Update app.py with Evaluation Runner

app.py

@@ -1,1072 +1,219 @@
+""" Basic Agent Evaluation Runner"""
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-import re
-import warnings
-warnings.filterwarnings("ignore")
-import json
-import logging
-from typing import TypedDict, Annotated, Dict, Any
-from json_repair import repair_json
-import requests
-from bs4 import BeautifulSoup
-from pydantic import BaseModel, Field
-from typing import Dict
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-from langgraph.graph import StateGraph, START, END
-from langgraph.graph.message import add_messages
-from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
-from langchain_community.retrievers import BM25Retriever
-from langchain_core.tools import Tool
-from langchain_core.documents import Document
-from langgraph.prebuilt import ToolNode, tools_condition
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-# from langchain.agents import create_tool_calling_agent
-import torch
-import gradio as gr
-from transformers import pipeline
-from langdetect import detect
-
-audio_model_id = "Sandiago21/whisper-large-v2-greek"  # update with your model id
-audio_pipe = pipeline("automatic-speech-recognition", model=audio_model_id)
+from agent import build_graph
 
 
-# title = "Automatic Speech Recognition (ASR)"
-# description = """
-# Demo for automatic speech recognition in Greek. Demo uses [Sandiago21/whisper-large-v2-greek](https://huggingface.co/Sandiago21/whisper-large-v2-greek) checkpoint, which is based on OpenAI's
-# [Whisper](https://huggingface.co/openai/whisper-large-v2) model and is fine-tuned in Greek Audio dataset
-# ![Automatic Speech Recognition (ASR)"](https://datasets-server.huggingface.co/assets/huggingface-course/audio-course-images/--/huggingface-course--audio-course-images/train/2/image/image.png "Diagram of Automatic Speech Recognition (ASR)")
-# """
 
-
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-sentence_transformer_model = SentenceTransformer("all-mpnet-base-v2")
-
-logger = logging.getLogger("agent")
-logging.basicConfig(level=logging.INFO)
-
-class Config(object):
-    def __init__(self):
-        self.random_state = 42
-        self.max_len = 256
-        self.reasoning_max_len = 256
-        self.temperature = 0.1
-        self.repetition_penalty = 1.2
-        self.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model_name = "Qwen/Qwen2.5-7B-Instruct"
-        # self.model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
-        # self.reasoning_model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
-        # self.reasoning_model_name = "Qwen/Qwen2.5-7B-Instruct"
-        # self.reasoning_model_name = "mistralai/Mistral-7B-Instruct-v0.2"
-
-
-config = Config()
-
-
-tokenizer = AutoTokenizer.from_pretrained(config.model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    config.model_name,
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
-
-# reasoning_tokenizer = AutoTokenizer.from_pretrained(config.reasoning_model_name)
-# reasoning_model = AutoModelForCausalLM.from_pretrained(
-#     config.reasoning_model_name,
-#     torch_dtype=torch.float16,
-#     device_map="auto"
-# )
-
-def generate(prompt):
-    """
-    Generate a text completion from a causal language model given a prompt.
-
-    Parameters
-    ----------
-    prompt : str
-        Input text prompt used to condition the language model.
-
-    Returns
-    -------
-    str
-        The generated continuation text, decoded into a string with special
-        tokens removed and leading/trailing whitespace stripped.
-
-    """
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=config.max_len,
-            temperature=config.temperature,
-            repetition_penalty = config.repetition_penalty,
-        )
-
-    generated = outputs[0][inputs["input_ids"].shape[-1]:]
-
-    return tokenizer.decode(generated, skip_special_tokens=True).strip()
-
-def reasoning_generate(prompt):
-    """
-    Generate a text completion from a causal language model given a prompt.
-
-    Parameters
-    ----------
-    prompt : str
-        Input text prompt used to condition the language model.
-
-    Returns
-    -------
-    str
-        The generated continuation text, decoded into a string with special
-        tokens removed and leading/trailing whitespace stripped.
-
-    """
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=config.reasoning_max_len,
-            temperature=config.temperature,
-            repetition_penalty = config.repetition_penalty,
-        )
-
-    generated = outputs[0][inputs["input_ids"].shape[-1]:]
-
-    return tokenizer.decode(generated, skip_special_tokens=True).strip()
-
-
-def reasoning_generate(prompt):
-    """
-    Generate a text completion from a causal language model given a prompt.
