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app.py

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+import os
+import gradio as gr
+import requests
+import pandas as pd
+import time
+from pathlib import Path
+from typing import Dict, Any, List, Optional, TypedDict, Annotated
+import operator
+
+# LangChain and LangGraph imports
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_openai import AzureChatOpenAI
+from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
+from langchain_core.tools import tool
+from langchain_core.prompts import ChatPromptTemplate
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.prebuilt import ToolNode
+from langgraph.checkpoint.memory import MemorySaver
+
+# Existing utility imports
+from youtube_transcript_api import YouTubeTranscriptApi, NoTranscriptFound
+from bs4 import BeautifulSoup
+import pdfplumber
+import docx
+import speech_recognition as sr
+import base64
+import tempfile
+import re
+from io import BytesIO, StringIO
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ------------------------------
+# Configuration
+# ------------------------------
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+api_key = os.getenv("AZURE_OPENAI_API_KEY")
+azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+azure_api_version = os.getenv("AZURE_OPENAI_API_VERSION")
+azure_deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME")
+azure_model_name = os.getenv("AZURE_OPENAI_MODEL_NAME")
+
+# Initialize Azure OpenAI LLM
+llm = AzureChatOpenAI(
+    deployment_name=azure_deployment_name,
+    model_name=azure_model_name,
+    temperature=0.0,
+    top_p=0.1,
+    azure_endpoint=azure_endpoint,
+    api_key=api_key,
+    api_version=azure_api_version,
+)
+
+# ------------------------------
+# State Definition
+# ------------------------------
+class AgentState(TypedDict):
+    messages: Annotated[List[Any], operator.add]
+    question: str
+    task_id: str
+    file_name: str
+    file_type: Optional[str]
+    file_url: Optional[str]
+    final_answer: Optional[str]
+    agent_used: Optional[str]
+    reasoning: Optional[str]
+
+# ------------------------------
+# Tool Functions
+# ------------------------------
+
+def transcribe_audio(content: bytes) -> str:
+    """Transcribe audio from bytes to text."""
+    try:
+        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as mp3_tmp:
+            mp3_tmp.write(content)
+            mp3_path = mp3_tmp.name
+        
+        wav_path = mp3_path.replace(".mp3", ".wav")
+        
+        try:
+            from pydub import AudioSegment
+            audio = AudioSegment.from_mp3(mp3_path)
+            audio.export(wav_path, format="wav")
+            audio_file = wav_path
+        except ImportError:
+            audio_file = mp3_path
+        
+        recognizer = sr.Recognizer()
+        with sr.AudioFile(audio_file) as source:
+            audio = recognizer.record(source)
+        transcript = recognizer.recognize_google(audio)
+        
+        for path in [mp3_path, wav_path]:
+            if os.path.exists(path):
+                os.remove(path)
+                
+        return f"Audio Transcript: {transcript}"
+    
+    except Exception as e:
+        print(f"Audio transcription error: {e}")
+        return "Could not transcribe audio"
+
+@tool
+def parse_file_tool(file_url: str, file_name: str) -> str:
+    """Parse various file types and extract content."""
+    try:
+        if len(file_name) > 0:
+            file_type = Path(file_name).suffix.lower()
+            file_type = file_type.split("?")[0]
+        else:
+            file_type = None
+            
+        if file_type:
+            resp = requests.get(file_url, timeout=30)
+            resp.raise_for_status()
+            content = resp.content
+
+            # Excel Files
+            if file_type in [".xlsx", ".xls"]:
+                try:
+                    df = pd.read_excel(BytesIO(content))
+                    return f"Excel Content:\n{df.head(10).to_string(index=False)}"
+                except Exception as e:
+                    return f"Excel parsing error: {str(e)}"
+
+            # CSV Files
+            elif file_type == ".csv":
+                try:
+                    df = pd.read_csv(BytesIO(content))
+                    return f"CSV Content:\n{df.head(10).to_string(index=False)}"
+                except Exception as e:
+                    return f"CSV parsing error: {str(e)}"
+
+            # Text Files
+            elif file_type == ".txt":
+                text = content.decode(errors='ignore')
+                return f"Text Content:\n{text[:5000]}"
+
+            # PDF Files
+            elif file_type == ".pdf":
+                try:
+                    with pdfplumber.open(BytesIO(content)) as pdf:
+                        text = "\n".join(page.extract_text() or "" for page in pdf.pages[:5])
+                    return f"PDF Content:\n{text[:5000]}"
+                except Exception as e:
+                    return f"PDF parsing error: {str(e)}"
+
+            # DOCX Files
+            elif file_type == ".docx":
+                try:
+                    d = docx.Document(BytesIO(content))
+                    text = "\n".join(p.text for p in d.paragraphs[:100])
+                    return f"DOCX Content:\n{text[:5000]}"
+                except Exception as e:
+                    return f"DOCX parsing error: {str(e)}"
+
+            # MP3 Files
+            elif file_type == ".mp3":
+                return transcribe_audio(content)
+
+            # Python Files
+            elif file_type == ".py":
+                text = content.decode(errors='ignore')
+                return f"Python Code:\n{text[:5000]}"
+
+            else:
+                return f"Unsupported file type: {file_type}"
+        else:
+            return "No file type provided or file URL is invalid."
