excel file not found at {path}"
+    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:
-        sheet = 0 if sheet_name is None else (int(sheet_name) if sheet_name.isdigit() else sheet_name)
-        df = pd.read_excel(path, sheet_name=sheet)
-        return df.to_markdown(index=False)
-    except Exception as e:
-        return f"Error reading Excel file: {e}"
+        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)
 
 @tool
 def pdf_to_text_tool(pdf_path: str) -> str:
@@ -102,100 +195,224 @@ def analyze_image_tool(image_path: str) -> str:
     except Exception as e:
         return f"Error analyzing image: {e}"
 
-# ── 5) Build worker and manager CodeAgents ───────────────────────────────────
-worker_agent = CodeAgent(
-    model=llm,
-    tools=[
-        DuckDuckGoSearchTool(),
-        WikipediaSearchTool(),
-        SpeechToTextTool(),
-        excel_to_text_tool,
-        pdf_to_text_tool,
-        analyze_image_tool,
-        PythonInterpreterTool()
-    ],
-    add_base_tools=True,
-    name="worker_agent",
-    additional_authorized_imports=['pandas','numpy','csv','subprocess'],
-    description="Handles web, Excel, PDF, and image analysis tasks",
-    max_steps=15,
-    verbosity_level=1
-)
 
-manager_agent = CodeAgent(
-    model=llm,
-    tools=[],
-    managed_agents=[worker_agent],
-    additional_authorized_imports=["pandas", "matplotlib.pyplot as plt"],
-    planning_interval=5,
-    name="manager_agent",
-    description="Orchestrates complex workflows via worker_agent",
-    verbosity_level=2
+litellm.callbacks = [OpikLogger()]
+llm = LiteLLMModel(
+    model_id="groq/llama-3.3-70b-versatile",
+    client=litellm
 )
 
-# ── 6) Helper to download attached files ─────────────────────────────────────
-def download_file_if_any(base_api_url: str, task_id: str) -> Optional[str]:
-    url = f"{base_api_url}/files/{task_id}"
-    resp = requests.get(url, timeout=30)
-    if resp.status_code == 404:
-        return None
-    resp.raise_for_status()
-    fname = task_id
-    cd = resp.headers.get("content-disposition", "")
-    m = re.search(r'filename="([^"]+)"', cd)
-    if m:
-        fname = m.group(1)
-    tmp = Path(tempfile.gettempdir()) / "gaia_files"
-    tmp.mkdir(exist_ok=True)
-    fp = tmp / fname
-    fp.write_bytes(resp.content)
-    return str(fp)
-
-# ── 7) Gradio runner callback ─────────────────────────────────────────────────
-def run_and_submit_all(profile: gr.OAuthProfile | None, agent_choice: str):
-    if profile is None:
-        return "Please login to Hugging Face.", None
-    username = profile.username
-
-    resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
-    resp.raise_for_status()
-    questions = resp.json()
-
-    results, answers = [], []
-    for item in questions:
-        tid, q = item["task_id"], item["question"]
-        fpath = download_file_if_any(DEFAULT_API_URL, tid)
-        prompt = q + (f"\n\n---\nFile: {fpath}\n---\n" if fpath else "")
-        agent = worker_agent if agent_choice == "worker" else manager_agent
-        ans = agent.run(prompt)
-        results.append({"Task ID": tid, "Question": q, "Answer": ans})
-        answers.append({"task_id": tid, "submitted_answer": ans})
-
-    payload = {
-        "username": username,
-        "agent_code": "https://huggingface.co/spaces/l3xv/Final_Assignment_Template/tree/main",
-        "answers": answers
-    }
-    sub = requests.post(f"{DEFAULT_API_URL}/submit", json=payload, timeout=60)
-    sub.raise_for_status()
-    data = sub.json()
-    status = f"Score: {data['score']}% ({data['correct_count']}/{data['total_attempted']})"
-    return status, pd.DataFrame(results)
 
-# ── 8) Gradio UI wiring ─────────────────────────────────────────────────────
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+@track
+class BasicAgent:
+    def __init__(self):
+        self.agent = CodeAgent(
+            model=llm,
+            tools=[DuckDuckGoSearchTool(), WikipediaSearchTool(), SpeechToTextTool(), ExcelToTextTool() , pdf_to_text_tool, analyze_image_tool],
+            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 = self.agent.run(question)
+        print(f"Agent returning 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 = "l3xv/Final_Assignment_Template"
+
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    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)
+    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}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        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(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
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    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)
+            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}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('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
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {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
+    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
+
+
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("## Multi-Agent Runner (Groq-only LLM)")
-    profile = gr.LoginButton()
-    agent_choice = gr.Radio(["manager", "worker"], label="Agent to use?", value="manager")
-    run_btn = gr.Button("Run & Submit")
-    out_status = gr.Textbox(label="Status", interactive=False)
-    out_table = gr.DataFrame(label="Results")
-
-    run_btn.click(
+    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.
+        """
+    )
+
+    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
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
         fn=run_and_submit_all,
-        inputs=[profile, agent_choice],
-        outputs=[out_status, out_table]
+        outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    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"
+
+    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?).")
+
+    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")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)