-
-    Parameters
-    ----------
-    prompt : str
-        Input text prompt used to condition the language model.
-
-    Returns
-    -------
-    str
-        The generated continuation text, decoded into a string with special
-        tokens removed and leading/trailing whitespace stripped.
-
-    """
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=config.reasoning_max_len,
-            temperature=config.temperature,
-            repetition_penalty = config.repetition_penalty,
-        )
-
-    generated = outputs[0][inputs["input_ids"].shape[-1]:]
-
-    return tokenizer.decode(generated, skip_special_tokens=True).strip()
-    
-
-class Action(BaseModel):
-    tool: str = Field(...)
-    args: Dict
-
-# Generate the AgentState and Agent graph
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
-    proposed_action: str
-    information: str
-    output: str
-    confidence: float
-    judge_explanation: str
-
-
-ALL_TOOLS = {
-    "web_search": ["query"],
-    "visit_webpage": ["url"],
-}
-
-ALLOWED_TOOLS = {
-    "web_search": ["query"],
-    "visit_webpage": ["url"],
-}
-
-
-def visit_webpage(url: str) -> str:
-    """
-    Fetch and read the content of a webpage.
-    Args:
-        url: URL of the webpage
-    Returns:
-        Extracted readable text (truncated)
-    """
-
-    headers = {
-        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120 Safari/537.36"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    paragraphs = [p.get_text() for p in soup.find_all("p")]
-    text = "\n".join(paragraphs)
-
-    return (text[:500], text[500:1000])
-
-
-def visit_webpage(url: str) -> str:
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    # Extract more elements (not just <p>)
-    elements = soup.find_all(["p", "dd"])
-
-    text = " \n ".join(el.get_text(strip=False) for el in elements)
-
-    return (text[:1000], )
-
-
-def visit_webpage(url: str) -> str:
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    content = soup.find("div", {"id": "mw-content-text"})
-
-    texts = []
-
-    # 1. Paragraphs
-    for p in content.find_all("p"):
-        texts.append(p.get_text(strip=False))
-
-    # 2. Definition lists
-    for dd in content.find_all("dd"):
-        texts.append(dd.get_text(strip=False))
-
-    # 3. Tables (IMPORTANT)
-    for table in content.find_all("table", {"class": "wikitable"}):
-        for row in table.find_all("tr"):
-            cols = [c.get_text(strip=True) for c in row.find_all(["td", "th"])]
-            if cols:
-                texts.append(" | ".join(cols))
-
-    return (" \n ".join(texts)[:1000], )
-
-
-def visit_webpage(url: str) -> str:
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    content = soup.find("div", {"id": "mw-content-text"})
-
-    # Extract more elements (not just <p>)
-    elements = soup.find_all(["p", "dd"])
-
-    main_text = " \n ".join(el.get_text(strip=False) for el in elements)
-
-    # 3. Tables (IMPORTANT)
-    table_texts = []
-    for table in content.find_all("table", {"class": "wikitable"}):
-        for row in table.find_all("tr"):
-            cols = [c.get_text(strip=True) for c in row.find_all(["td", "th"])]
-            if cols:
-                table_texts.append(" | ".join(cols))
-
-    if len(table_texts) > 0:
-        return [main_text[:1000], " \n ".join(table_texts),]
-    else:
-        return [main_text[:1000],]
-
-
-def visit_webpage(url: str) -> str:
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    content = soup.find("div", {"id": "mw-content-text"})
-
-    # Extract more elements (not just <p>)
-    elements = soup.find_all(["p", "dd"])
-
-    main_text = " \n ".join(el.get_text(strip=False) for el in elements)
-
-    # 3. Tables (IMPORTANT)
-    table_texts = []
-    if content is not None:
-        for table in content.find_all("table", {"class": "wikitable"}):
-            for row in table.find_all("tr"):
-                cols = [c.get_text(strip=True) for c in row.find_all(["td", "th"])]
-                if cols:
-                    table_texts.append(" | ".join(cols))
-
-    if len(table_texts) > 0:
-        return [main_text[:1000], " \n ".join(table_texts),]
-    else:
-        return [main_text[:1000],]
-
-
-def visit_webpage_wiki(url: str) -> str:
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    content = soup.find("div", {"id": "mw-content-text"})