+    except Exception as e:
+        print(f"[parse_file_tool] ERROR: {e}")
+        return f"File parsing failed: {str(e)}"
+
+@tool
+def youtube_transcript_tool(url: str) -> str:
+    """Extract transcript from YouTube video."""
+    try:
+        video_id = url.split("v=")[-1].split("&")[0]
+        transcript = YouTubeTranscriptApi.get_transcript(video_id)
+        return " ".join([e['text'] for e in transcript])
+    except NoTranscriptFound:
+        return "No transcript available for this video"
+    except Exception as e:
+        return f"Error retrieving transcript: {str(e)}"
+
+def scrape_text_from_url(url: str, max_chars=4000) -> str:
+    """Fetch and clean main text from a webpage."""
+    try:
+        resp = requests.get(url, timeout=10)
+        soup = BeautifulSoup(resp.text, 'html.parser')
+        text = ' '.join(soup.stripped_strings)
+        return text[:max_chars]
+    except Exception as e:
+        return f"Could not scrape {url}: {e}"
+
+@tool
+def web_search_tool(question: str) -> str:
+    """Perform web search using DuckDuckGo and scrape results."""
+    try:
+        from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
+        ddg_spec = DuckDuckGoSearchToolSpec()
+        results = ddg_spec.duckduckgo_full_search(question) or []
+
+        if not isinstance(results, list):
+            return "No search results found."
+
+        max_results = 10
+        min_chars = 400
+        max_chars = 4000
+
+        for entry in results[:max_results]:
+            href = entry.get("href", "")
+            if not href:
+                continue
+
+            text = scrape_text_from_url(href, max_chars=max_chars)
+            if text.startswith("Could not scrape") or len(text) < min_chars:
+                continue
+            
+            return (
+                f"Here is content scraped from {href}:\n\n"
+                f"{text}\n\n"
+                "Based on this, please answer the original question."
+            )
+
+        # Fallback to search result metadata
+        if not results:
+            return "No search results found."
+
+        summary_lines = []
+        for idx, entry in enumerate(results[:max_results], start=1):
+            title = entry.get("title") or "Untitled result"
+            snippet = (entry.get("body") or "").replace("\n", " ")[:160]
+            href = entry.get("href")
+            summary_lines.append(f"{idx}. {title} – {snippet}  ({href})")
+
+        return (
+            "I could not successfully scrape any of the top pages. "
+            "Here are the top DuckDuckGo results:\n\n"
+            + "\n".join(summary_lines)
+            + "\n\nPlease answer the original question using this list."
+        )
+    except Exception as e:
+        return f"Web search failed: {str(e)}"
+
+@tool
+def image_processing_tool(file_url: str, question: str) -> str:
+    """Process image and answer questions about it using Azure Vision."""
+    try:
+        print(f"Processing image from URL: {file_url}")
+        resp = requests.get(file_url, timeout=30)
+        resp.raise_for_status()
+        raw = resp.content
+        
+        mime = resp.headers.get("Content-Type", "image/png")
+        img_b64 = base64.b64encode(raw).decode()
+        data_uri = f"data:{mime};base64,{img_b64}"
+
+        print("Image downloaded and encoded successfully.")
