app.py

import os
import gradio as gr
import requests

import speech_recognition as sr
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 transformers import AutoTokenizer, AutoModelForCausalLM
from langchain.agents import initialize_agent
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
from langchain_community.llms import HuggingFaceHub

from typing import Union
import os
from langchain.agents import AgentExecutor, Tool, initialize_agent
from langchain_community.llms import Ollama
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
from langchain_community.document_loaders import (
    CSVLoader,
    PyPDFLoader,
    UnstructuredWordDocumentLoader
)
from langchain_community.utilities import TextRequestsWrapper
import speech_recognition as sr
from pydub import AudioSegment  # For audio format conversion

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        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 __init__(self, model_name: str = "llama3"):
        """
        Open-source multi-modal agent with:
        - Web search
        - Document processing
        - Speech-to-text
        - URL content fetching
        """
        # Initialize LLM (local via Ollama)
        self.llm = Ollama(model=model_name, temperature=0.7)
        
        # Initialize tools
        self.search_tool = DuckDuckGoSearchRun()
        self.wikipedia_tool = WikipediaQueryRun()
        self.requests_tool = TextRequestsWrapper()
        
        # Speech recognition
        self.recognizer = sr.Recognizer()
        
        # Initialize agent
        self.tools = self._initialize_tools()
        self.agent = self._create_agent()

    def _initialize_tools(self) -> list[Tool]:
        """Initialize all available tools"""
        return [
            Tool(
                name="Web Search",
                func=self.search_tool.run,
                description="For current events/unknown topics"
            ),
            Tool(
                name="Wikipedia",
                func=self.wikipedia_tool.run,
                description="For factual information"
            ),
            Tool(
                name="Document Loader",
                func=self.process_document,
                description="Processes PDF, Word, CSV files"
            ),
            Tool(
                name="Speech Transcription",
                func=self.transcribe_audio,
                description="Converts speech from audio files to text"
            ),
            Tool(
                name="Website Content",
                func=self.requests_tool.get,
                description="Fetches content from URLs"
            )
        ]

    def _create_agent(self) -> AgentExecutor:
        """Create the agent executor"""
        return initialize_agent(
            tools=self.tools,
            llm=self.llm,
            agent="structured-chat-react",
            verbose=True,
            handle_parsing_errors=True
        )

    def process_document(self, file_path: str) -> str:
        """Handle different document types"""
        if not os.path.exists(file_path):
            return "File not found"
        
        ext = os.path.splitext(file_path)[1].lower()
        
        try:
            if ext == '.pdf':
                loader = PyPDFLoader(file_path)
            elif ext in ('.doc', '.docx'):
                loader = UnstructuredWordDocumentLoader(file_path)
            elif ext == '.csv':
                loader = CSVLoader(file_path)
            else:
                return "Unsupported file format"
            
            docs = loader.load()
            return "\n".join([doc.page_content for doc in docs])
        
        except Exception as e:
            return f"Error processing document: {str(e)}"

    def _convert_audio_format(self, audio_path: str) -> str:
        """Convert audio to WAV format if needed"""
        if audio_path.endswith('.wav'):
            return audio_path
            
        try:
            sound = AudioSegment.from_file(audio_path)
            wav_path = os.path.splitext(audio_path)[0] + ".wav"
            sound.export(wav_path, format="wav")
            return wav_path
        except:
            return audio_path  # Fallback to original if conversion fails

    def transcribe_audio(self, audio_path: str) -> str:
        """Convert speech to text using purely open-source tools"""
        audio_path = self._convert_audio_format(audio_path)
        
        try:
            with sr.AudioFile(audio_path) as source:
                audio = self.recognizer.record(source)
                return self.recognizer.recognize_vosk(audio)  # Offline recognition
        except sr.UnknownValueError:
            try:
                # Fallback to Sphinx if Vosk fails
                return self.recognizer.recognize_sphinx(audio)
            except Exception as e:
                return f"Transcription failed: {str(e)}"

    def run(self, input_data: Union[str, dict]) -> str:
        """
        Handle different input types:
        - Text queries
        - File paths
        - Structured requests
        """
        if isinstance(input_data, dict):
            if 'query' in input_data:
                return self.agent.run(input_data['query'])
            elif 'file' in input_data:
                content = self.process_document(input_data['file'])
                return self.agent.run(f"Process this: {content}")
        elif isinstance(input_data, str):
            if input_data.endswith(('.pdf', '.docx', '.csv')):
                content = self.process_document(input_data)
                return self.agent.run(f"Process this document: {content}")
            elif input_data.endswith(('.wav', '.mp3', '.ogg')):
                content = self.transcribe_audio(input_data)
                return self.agent.run(f"Process this transcript: {content}")
            else:
                return self.agent.run(input_data)
        return "Unsupported input type"

# Usage Example
if __name__ == "__main__":
    agent = FullyOpenSourceAgent(model_name="mistral")  # Try "llama3", "gemma", etc.
    
    # Example 1: Web search
    print(agent.run("Latest breakthroughs in renewable energy"))
    
    # Example 2: Process document
    print(agent.run({"file": "research.pdf"}))
    
    # Example 3: Complex workflow
    print(agent.run({
        "query": "Summarize the key points from this meeting recording",
        "file": "meeting.wav"
    }))



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)