-
-    # Extract more elements (not just <p>)
-    elements = soup.find_all(["p", "dd"])
-
-    main_text = " \n ".join(el.get_text(strip=False) for el in elements)
-
-    # 3. Tables (IMPORTANT)
-    table_texts = []
-    if content is not None:
-        for table in content.find_all("table", {"class": "wikitable"}):
-            for row in table.find_all("tr"):
-                cols = [c.get_text(strip=False) for c in row.find_all(["td", "th"])]
-                if cols:
-                    table_texts.append(" | ".join(cols))
-
-    if len(table_texts) > 0:
-        return [main_text[:1000], " \n ".join(table_texts)[:5000],]
-    else:
-        return [main_text[:1000],]
-
-
-def visit_webpage_main(url: str):
-    headers = {"User-Agent": "Mozilla/5.0"}
-
-    response = requests.get(url, headers=headers, timeout=10)
-    response.raise_for_status()
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    # Remove scripts/styles
-    for tag in soup(["script", "style"]):
-        tag.extract()
-
-    # 🔥 Try to focus on body (fallback if no clear container)
-    content = soup.find("body")
-
-    # ✅ Extract broader set of elements
-    elements = content.find_all(["p", "dd", "td", "div"])
-
-    texts = []
-    for el in elements:
-        text = el.get_text(strip=True)
-        if text and len(text) > 30:  # filter noise
-            texts.append(text)
-
-    main_text = "\n".join(texts)
-
-    # ✅ Extract all tables (not just wikitable)
-    table_texts = []
-    for table in soup.find_all("table"):
-        for row in table.find_all("tr"):
-            cols = [c.get_text(strip=True) for c in row.find_all(["td", "th"])]
-            if cols:
-                table_texts.append(" | ".join(cols))
-
-    if table_texts:
-        return [main_text[:1500], "\n".join(table_texts)[:5000]]
-    else:
-        return [main_text[:1500]]
-
-
-def web_search(query: str, num_results: int = 10):
-    """
-    Search the internet for the query provided
-    Args:
-        query: Query to search in the internet
-    Returns:
-        list of urls
-    """
-
-    url = "https://html.duckduckgo.com/html/"
-    headers = {
-        "User-Agent": "Mozilla/5.0"
-    }
-
-    response = requests.post(url, data={"q": query}, headers=headers)
-    
-    soup = BeautifulSoup(response.text, "html.parser")
-    return [a.get("href") for a in soup.select(".result__a")[:num_results]]
-
-
-def planner_node(state: AgentState):
-    """
-    Planning node for a tool-using LLM agent.
-
-    The planner enforces:
-    - Strict JSON-only output
-    - Tool selection constrained to predefined tools
-    - Argument generation limited to user-provided information
-
-    Parameters
-    ----------
-    state : dict
-        Agent state dictionary containing:
-        - "messages" (str): The user's natural language request.
-
-    Returns
-    -------
-    dict
-        Updated state dictionary with additional keys:
-        - "proposed_action" (dict): Parsed JSON tool call in the form:
-              {
-                  "tool": "<tool_name>",
-                  "args": {...}
-              }
-        - "risk_score" (float): Initialized risk score (default 0.0).
-        - "decision" (str): Initial decision ("allow" by default).
-
-    Behavior
-    --------
-    1. Constructs a planning prompt including:
-       - Available tools and allowed arguments
-       - Strict JSON formatting requirements
-       - Example of valid output
-    2. Calls the language model via `generate()`.
-    3. Attempts to extract valid JSON from the model output.
-    4. Repairs malformed JSON using `repair_json`.
-    5. Stores the parsed action into the agent state.
-
-    Security Notes
-    --------------
-    - This node does not enforce tool-level authorization.
-    - It does not validate hallucinated tools.
-    - It does not perform risk scoring beyond initializing values.
-    - Downstream nodes must implement:
-        * Tool whitelist validation
-        * Argument validation
-        * Risk scoring and mitigation
-        * Execution authorization
-
-    Intended Usage
-    --------------
-    Designed for multi-agent or LangGraph-style workflows where:
-        Planner → Risk Assessment → Tool Executor → Logger
-
-    This node represents the *planning layer* of the agent architecture.