+        
+        from openai import AzureOpenAI
+        vision_client = AzureOpenAI(
+            api_key=api_key,
+            api_version=azure_api_version,
+            azure_endpoint=azure_endpoint,
+        )
+        
+        messages = [
+            {"role": "system", "content": "You are a vision expert. Answer based only on the image content."},
+            {"role": "user", "content": [
+                {"type": "text", "text": question},
+                {"type": "image_url", "image_url": {"url": data_uri}}
+            ]},
+        ]
+        
+        response = vision_client.chat.completions.create(
+            model=azure_model_name,
+            messages=messages,
+            temperature=0.0,
+            max_tokens=2000,
+        )
+        
+        print(f"Vision API response received: {response.choices[0].message.content.strip()}")
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        return f"Vision API error: {e}"
+
+# ------------------------------
+# Agent Functions
+# ------------------------------
+
+# prompts.py  (new file)
+SCORER_TEMPLATE = """You are a general AI assistant.
+Answer the question and finish with:
+FINAL ANSWER: <your answer>
+
+Formatting rules:
+• numbers: digits only, no commas/units unless requested
+• strings: no articles/abbreviations, digits in plain text
+• for lists: same rules per element, comma-separated, no spaces
+"""
+
+from langchain_core.prompts import ChatPromptTemplate
+
+def make_prompt(extra_instruction: str = "") -> ChatPromptTemplate:
+    return ChatPromptTemplate.from_messages([
+        ("system", SCORER_TEMPLATE + "\n" + extra_instruction),
+        ("human", "{human_input}")
+    ])
+
+import re
+
+def extract_final_answer(text: str) -> str:
+    # robust to quotes, stray whitespace, different capitalisation
+    m = re.search(r"FINAL ANSWER:\s*(.+)", text, re.I | re.S)
+    ans = m.group(1).strip() if m else text.strip()
+    # strip surrounding quotes/backticks
+    return re.sub(r'^[\'"`\s]+|[\'"`\s]+$', "", ans)
+
+
+
+def router_agent(state: AgentState) -> AgentState:
+    """Router agent that determines which specialized agent to use."""
+    question = state["question"]
+    file_name = state.get("file_name", "")
+    
+    # Check for files
+    if file_name:
+        file_type = Path(file_name).suffix.lower().split("?")[0] if len(file_name)>0 else None
+        
+        # Image files
+        if file_type in ['.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp']:
+            return {
+                **state,
+                "agent_used": "image_agent",
+                "reasoning": f"Image file detected: {file_name}"
+            }
+        # Other files
+        else:
+            return {
+                **state,
+                "agent_used": "file_agent", 
+                "reasoning": f"File detected: {file_name} (type: {file_type})"
+            }
+    
+    # Check for YouTube links
+    if "youtube.com" in question.lower() or "youtu.be" in question.lower():
+        return {
+            **state,
+            "agent_used": "youtube_agent",
+            "reasoning": "YouTube link detected in question"
+        }
+    
+    # Check if question contains all needed information (self-contained)
+    self_contained_indicators = [
+        "reverse", "backward", "opposite", "calculate", "math", "add", "subtract",
+        "multiply", "divide", "cipher", "decode", "encode", "spell", "count"
+    ]
+    
+    if any(indicator in question.lower() for indicator in self_contained_indicators):
+        # Additional check: does it seem like it needs external info?
+        external_indicators = ["who is", "when did", "where is", "what year", "latest", "current"]
+        if not any(indicator in question.lower() for indicator in external_indicators):
+            return {
+                **state,
+                "agent_used": "reasoning_agent",
+                "reasoning": "Question appears self-contained, no external data needed"
+            }
+    
+    # Default to web search
+    return {
+        **state,
+        "agent_used": "web_search_agent",
+        "reasoning": "Question requires external knowledge - using web search"
+    }
+
+def reasoning_agent(state: AgentState) -> AgentState:
+    """Agent for self-contained reasoning tasks."""
+    question = state["question"]
+    
+    extra_sys = """You are a reasoning expert. Answer questions that can be
+    solved with logic, mathematics, or text manipulation without external data."""
+    prompt = make_prompt(extra_sys)
+        
+    human_block = question
+
+    content = (prompt | llm).invoke({"human_input": human_block}).content
+    final_answer = extract_final_answer(content)
+    
+    return {
+        **state,
+        "final_answer": final_answer,
+        "messages": state["messages"] + [AIMessage(content=content)]
+    }
+
+def file_agent(state: AgentState) -> AgentState:
+    """Agent for processing various file types."""
+    question = state["question"]
+    file_url = state.get("file_url")
+    file_name = state.get("file_name", "")
+    
+    if not file_url:
+        return {
+            **state,
+            "final_answer": "No file URL provided",
+            "messages": state["messages"] + [AIMessage(content="No file URL provided")]
+        }
+    
+    # Parse the file
+    file_content = parse_file_tool.invoke({"file_url": file_url, "file_name": file_name})
+    
+    extra_sys = """You are a file analysis expert. Based on the file content provided,
+        answer the user's question accurately and concisely."""
