Update app.py

app.py

@@ -2,122 +2,132 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from datasets import load_dataset
-from duckduckgo_search import DDGS
-from llama_index.llms.huggingface import HuggingFaceLLM
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
 from huggingface_hub import InferenceClient
+from duckduckgo_search import DDGS
+from datasets import load_dataset
 import wikipediaapi
 
 # Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_TOKEN = os.environ.get("HF_TOKEN")
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Advanced LLM via Hugging Face Inference API
+# Setup Hugging Face client (advanced model)
 llm_model_id = "deepseek-ai/DeepSeek-R1"
 hf_client = InferenceClient(llm_model_id, token=HF_TOKEN)
 
-# Wikipedia API setup
-wiki_api = wikipediaapi.Wikipedia('en')
-
-# Load Wikipedia dataset from Hugging Face
-wiki_dataset = load_dataset(
-    "wikipedia", "20220301.en", split="train[:10000]", trust_remote_code=True
+# Wikipedia API setup (corrected user-agent)
+wiki_api = wikipediaapi.Wikipedia(
+    language='en', 
+    user_agent='SmartAgent/1.0 ([email protected])'
 )
 
-# DuckDuckGo search function
+# Load a subset of Wikipedia dataset (adjust as needed)
+wiki_dataset = load_dataset("wikipedia", "20220301.en", split="train[:10000]", trust_remote_code=True)
+
+# Search functions
 def duckduckgo_search(query):
     with DDGS() as ddgs:
         results = [r for r in ddgs.text(query, max_results=3)]
-        if results:
-            return "\n".join([r["body"] for r in results if r.get("body")])
-        else:
-            return "No results found."
+        return "\n".join([r["body"] for r in results if r.get("body")]) or "No results found."
+
+def wikipedia_search(query):
+    page = wiki_api.page(query)
+    return page.summary if page.exists() else "No Wikipedia page found."
 
-# Smart Agent combining multiple sources
+# Comprehensive Agent
 class SmartAgent:
     def __init__(self):
-        service_context = ServiceContext.from_defaults(
-            llm=HuggingFaceLLM(model_name=llm_model_id, token=HF_TOKEN)
-        )
-        docs = [doc["text"] for doc in wiki_dataset]
-        self.index = VectorStoreIndex.from_documents(
-            [SimpleDirectoryReader.input_to_document(doc) for doc in docs],
-            service_context=service_context,
-            show_progress=True
-        )
-        self.query_engine = self.index.as_query_engine()
+        pass
 
     def __call__(self, question: str) -> str:
-        question_lower = question.lower()
+        q_lower = question.lower()
 
-        # Use DuckDuckGo for recent events, dates, or temporal queries
-        if any(term in question_lower for term in ["current", "latest", "2024", "2025", "recent", "today", "president"]):
+        if any(term in q_lower for term in ["current", "latest", "2024", "2025", "who is the president", "recent", "live"]):
             return duckduckgo_search(question)
 
-        # Check if Wikipedia page exists for topic
-        page = wiki_api.page(question)
-        if page.exists():
-            return page.summary[:1000] + "..."
+        wiki_result = wikipedia_search(question)
+        if "No Wikipedia page found" not in wiki_result:
+            return wiki_result
 
-        # Fallback to indexed Wikipedia with RAG
         try:
-            response = self.query_engine.query(question)
-            return str(response)
+            resp = hf_client.text_generation(question, max_new_tokens=512)
+            return resp
         except Exception as e:
-            return f"LLM query error: {e}"
+            return f"HF LLM error: {e}"
 
-# Run and submit evaluation
+# Submission logic
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
     if profile:
-        username = f"{profile.username}"
+        username = profile.username
+        print(f"User logged in: {username}")
     else:
         return "Please Login to Hugging Face with the button.", None
 
-    questions_url = f"{DEFAULT_API_URL}/questions"
-    submit_url = f"{DEFAULT_API_URL}/submit"
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-    # Instantiate agent
     agent = SmartAgent()
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
-    # Fetch questions
     try:
-        questions_data = requests.get(questions_url).json()
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    results_log, answers_payload = [], []
+    results_log = []
+    answers_payload = []
+    correct_answers = 0
+
     for item in questions_data:
-        task_id, question_text = item.get("task_id"), item.get("question")
-        answer = agent(question_text)
-        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-        results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or not question_text:
+            continue
 
-    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+        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})
+
+    if not answers_payload:
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
 
     try:
-        result_data = requests.post(submit_url, json=submission_data).json()
+        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"
-            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')}) correct\n"
-            f"Message: {result_data.get('message')}"
+            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.')}"
         )
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
     except Exception as e:
         return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-# Gradio interface setup
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🚀 Smart Multi-Source Agent Evaluation")
+    gr.Markdown("# Smart Agent Evaluation Runner")
+    gr.Markdown("""
+        **Instructions:**
+        1. Clone this space, define your agent logic, tools, packages, etc.
+        2. Log in to Hugging Face.
+        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="Status & Results", lines=6, interactive=False)
-    results_table = gr.DataFrame(label="Agent 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])