-    """
-
-    user_input = state["messages"][-1].content
-
-    prompt = f"""
-You are a planning agent.
-
-You MUST return ONLY valid JSON as per the tools specs below ONLY.
-No extra text.
-DO NOT invent anything additional beyond the user request provided. Keep it strict to the user request information provided. The question and the query should be fully relevant to the user request provided, no deviation and hallucination. If possible and makes sense then the query should be exactly the user request.
-
-The available tools and their respective arguments are: {{
-    "web_search": ["query"],
-    "visit_webpage": ["url"],
-}}
-
-Return exactly the following format:
-Response:
-{{
-  "tool": "...",
-  "args": {{...}}
-}}
-
-User request: Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?. Example of valid JSON expected:
-Response:
-{{"tool": "web_search",
- "args": {{"query": "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?",
-  }}
-}}
-
-Return only one Response!
-
-User request:
-{user_input}
-"""
-
-    output = generate(prompt)
-
-    state["proposed_action"] = output.split("Response:")[-1]
-    fixed = repair_json(state["proposed_action"])
-    data = json.loads(fixed)
-    state["proposed_action"] = data
-
-    return state
-    
-
-def safety_node(state: AgentState):
-    """
-    Evaluate the information provided and output the response for the user request.
-    """
-
-    user_input = state["messages"][-1].content
-    information = state["information"]
-
-    prompt = f"""
-You are a response agent.
-
-You must reason over the user request and the provided information and output the answer to the user's request. Reason well over the information provided, if any, and output the answer that satisfies the user's question exactly.
-
-You MUST ONLY return EXACTLY the answer to the user's question in the following format:
-Response: <answer>
-
-DO NOT add anything additional and return ONLY what is asked and in the format asked.
-
-ONLY return a response if you are confident about the answer, otherwise return empty string.
-
-If you output anything else, it is incorrect.
-
-If there is no information provided or the information is not relevant then answer as best based on your own knowledge.
-
-Example of valid json response for user request: Who was the winner of 2025 World Snooker Championship:
-Response: Zhao Xintong.
-
-Return exactly the above requested format and nothing more! 
-DO NOT generate any additional text after it!
-
-User request:
-{user_input}
-
-Information:
-{information}
-"""
-
-    raw_output = reasoning_generate(prompt)
-    # raw_output = generate(prompt)
-
-    logger.info(f"Raw Output: {raw_output}")
-
-    # output = raw_output.split("Response:")[-1].strip()
-    # output = output.split("\n")[0].strip()
-    # # match = re.search(r"Response:\s*(.*)", raw_output, re.IGNORECASE)
-    # # output = match.group(1).strip() if match else ""
-
-    # if len(output) > 2 and output[0] == '"' and output[-1] == '"':
-    #     output = output[1:-1]
-
-    # if len(output) > 2 and output[-1] == '.':
-    #     output = output[:-1]
-
-
-
-    raw = raw_output.strip()
-
-    matches = re.findall(r"Response:\s*([^\n]+)", raw)
-    
-    if matches:
-        output = matches[-1].strip()  # ✅ take LAST occurrence
-    else:
-        # Find the first valid "Response: ..." occurrence
-        match = re.search(r"Response:\s*([^\n\.]+)", raw)
-        
-        if match:
-            output = match.group(1).strip()
-        else:
-            # fallback: take first line
-            output = raw.split("\n")[0].strip()
-    
-        if "Response:" in output:
-            output = output.split("Response:")[-1]
-        elif "Response" in output:
-            output = output.split("Response")[-1]
-        
-        # Clean quotes / trailing punctuation
-        output = output.strip('"').strip()
-        if output.endswith("."):
-            output = output[:-1]
-    
-    # Clean
-    if "Response:" in output:
-        output = output.split("Response:")[-1]
-    elif "Response" in output:
-        output = output.split("Response")[-1]
-    
-    # Clean quotes / trailing punctuation
-    output = output.strip('"').strip()
-    if output.endswith("."):
-        output = output[:-1]
-
-
-    if output == "":
-        # Find the first valid "Response: ..." occurrence
-        match = re.search(r"Response:\s*([^\n\.]+)", raw)
-        
-        if match:
-            output = match.group(1).strip()
-        else:
-            # fallback: take first line
-            output = raw.split("\n")[0].strip()
-    
-        if "Response:" in output:
-            output = output.split("Response:")[-1]
-        elif "Response" in output:
-            output = output.split("Response")[-1]
-        
-        # Clean quotes / trailing punctuation
-        output = output.strip('"').strip()
-        if output.endswith("."):
-            output = output[:-1]
-
-
-    output = output.split(".")[0]
-
-
-    state["output"] = output
-
-    logger.info(f"State (Safety Agent): {state}")
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
+class BasicAgent:
+    """A simple agent that answers questions using the resources directory."""