+    prompt = make_prompt(extra_sys)
+        
+    human_block = f"Question: {question}\n\nFile Content:\n{file_content}"
+    
+    content = (prompt | llm).invoke({"human_input": human_block}).content
+
+    final_answer = extract_final_answer(content)
+    
+    return {
+        **state,
+        "final_answer": final_answer,
+        "messages": state["messages"] + [AIMessage(content=content)]
+    }
+
+def youtube_agent(state: AgentState) -> AgentState:
+    """Agent for processing YouTube video transcripts."""
+    question = state["question"]
+    
+    # Extract YouTube URL from question
+    import re
+    youtube_pattern = r'(https?://(?:www\.)?(?:youtube\.com/watch\?v=|youtu\.be/)[\w-]+)'
+    urls = re.findall(youtube_pattern, question)
+    
+    if not urls:
+        return {
+            **state,
+            "final_answer": "No YouTube URL found in question",
+            "messages": state["messages"] + [AIMessage(content="No YouTube URL found")]
+        }
+    
+    # Get transcript
+    transcript = youtube_transcript_tool.invoke({"url": urls[0]})
+    
+    extra_sys = """You are a YouTube content expert. Based on the video transcript provided,
+        answer the user's question accurately and concisely."""
+    prompt = make_prompt(extra_sys)
+        
+    human_block = f"Question: {question}\n\nTranscript: {transcript}"
+    
+    content = (prompt | llm).invoke({"human_input": human_block}).content
+
+    final_answer = extract_final_answer(content)
+    
+    return {
+        **state,
+        "final_answer": final_answer,
+        "messages": state["messages"] + [AIMessage(content=content)]
+    }
+
+def web_search_agent(state: AgentState) -> AgentState:
+    """Agent for web search and information retrieval."""
+    question = state["question"]
+    
+    # Perform web search
+    search_results = web_search_tool.invoke({"question": question})
+
+    extra_sys = """You are a web search expert. Based on the search results provided,
+        answer the user's question accurately and concisely."""
+    prompt = make_prompt(extra_sys)
+        
+    human_block = f"Question: {question}\n\Search Results:: {search_results}"
+    
+    content = (prompt | llm).invoke({"human_input": human_block}).content
+
+    final_answer = extract_final_answer(content)
+    
+    return {
+        **state,
+        "final_answer": final_answer,
+        "messages": state["messages"] + [AIMessage(content=content)]
+    }
+
+def image_agent(state: AgentState) -> AgentState:
+    """Agent for processing images."""
+    question = state["question"]
+    file_url = state.get("file_url")
+    
+    if not file_url:
+        return {
+            **state,
+            "final_answer": "No image URL provided",
+            "messages": state["messages"] + [AIMessage(content="No image URL provided")]
+        }
+    
+    # Process the image
+    image_analysis = image_processing_tool.invoke({"file_url": file_url, "question": question})
+
+    extra_sys = """You are a web search expert. Based on the search results provided,
+        answer the user's question accurately and concisely."""
+    prompt = make_prompt(extra_sys)
+        
+    human_block = f"Question: {question}\n\nImage Analysis: {image_analysis}"
+    
+    content = (prompt | llm).invoke({"human_input": human_block}).content
+
+    final_answer = extract_final_answer(content)
+    
+    return {
+        **state,
+        "final_answer": final_answer,
+        "messages": state["messages"] + [AIMessage(content=content)]
+    }
+
+# ------------------------------
+# Conditional Logic
+# ------------------------------
+
+def route_to_agent(state: AgentState) -> str:
+    """Route to the appropriate agent based on the router's decision."""
+    agent_used = state.get("agent_used")
+    
+    if agent_used == "reasoning_agent":
+        return "reasoning_agent"
+    elif agent_used == "file_agent":
+        return "file_agent"
+    elif agent_used == "youtube_agent":
+        return "youtube_agent"
+    elif agent_used == "image_agent":
+        return "image_agent"
+    else:
+        return "web_search_agent"
+
+def should_end(state: AgentState) -> str:
+    """Check if we have a final answer and should end."""
+    if state.get("final_answer"):
+        return END
+    else:
+        return "router"
+
+# ------------------------------
+# Graph Construction
+# ------------------------------
+
+def create_agent_graph():
+    """Create and return the agent graph."""
