Update app.py

app.py

@@ -1,200 +1,6 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-from bs4 import BeautifulSoup
-import re
-
-# Constants
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Generalized Tools ---
-class KnowledgeExtractionTool:
-    def __init__(self):
-        self.api_url = DEFAULT_API_URL
-    
-    def fetch_data(self, url):
-        """Fetches data from a URL (Wikipedia or other sources)"""
-        response = requests.get(url)
-        if response.status_code == 200:
-            return response.text
-        return None
-    
-    def extract_winners_from_wikipedia(self, url):
-        """Extracts competition winners from a Wikipedia page"""
-        page_content = self.fetch_data(url)
-        if not page_content:
-            return []
-        
-        soup = BeautifulSoup(page_content, 'html.parser')
-        winners_data = []
-        
-        # Look for the table of winners
-        winners_table = soup.find('table', {'class': 'wikitable'})
-        if winners_table:
-            for row in winners_table.find_all('tr')[1:]:  # Skipping the header row
-                cells = row.find_all('td')
-                if len(cells) > 3:
-                    name = cells[0].text.strip()  # Winner's name
-                    year = int(cells[1].text.strip())  # Year
-                    nationality = cells[3].text.strip()  # Nationality
-                    winners_data.append({"name": name, "year": year, "nationality": nationality})
-        return winners_data
-    
-    def filter_winners(self, winners_data, year_threshold=1977, countries_to_check=None):
-        """Filters winners by year and nationality (if country no longer exists)"""
-        if not countries_to_check:
-            countries_to_check = ["Yugoslavia", "Soviet Union", "East Germany"]  # Example list
-        
-        filtered_winners = []
-        for winner in winners_data:
-            if winner["year"] > year_threshold and any(country in winner["nationality"] for country in countries_to_check):
-                filtered_winners.append(winner)
-        return filtered_winners
-
-    def extract_first_name(self, full_name):
-        """Extract the first name from a full name string"""
-        return full_name.split()[0] if full_name else None
-
-    def process_question(self, question):
-        """Generalized function to process a wide variety of questions"""
-        if "Malko Competition" in question and "first name" in question:
-            print("Processing Malko Competition query...")
-
-            # Fetch data from Wikipedia (or other sources)
-            winners_data = self.extract_winners_from_wikipedia("https://en.wikipedia.org/wiki/Malko_Competition")
-            
-            # Filter by year and nationality of winners
-            filtered_winners = self.filter_winners(winners_data)
-
-            # Extract and return the first name
-            if filtered_winners:
-                first_name = self.extract_first_name(filtered_winners[0]["name"])
-                return f"The first name of the winner is: {first_name}"
-            else:
-                return "No winners found with the specified conditions."
-        
-        # Add other types of questions and their handling here as needed
-        return "Question could not be processed."
-
-# --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self):
-        self.knowledge_tool = KnowledgeExtractionTool()
-    
-    def __call__(self, question: str) -> str:
-        print(f"Processing question: {question}")
-        return self.knowledge_tool.process_question(question)
-
-# --- Run and Submit All Function ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    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"
-
-    # Instantiate Agent
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    # 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
-
-    # Run agent on each question
-    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)
-
-    # Prepare and submit answers
-    submission_data = {"username": username.strip(), "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main", "answers": answers_payload}
-    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.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
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Generalized Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1. Clone this space and modify the code to define your agent's logic, tools, and packages.
-        2. Log in to your Hugging Face account using the button below.
-        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        """
-    )
-
-    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__":
-    demo.launch(debug=True, share=False)
+login_btn = gr.LoginButton()
+run_btn.click(
+    fn=run_and_submit_all,
+    inputs=[login_btn],    # pass the profile from LoginButton
+    outputs=[status_out, results_tbl]
+)