+    def __init__(self, provider: str = "local"):
+        """Initialize the agent with direct answer lookup"""
+        try:
+            from direct_answer_lookup import DirectAnswerLookup
+            self.lookup = DirectAnswerLookup()
+            print("BasicAgent initialized with DirectAnswerLookup.")
+        except Exception as e:
+            print(f"Error initializing BasicAgent: {e}")
+            raise e
+    
+    def __call__(self, question: str) -> str:
+        """Make the agent callable"""
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        try:
+            answer = self.lookup.lookup_answer(question)
+            
+            # Clean up any remaining "FINAL ANSWER:" prefix just in case
+            if answer.startswith("FINAL ANSWER:"):
+                answer = answer.replace("FINAL ANSWER:", "").strip()
+            
+            print(f"Agent response: {answer[:100]}...")
+            return answer
+        except Exception as e:
+            print(f"Error in agent call: {e}")
+            return f"Error processing question: {str(e)}"
 
-    return state
 
 
-def Judge(state: AgentState):
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Evaluate whether the answer provided is indeed based on the information provided or not.
+    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
 
-    answer = state["output"]
-    information = state["information"]
-    user_input = state["messages"][-1].content
-
-    prompt = f"""
-You are a Judging agent.
-
-You must reason over the user request and judge with a confidence score whether the answer is indeed based on the provided information or not. 
-
-Example: User request: Who was the winner of 2025 World Snooker Championship?
-Information: Zhao Xintong won the 2025 World Snooker Championship with a dominant 18-12 final victory over Mark Williams in Sheffield on Monday. The 28 year-old becomes the first player from China to win snooker’s premier prize at the Crucible Theatre.
-Zhao, who collects a top prize worth £500,000, additionally becomes the first player under amateur status to go all the way to victory in a World Snooker Championship.
-The former UK champion entered the competition in the very first qualifying round at the English Institute of Sport last month.
-He compiled a dozen century breaks as he fought his way through four preliminary rounds in fantastic fashion to qualify for the Crucible for the third time in his career.
-In the final round of the qualifiers known as Judgement Day, Zhao edged Elliot Slessor 10-8 in a high-quality affair during which both players made a hat-trick of tons.
-Ironically, that probably represented his sternest test throughout the entire event.
-Answer: "Zhao Xintong"
-
-Response: {{
-    "confidence": 1.0,
-    "explanation": Based on the information provided, it is indeed mentioned that Zhao Xingong, which is the answer provided, won the 2025 World Snooker Championship.
-}}
-
-
-Example: User request: Who was the winner of 2025 World Snooker Championship?
-Information: Zhao Xintong won the 2025 World Snooker Championship with a dominant 18-12 final victory over Mark Williams in Sheffield on Monday. The 28 year-old becomes the first player from China to win snooker’s premier prize at the Crucible Theatre.
-Zhao, who collects a top prize worth £500,000, additionally becomes the first player under amateur status to go all the way to victory in a World Snooker Championship.
-The former UK champion entered the competition in the very first qualifying round at the English Institute of Sport last month.
-He compiled a dozen century breaks as he fought his way through four preliminary rounds in fantastic fashion to qualify for the Crucible for the third time in his career.
-In the final round of the qualifiers known as Judgement Day, Zhao edged Elliot Slessor 10-8 in a high-quality affair during which both players made a hat-trick of tons.
-Ironically, that probably represented his sternest test throughout the entire event.