+    workflow = StateGraph(AgentState)
+    
+    # Add nodes
+    workflow.add_node("router", router_agent)
+    workflow.add_node("reasoning_agent", reasoning_agent) 
+    workflow.add_node("file_agent", file_agent)
+    workflow.add_node("youtube_agent", youtube_agent)
+    workflow.add_node("web_search_agent", web_search_agent)
+    workflow.add_node("image_agent", image_agent)
+    
+    # Add edges
+    workflow.add_edge(START, "router")
+    workflow.add_conditional_edges("router", route_to_agent)
+    
+    # All agents go to end
+    workflow.add_edge("reasoning_agent", END)
+    workflow.add_edge("file_agent", END)
+    workflow.add_edge("youtube_agent", END)
+    workflow.add_edge("web_search_agent", END)
+    workflow.add_edge("image_agent", END)
+    
+    # Compile the graph
+    memory = MemorySaver()
+    graph = workflow.compile(checkpointer=memory)
+    
+    return graph
+
+# ------------------------------
+# Main Agent Class
+# ------------------------------
+
+class LangGraphAgent:
+    def __init__(self):
+        """Initialize the LangGraph agent."""
+        self.graph = create_agent_graph()
+        self.api_url = DEFAULT_API_URL
+    
+    def __call__(self, question: str, task_id: str, file_name: str, file_type: str = None) -> str:
+        """
+        Main method to process a question and return an answer.
+        
+        Args:
+            question (str): The question to answer
+            task_id (str): Task ID for file retrieval
+            file_name (str): Name of the file associated with the question
+            file_type (str): Type of the file (e.g., .pdf, .docx, etc.)
+        Returns:
+            str: The answer to the question
+        """
+        try:
+            # Prepare initial state
+            initial_state = {
+                "messages": [HumanMessage(content=question)],
+                "question": question,
+                "task_id": task_id,
+                "file_name": file_name or "",
+                "file_type": Path(file_name).suffix.lower().split("?")[0] if len(file_name)>0 else None,
+                "file_url": f"{self.api_url}/files/{task_id}" if len(file_name)>0 else None,
+                "final_answer": None,
+                "agent_used": None,
+                "reasoning": None
+            }
+            
+            print(f"Processing question: {question}")
+            if len(file_name)>0:
+                print(f"File detected: {file_name} (type: {file_type})")
+            
+            # Run the graph
+            config = {"configurable": {"thread_id": task_id}}
+            result = self.graph.invoke(initial_state, config=config)
+            
+            final_answer = result.get("final_answer", "No answer generated")
+            agent_used = result.get("agent_used", "unknown")
+            reasoning = result.get("reasoning", "")
+            
+            print(f"Agent used: {agent_used}")
+            print(f"Reasoning: {reasoning}")
+            print(f"Final answer: {final_answer}")
+            print("=" * 80)
+            
+            return final_answer
+            
+        except Exception as e:
+            print(f"Error in LangGraphAgent.__call__: {e}")
+            return f"Error processing question: {str(e)}"
+
+# ------------------------------
+# Gradio Interface Functions
+# ------------------------------
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the LangGraphAgent on them, submits all answers,
+    and displays the results.
+    """
+    space_id = os.getenv("SPACE_ID")
+
+    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
+    try:
+        agent = LangGraphAgent()
+        print("LangGraphAgent instantiated successfully.")
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    
+    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 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")
+        file_name = item.get("file_name", "")
+        
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+            
+        try:
+            file_type = Path(file_name).suffix.lower().split("?")[0] if len(file_name)>0 else None
+            
+            # Call the agent
+            submitted_answer = agent(question_text, task_id, file_name, file_type)
+            
+            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
+
+# ------------------------------
+# Gradio Interface
+# ------------------------------
+
+# --- 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)
+    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)

requirements.txt

@@ -0,0 +1,18 @@
+gradio
+requests
+torch==2.7.0
+wikipedia==1.4.0
+openpyxl==3.1.5
+python-docx==1.1.2
+youtube_transcript_api==1.0.3
+SpeechRecognition==3.14.3
+pdfplumber==0.11.6
+docx==0.2.4
+beautifulsoup4
+python-dotenv
+gradio[oauth]
+langchain==0.3.25 
+langchain-openai==0.3.18
+langgraph==0.4.7
+langchain-community==0.3.24
+duckduckgo-search