Update app.py

app.py

@@ -1,129 +1,71 @@
-import os
-import gradio as gr
+import streamlit as st
 import requests
-import pandas as pd
 
-# --- Constants ---
-# ✅ correct backend API base URL
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+BASE_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# 👇 customize this class to make your own agent smarter
 class BasicAgent:
     def __init__(self):
-        print("✅ BasicAgent initialized.")
+        # You can initialize any tools, memory, or logic here
+        pass
 
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:50]}...")
-        # For now, it returns a placeholder answer
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning: {fixed_answer}")
-        return fixed_answer
-
-
-# --- Evaluation Logic ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """Fetches all questions, runs agent, submits answers, shows results."""
-    space_id = os.getenv("SPACE_ID")  # for linking to code repo
-
-    if profile:
-        username = f"{profile.username}"
-        print(f"👤 Logged in as: {username}")
-    else:
-        return "Please log in with your Hugging Face account.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # --- Instantiate your agent ---
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
+    def answer_question(self, question: str) -> str:
+        """
+        Implement your custom logic here.
+        For now, this just echoes the question.
+        """
+        return f"This is my answer to: {question}"
 
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "N/A"
-    print(f"🔗 Code link: {agent_code}")
+# Initialize Streamlit app
+st.set_page_config(page_title="Basic Agent Evaluation Runner", page_icon="🤖", layout="centered")
+st.title("🤖 Basic Agent Evaluation Runner")
 
-    # --- Fetch Questions ---
-    print(f"📡 Fetching from {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "No questions fetched.", None
-        print(f"✅ {len(questions_data)} questions retrieved.")
-    except Exception as e:
-        return f"Error fetching questions: {e}", None
+st.markdown("""
+### Instructions:
+1️⃣ Clone this space on your Hugging Face profile.  
+2️⃣ Modify the `BasicAgent` class with your logic.  
+3️⃣ Log in below and run evaluation.
+""")
 
-    # --- 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")
-        if not task_id or question_text is None:
-            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, "Answer": submitted_answer})
-        except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Answer": f"ERROR: {e}"})
+# --- UI for Hugging Face login ---
+with st.expander("🔐 Login"):
+    hf_token = st.text_input("Enter your Hugging Face token", type="password")
+    if hf_token:
+        st.success("✅ Token saved!")
 
-    if not answers_payload:
-        return "No answers produced by the agent.", pd.DataFrame(results_log)
+# --- Initialize agent ---
+agent = BasicAgent()
 
-    # --- Prepare Submission ---
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"🚀 Submitting {len(answers_payload)} answers...")
+# --- Fetch questions ---
+st.subheader("📋 Questions and Answers")
 
-    # --- Submit ---
+if st.button("🚀 Run Evaluation & Submit All Answers"):
+    st.info("Fetching questions...")
     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"Score: {result_data.get('score', 'N/A')}%\n"
-            f"Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        return final_status, pd.DataFrame(results_log)
+        response = requests.get(f"{BASE_URL}/questions")  # Correct endpoint
+        if response.status_code != 200:
+            st.error(f"Failed to fetch questions: {response.status_code} {response.reason}")
+        else:
+            data = response.json()
+            st.success(f"Fetched {len(data)} questions.")
+            answers = []
+
+            for q in data:
+                question = q.get("question", "")
+                task_id = q.get("task_id", "")
+                answer = agent.answer_question(question)
+                answers.append({
+                    "task_id": task_id,
+                    "answer": answer
+                })
+                st.write(f"**Q:** {question}")
+                st.write(f"**A:** {answer}")
+
+            # Submit answers
+            st.info("Submitting answers...")
+            submit_res = requests.post(f"{BASE_URL}/submit", json={"answers": answers})
+            if submit_res.status_code == 200:
+                st.success("✅ Submission complete! Check leaderboard or logs.")
+            else:
+                st.error(f"Submission failed: {submit_res.status_code}")
     except Exception as e:
-        return f"Submission failed: {e}", pd.DataFrame(results_log)
-
-
-# --- Build Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        ### Instructions
-        1️⃣ Clone this space on your Hugging Face profile.  
-        2️⃣ Modify the `BasicAgent` class to add your logic.  
-        3️⃣ Log in below, then click **Run Evaluation & Submit All Answers**.  
-
-        ---
-        The process might take a few minutes while the agent runs all questions.  
-        You can enhance your agent with reasoning, web tools, or retrieval modules.
-        """
-    )
-
-    gr.LoginButton()
-    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
-    results_table = gr.DataFrame(label="🧾 Questions and Agent Answers")
-
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
-
-# --- Run ---
-if __name__ == "__main__":
-    print("\n" + "-" * 40)
-    print("🌐 App Starting")
-    print("-" * 40)
-    demo.launch(debug=True, share=False)
+        st.error(f"Error: {str(e)}")