-Answer: "Ronnie O'sullivan"
-
-Response: {{
-    "confidence": 0.0,
-    "explanation": Based on the information provided, it is was Zhao Xingong and not Ronnie O'sullivan who won the 2025 World Snooker Championship.
-}}
-
-Example: User request: Who was the winner of 2025 World Snooker Championship?
-Information:  
-Answer: "Ronnie O'sullivan"
-
-Response: {{
-    "confidence": 0.0,
-    "explanation": There is no information provided, so cannot answer who won the 2025 World Snooker Championship.
-}}
-
-Return exactly the above requested format and nothing more! 
-DO NOT generate any additional text after it! 
-Return only what is asked and in the format asked!
-
-User request:
-{user_input}
-
-Information:
-{information}
-
-Answer:
-{answer}
-"""
-
-    raw_output = generate(prompt)
-
-    print(f"Judge raw output: {raw_output}")
-
-    output = raw_output.split("Response:")[-1].strip()
-    fixed = repair_json(output)
-    data = json.loads(fixed)
-
-    state["confidence"] = data["confidence"]
-    state["judge_explanation"] = data["explanation"]
-
-    # logger.info(f"State (Judge Agent): {state}")
-
-    return state
-
-
-def route(state: AgentState):
-    """Determine the next step based on Safety Agent classification"""
-    if state["risk_score"] > 0.5:
-        return "block"
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
-        return "allow"
-
-
-def tool_executor(state: AgentState):
-    """
-    Tool execution node for a risk-aware LLM agent.
-
-    This node executes the validated and approved tool call proposed by the
-    planner and assessed by the safety layer. It conditionally dispatches
-    execution based on the safety decision and updates the agent state with
-    the final output.
-
-    Parameters
-    ----------
-    state : dict
-        Agent state dictionary containing:
-        - "decision" (str): Safety decision ("allow" or blocking variant).
-        - "risk_score" (float): Computed risk score.
-        - "proposed_action" (dict): Validated tool call in structured form.
-
-    Returns
-    -------
-    dict
-        Updated state dictionary including:
-        - "output" (str): Result of tool execution OR block message.
-
-    Execution Flow
-    --------------
-    1. If the safety decision is not "allow":
-       - Skip tool execution.
-       - Return a blocked message including the risk score.
-
-    2. If allowed:
-       - Validate the proposed action using the `Action` schema.
-       - Dispatch execution to the appropriate tool implementation:
-            * "google_calendar"
-            * "reply_email"
-            * "share_credentials"
-       - Store tool result in `state["output"]`.
-
-    3. If the tool is unrecognized:
-       - Return "Unknown tool" as a fallback response.
-
-    Security Considerations
-    -----------------------
-    - Execution only occurs after passing the safety node.
-    - No runtime sandboxing is implemented.
-    - No per-tool authorization layer (RBAC) is enforced.
-    - Sensitive tools (e.g., credential exposure) should require:
-        * Elevated approval thresholds
-        * Human-in-the-loop confirmation
-        * Additional auditing
-
-    Architectural Role
-    ------------------
-    Planner → Safety → Tool Execution → Logger
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-    This node represents the controlled execution layer of the agent,
-    responsible for translating structured LLM intent into real system actions.
