Delete app.py

app.py

@@ -1,402 +0,0 @@
-import os
-from dotenv import load_dotenv
-load_dotenv()
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-#model requirement
-from smolagents import DuckDuckGoSearchTool, load_tool, tool, CodeAgent,InferenceClientModel
-from typing import TypedDict, List, Dict, Any, Optional,Callable
-from langgraph.graph import StateGraph, END
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from youtube_transcript_api import YouTubeTranscriptApi
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-def openrouter_inference(prompt, model="deepseek/deepseek-r1:free"):
-    api_key = os.environ["OPENROUTER_API_KEY"]
-    url = "https://openrouter.ai/api/v1/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json"
-    }
-    payload = {
-        "model": model,
-        "messages": [
-            {"role": "user", "content": prompt}
-        ]
-    }
-    response = requests.post(url, headers=headers, json=payload)
-    response.raise_for_status()
-    data = response.json()
-    # Extract the answer from the response
-    return data["choices"][0]["message"]["content"]
-
-@tool
-def add(a:int,b:int)->int:
-    """
-    Adds two integers.
-    Args:
-        a (int): The first integer.
-        b (int): The second integer.
-    Returns:
-        int: The sum of the two integers.   
-    """
-    return a + b
-
-@tool
-def subtract(a:int,b:int)->int:
-    """
-    Subtracts two integers.
-    Args:
-        a (int): The first integer.
-        b (int): The second integer.
-    Returns:
-        int: The difference of the two integers.   
-    """
-    return a - b
-
-@tool
-def multiply(a:int,b:int)->int:
-    """
-    Multiplies two integers.
-    Args:
-        a (int): The first integer.
-        b (int): The second integer.
-    Returns:
-        int: The product of the two integers.   
-    """
-    return a * b
-
-@tool
-def divide(a:int,b:int)->float:
-    """
-    Divides two integers.
-    Args:
-        a (int): The numerator.
-        b (int): The denominator.
-    Returns:
-        float: The quotient of the two integers.   
-    """
-    if b == 0:
-        raise ValueError("Division by zero is not allowed.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
-
-search_tool = DuckDuckGoSearchTool()
-
-@tool
-def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"web_results": formatted_search_docs}
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
-    
-    Args:
-        query: The search query."""
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"arvix_results": formatted_search_docs}
-
-@tool
-def wikipedia_tool(query: str) -> str:
-    """
-    Searches Wikipedia for the given query and returns a summary.
-
-    Args:
-        query (str): The search term or question to look up on Wikipedia.
-
-    Returns:
-        str: A summary or error message.
-    """
-    try:
-        search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-        formatted_search_docs = "\n\n---\n\n".join(
-            [
-                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-                for doc in search_docs
-            ]
-        )
-        return formatted_search_docs
-    except Exception as e:
-        return f"Wikipedia search error: {e}"
-    
-@tool
-def youtube_transcript_tool(video_id: str,query:str) -> str:
-    """
-    Fetches the transcript of a YouTube video.
-
-    Args:
-        video_id (str): The YouTube video ID.
-        query (str): The question to be answered based on the transcript.
-
-    Returns:
-        str: The transcript text or an error message.
-    """
-    try:
-        transcript = YouTubeTranscriptApi.get_transcript(video_id)
-        question = f"Answer the question based on the transcript: {query}"
-        prompt = (
-            f"Given the following YouTube transcript, answer the question as directly as possible:\n"
-            f"Question: {question}\n"
-            f"Transcript: {transcript}\n"
-            f"Answer:"
-        )
-        answer = openrouter_inference(prompt)
-    except Exception as e:
-        return f"Transcript error: {e}"
-    
-
-    
-    
-image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
-
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        token=os.environ["OPENROUTER_API_KEY"]  
-        self.system_prompt = """
-You are a helpful assistant. Answer each question as directly and briefly as possible.
-Return only the answer, with no extra text, no punctuation, and no justification.
-If the answer is a list, return it as a comma-separated list with no brackets or bullets.
-If the answer is a number, write it in digits with no units.
-If the answer is a string, use lowercase and no articles or abbreviations.
-"""
-        
-        model = InferenceClientModel(
-    model_id="deepseek/deepseek-r1:free",  # Correct OpenRouter model ID
-    token=os.environ["OPENROUTER_API_KEY"],               # Your OpenRouter API key
-    provider="openrouter"                                 # Explicitly set to openrouter
-)
-        self.agent= CodeAgent(
-            tools = [add, subtract, multiply, divide,modulus,arvix_search, web_search, image_generation_tool,youtube_transcript_tool, wikipedia_tool],
-            model=model,
-        )
-    def __call__(self, question: str, context: str = "") -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Inject system prompt + question
-        question_with_prompt = f"{self.system_prompt}\n\nContext: {context}\n\nQuestion: {question.strip()}"
-        try:
-            answer = openrouter_inference(question_with_prompt)
-        except Exception as e:
-            print(f"Error calling OpenRouter: {e}")
-            answer = f"Sorry, I couldn't get an answer from the model {e}."
-        print(f"Agent returning answer: {answer.strip()}")
-        return answer.strip()
-        # # Fix: handle dict or string
-        # if isinstance(answer, dict) and "content" in answer:
-        #     result = answer["content"]
-        # else:
-        #     result = str(answer)
-        # print(f"Agent returning answer: {result.strip()}")
-        # return result.strip()
-
-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)
-
-    # 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)