Update app.py

app.py

@@ -1,314 +1 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-import asyncio
-import nest_asyncio
-from typing import List, Dict, Any
-from llama_index.core.agent import ReActAgent
-from llama_index.core.agent.workflow import AgentWorkflow
-from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
-from youtube_tool import youtube_transcript_tool, youtube_transcript_snippet_tool
-#from multiple_tools import round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool, wikipedia_search_tool
-from multiple_tools import round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool, wikipedia_search_tool, transcribe_audio_tool, excel_food_sales_sum_tool, parse_file_and_summarize_tool, solve_chess_image_tool, vegetable_classifier_tool
-from agent import smart_agent
-from llama_index.llms.openai import OpenAI
-import re
-#-----------------------------------------------------------------
-
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_key = os.getenv("HF_TOKEN")
-OpenAI_key = os.getenv("OPEN_AI_TOKEN")
-
-# --- Basic Agent Definition ---
-
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized. . . .")
-        
-        #self.llm = OpenAI(model="gpt-4o-mini", temperature=0.2, api_key=OpenAI_key)
-        # self.system_prompt = (
-        # "You are a helpful AI assistant completing GAIA benchmark tasks.\n"
-        # "You MUST use the tools provided when needed.\n"
-        # "If you already have enough information, respond directly with:\n"
-        # "<answer>\n"
-        # "Once you output '<answer>', stop reasoning and do not call any tool.\n"
-        # )
-        self.system_prompt = (
-        "You are a helpful assistant tasked with answering questions using a set of tools.\n"
-        "Your final answer must strictly follow this format:\n"
-        "FINAL ANSWER: [ANSWER]\n"
-        "Only write the answer in that exact format. Do not explain anything. Do not include any other text. \n"
-        "If you are provided with a similar question and its final answer, and the current question is **exactly the same**, then simply return the same final answer without using any tools. \n"
-        "Only use tools if the current question is different from the similar one. \n"
-        "Examples: \n"
-        "- FINAL ANSWER: FunkMonk \n"
-        "- FINAL ANSWER: Paris \n"
-        "- FINAL ANSWER: 128 \n"
-        " \n"
-        "Once you output 'FINAL ANSWER', stop reasoning and do not call any tool.\n"
-        "If you do not follow this format exactly, your response will be considered incorrect. \n"
-        )
-        self.llm = HuggingFaceInferenceAPI(
-            model_name="deepseek-ai/DeepSeek-R1-0528", 
-            token=HF_key,
-            provider="auto"
-        )
-        #self.llm = OpenAI(model="gpt-4o", temperature=0.1, api_key=OpenAI_key)
-        # self.system_prompt = (
-        #     "You are a helpful AI assistant completing GAIA benchmark tasks.\n"
-        #     "You MUST use the tools provided to answer the user's question. Do not answer from your own knowledge.\n"
-        #     "Carefully analyze the question to determine the most appropriate tool to use.\n"
-        #     "Here are guidelines for using the tools:\n"
-        #     "- Use 'wikipedia_search_tool' to find factual information about topics, events, people, etc. (e.g., 'Use wikipedia_search to find the population of France').\n"
-        #     "- Use 'youtube_transcript_tool' to extract transcripts from YouTube videos when the question requires understanding the video content. (e.g., 'Use youtube_transcript to summarize the key points of this video').\n"
-        #     "- Use 'transcribe_audio_tool' to transcribe uploaded audio files. (e.g., 'Use audio_transcriber to get the text from this audio recording').\n"
-        #     "- Use 'solve_chess_image_tool' to analyze and solve chess puzzles from images. (e.g., 'Use chess_image_solver to determine the best move in this chess position').\n"
-        #     "- Use 'parse_file_and_summarize_tool' to parse and analyze data from Excel or CSV files. (e.g., 'Use file_parser to calculate the average sales from this data').\n"
-        #     "- Use 'vegetable_classifier_tool' to classify a list of food items and extract only the vegetables. (e.g., 'Use vegetable_classifier_2022 to get a list of the vegetables in this grocery list').\n"
-        #     "- Use 'excel_food_sales_sum_tool' to extract total food sales from excel files. (e.g., 'Use excel_food_sales_sum to calculate the total food sales').\n"
-        #     "- Use 'google_web_search_tool' to find factual information about topics, events, people, from the web if not spificied to be fund in wikipedia etc. (e.g., 'find the population of France').\n"
-        #     "Do NOT guess or make up answers. If a tool cannot provide the answer, truthfully respond that you were unable to find the information.\n"
-        #     "Use the tools to research or calculate the answer.\n"
-        #     "If a tool fails, explain the reason for the failure instead of hallucinating an answer.\n"
-        #     "Provide concise and direct answers as requested in the questions. Do not add extra information unless explicitly asked for.\n"
-        #     "For example, if asked for a number, return only the number. If asked for a list, return only the list.\n"
-        # )
-        self.agent = AgentWorkflow.from_tools_or_functions(
-            [
-                wikipedia_search_tool, youtube_transcript_tool, youtube_transcript_snippet_tool, round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool,transcribe_audio_tool, excel_food_sales_sum_tool, parse_file_and_summarize_tool, solve_chess_image_tool, vegetable_classifier_tool
-             ],
-            llm=self.llm,
-            system_prompt=self.system_prompt,
-        )
-    def extract_answer(self, text: str) -> str:
-        match = re.search(r"(?<=<answer>)(.*?)(?=</answer>)", text)
-        return match.group(1) if match else ""
-    
-    async def run(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # answer = await self.agent.run(question)
-        answer = await self.agent.run(
-            f"{question}\n\nIf you have enough information, respond with a concise final answer.",
-            max_iterations=10
-        )
-        return str(answer)
-        #return self.extract_answer(str(answer));
-        # if hasattr(answer, "output"):
-        #     print(f"Agent returning answer: {answer}")
-        #     return str(answer.output)
-        # else:
-        #     print(f"Agent returning answer: {answer}")
-        #     return str(answer)
-        
-    def __call__(self, question: str) -> str:
-        return asyncio.run(self.run(question))
-
-
-
-
-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
-
-    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")
-    #     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}"})
-
-    # 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)
-
-    #3A
-
-    async def run_all_questions(questions_data):
-        answers_payload = []
-        for item in questions_data:
-            task_id = item.get("task_id")
-            question_text = item.get("question")
-            if not task_id or question_text is None:
-                print(f"Skipping item with missing task_id or question: {item}")
-                continue
-            try:
-                answer = await agent.run(question_text)  # await coroutine
-                answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-                print(f"Answered Task {task_id}:: {answer}")
-            except Exception as e:
-                answers_payload.append({"task_id": task_id, "submitted_answer": f"AGENT ERROR: {e}"})
-                print(f"Error on Task {task_id}: {e}")
-        return answers_payload
-
-    answers_payload = asyncio.run(run_all_questions(questions_data))
-    #answers_payload = run_all_questions(questions_data)
-    
-    #3B
-
-    # 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("# 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,
-        outputs=[status_output, results_table]
-    )
-
-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 = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    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)
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