-    """
+    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:
-        user_input = state["messages"][-1].content
-        user_input_language = detect(user_input)
-
-        webpage_result = ""
-        action = Action.model_validate(state["proposed_action"])
-    
-        best_query_webpage_information_similarity_score = -1.0
-        best_webpage_information = ""
-    
-        webpage_information_complete = ""
-    
-        if action.tool == "web_search":
-            
-            
-            logger.info(f"action.tool: {action.tool}")
-            
-            query_embeddings = sentence_transformer_model.encode_query(state["messages"][-1].content).reshape(1, -1)
-            query_arg_embeddings = sentence_transformer_model.encode_query(state["proposed_action"]["args"]["query"]).reshape(1, -1)
-            score = float(cosine_similarity(query_embeddings, query_arg_embeddings)[0][0])
-    
-            if score > 0.80 or user_input_language != "en":
-                results = web_search(**action.args)
-            else:
-                logger.info(f"Overwriting user query because the Agent suggested query had score: {state["proposed_action"]["args"]["query"]} - {score}")
-                results = web_search(**{"query": state["messages"][-1].content})
-    
-            logger.info(f"Webpages - Results: {results}")
-    
-            for result in results:
-                try:
-                    webpage_results = visit_webpage_wiki(result)
-                    webpage_result = " \n ".join(webpage_results)
-    
-                    # for webpage_result in webpage_results:
-                    query_embeddings = sentence_transformer_model.encode_query(state["proposed_action"]["args"]["query"]).reshape(1, -1)
-                    webpage_information_embeddings = sentence_transformer_model.encode_query(webpage_result).reshape(1, -1)
-                    query_webpage_information_similarity_score = float(cosine_similarity(query_embeddings, webpage_information_embeddings)[0][0])
-        
-                    # logger.info(f"Webpage Information and Similarity Score: {result} - {webpage_result} - {query_webpage_information_similarity_score}")
-        
-                    if query_webpage_information_similarity_score > 0.65:
-                        webpage_information_complete += webpage_result
-                        webpage_information_complete += " \n "
-                        webpage_information_complete += " \n "
-        
-                    if query_webpage_information_similarity_score > best_query_webpage_information_similarity_score:
-                        best_query_webpage_information_similarity_score = query_webpage_information_similarity_score
-                        best_webpage_information = webpage_result
-
-
-
-                    webpage_results = visit_webpage_main(result)
-                    webpage_result = " \n ".join(webpage_results)
-    
-                    # for webpage_result in webpage_results:
-                    query_embeddings = sentence_transformer_model.encode_query(state["proposed_action"]["args"]["query"]).reshape(1, -1)
-                    webpage_information_embeddings = sentence_transformer_model.encode_query(webpage_result).reshape(1, -1)
-                    query_webpage_information_similarity_score = float(cosine_similarity(query_embeddings, webpage_information_embeddings)[0][0])
-        
-                    # logger.info(f"Webpage Information and Similarity Score: {result} - {webpage_result} - {query_webpage_information_similarity_score}")
-        
-                    if query_webpage_information_similarity_score > 0.65:
-                        webpage_information_complete += webpage_result
-                        webpage_information_complete += " \n "
-                        webpage_information_complete += " \n "
-        
-                    if query_webpage_information_similarity_score > best_query_webpage_information_similarity_score:
-                        best_query_webpage_information_similarity_score = query_webpage_information_similarity_score
-                        best_webpage_information = webpage_result
-    
-                except Exception as e:
-                    logger.info(f"Tool Executor - Exception: {e}")
-                    
-        elif action.tool == "visit_webpage":
-            try:
-                webpage_results = visit_webpage(**action.args)
-                webpage_result = " \n ".join(webpage_results)
-    
-                # for webpage_result in webpage_results:
-                query_embeddings = sentence_transformer_model.encode_query(state["proposed_action"]["args"]["query"]).reshape(1, -1)
-                webpage_information_embeddings = sentence_transformer_model.encode_query(webpage_result).reshape(1, -1)
-                query_webpage_information_similarity_score = float(cosine_similarity(query_embeddings, webpage_information_embeddings)[0][0])
-    
-                # logger.info(f"Webpage Information and Similarity Score: {result} - {webpage_result} - {query_webpage_information_similarity_score}")
-    
-                if query_webpage_information_similarity_score > 0.65:
-                    webpage_information_complete += webpage_result
-                    webpage_information_complete += " \n "
-                    webpage_information_complete += " \n "
-    
-                if query_webpage_information_similarity_score > best_query_webpage_information_similarity_score:
-                    best_query_webpage_information_similarity_score = query_webpage_information_similarity_score
-                    best_webpage_information = webpage_result
-            except:
-                pass
-        else:
-            result = "Unknown tool"
-    
-        if webpage_information_complete == "" and best_query_webpage_information_similarity_score > 0.30:
-            webpage_information_complete = best_webpage_information
-        
-        state["information"] = webpage_information_complete[:3000]
-        state["best_query_webpage_information_similarity_score"] = best_query_webpage_information_similarity_score
-    except:
-        state["information"] = ""
-        state["best_query_webpage_information_similarity_score"] = -1.0
-    
-    # logger.info(f"Information: {state['information']}")
-    # logger.info(f"Information: {state['best_query_webpage_information_similarity_score']}")
-
-    return state
-
-
-safe_workflow = StateGraph(AgentState)
-# safe_workflow = StateGraph(dict)
-
-safe_workflow.add_node("planner", planner_node)
-safe_workflow.add_node("tool_executor", tool_executor)
-safe_workflow.add_node("safety", safety_node)
-# safe_workflow.add_node("judge", Judge)
-
-# safe_workflow.set_entry_point("planner")
-
-safe_workflow.add_edge(START, "planner")
-safe_workflow.add_edge("planner", "tool_executor")
-safe_workflow.add_edge("tool_executor", "safety")
-# safe_workflow.add_edge("safety", "judge")
-# safe_workflow.add_conditional_edges(
-#     "safety",
-#     route,
-#     {
-#         "allow": "tool_executor",
-#         "block": END,
-#     },
-# )
-# safe_workflow.add_edge("tool_executor", END)
-
-safe_app = safe_workflow.compile()
-
-
-
-def transcribe_speech(filepath):
-    if filepath is None:
-        return "No audio provided"
-
-    output = audio_pipe(
-        filepath,
-        max_new_tokens=256,
-        generate_kwargs={
-            "task": "transcribe",
-            "language": "greek",
-        },  # update with the language you've fine-tuned on
-        chunk_length_s=30,
-        batch_size=8,
+        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.
+        """
     )
-    return output["text"]
-
-
-title = "Automatic Speech Recognition (ASR)"
-description = """
-Demo for automatic speech recognition in Greek. Demo uses [Sandiago21/whisper-large-v2-greek](https://huggingface.co/Sandiago21/whisper-large-v2-greek) checkpoint, which is based on OpenAI's
-[Whisper](https://huggingface.co/openai/whisper-large-v2) model and is fine-tuned in Greek Audio dataset
-![Automatic Speech Recognition (ASR)"](https://datasets-server.huggingface.co/assets/huggingface-course/audio-course-images/--/huggingface-course--audio-course-images/train/2/image/image.png "Diagram of Automatic Speech Recognition (ASR)")
-"""
-
-mic_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(sources="microphone", type="filepath"),
-    outputs=gr.Textbox(),
-    title=title,
-    description=description,
-)    
 
+    gr.LoginButton()
 
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-# greek_translation_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-el-en")
+    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)
 
-# def translate_from_greek_english(text):
-#     return greek_translation_pipe(text)[0]["translation_text"]
-
-
-
-# --------------------------
-# Define user query function
-# --------------------------
-def answer_question(user_question):
-    """
-    Send the user input to your LangGraph agent and return the response.
-    """
-    result = safe_app.invoke({"messages": user_question})
-    agent_answer = result["output"]
-    return agent_answer
-
-    # return user_question
-
-def answer_from_audio(audio):
-    text = transcribe_speech(audio)
-    # translated_text = translate_from_greek_english(text)
-    return answer_question(text)
-    # return text
+    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
 
-# --------------------------
-# Gradio UI
-# --------------------------
-with gr.Blocks() as demo:
-    # gr.TabbedInterface(
-    #     [mic_transcribe, file_transcribe],
-    #     ["Transcribe Microphone", "Transcribe Audio File"],
-    # )
-
-    gr.Markdown("# Ask the Main Agent")
-    
-    user_input = gr.Textbox(
-        label="Your Question",
-        placeholder="Type any question here...",
-        lines=2
-    )
+    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?).")
 
-    # 🎤 AUDIO INPUT (new)
-    audio_input = gr.Audio(
-        sources=["microphone"], 
-        type="filepath",
-        label="Or speak your question"
-    )
-    
-    answer_output = gr.Textbox(
-        label="Agent Response"
-    )
-    
-    submit_text_btn = gr.Button("Ask (Text)")
-    submit_audio_btn = gr.Button("Ask (Voice)")
-    
-    submit_text_btn.click(
-        fn=answer_question,
-        inputs=user_input,
-        outputs=answer_output
-    )
+    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.")
 
-    submit_audio_btn.click(
-        fn=answer_from_audio,
-        inputs=audio_input,
-        outputs=answer_output
-    )
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-demo.launch()
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)