final agent course submit

app.py

@@ -8,308 +8,127 @@ Features:
 - Beautiful UI với LangGraph workflow visualization
 """
 
+import os
 import gradio as gr
 import requests
-import json
-from agent import process_question
+import pandas as pd
 
-# =============================================================================
-# API FUNCTIONS
-# =============================================================================
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
-def get_questions():
-    """Fetch questions from API"""
-    try:
-        response = requests.get("https://agents-course-unit4-scoring.hf.space/questions")
-        if response.status_code == 200:
-            return response.json()
-        else:
-            return {"error": f"API error: {response.status_code}"}
-    except Exception as e:
-        return {"error": f"Request failed: {str(e)}"}
 
-def submit_answers(username: str, agent_code: str, answers: dict):
-    """Submit answers to API"""
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    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")
+
+    if profile:
+        username = 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:
-        payload = {
-            "username": username,
-            "agent_code": agent_code,
-            "answers": answers
-        }
-        
-        response = requests.post(
-            "https://agents-course-unit4-scoring.hf.space/submit",
-            json=payload,
-            headers={"Content-Type": "application/json"}
-        )
-        
-        if response.status_code == 200:
-            return response.json()
-        else:
-            return {"error": f"Submit failed: {response.status_code}"}
+        agent = BasicAgent()
     except Exception as e:
-        return {"error": f"Submit error: {str(e)}"}
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
 
-# =============================================================================
-# GRADIO INTERFACE FUNCTIONS
-# =============================================================================
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
 
-def answer_question(question: str, task_id: str = "", file_name: str = ""):
-    """Process single question through AI agent"""
-    if not question.strip():
-        return "Please enter a question"
-    
+    # Fetch Questions
     try:
-        # Process through LangGraph AI agent
-        answer = process_question(question, task_id, file_name)
-        return answer
+        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.", None
+        print(f"Fetched {len(questions_data)} questions.")
     except Exception as e:
-        return f"Error: {str(e)}"
-
-def load_all_questions():
-    """Load and display all questions from API"""
-    questions_data = get_questions()
-    
-    if "error" in questions_data:
-        return f"Error loading questions: {questions_data['error']}"
-    
-    # Format questions for display
-    formatted = "📋 **ALL QUESTIONS FROM API:**\n\n"
-    
-    for i, q_data in enumerate(questions_data, 1):
-        question = q_data.get('question', 'No question')
-        task_id = q_data.get('task_id', '')
-        file_name = q_data.get('file_name', '')
-        
-        formatted += f"**Question {i}:**\n"
-        formatted += f"Q: {question}\n"
-        if task_id:
-            formatted += f"Task ID: `{task_id}`\n"
-        if file_name:
-            formatted += f"File: `{file_name}`\n"
-        formatted += f"\n---\n\n"
-    
-    return formatted
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
 
-def process_all_questions(username: str):
-    """Process all questions and prepare for submission"""
-    if not username.strip():
-        return "Please enter your username first"
-    
-    # Get questions
-    questions_data = get_questions()
-    if "error" in questions_data:
-        return f"Error: {questions_data['error']}"
-    
-    # Process each question
-    results = []
-    answers = {}
-    
-    for i, q_data in enumerate(questions_data, 1):
-        question = q_data.get('question', '')
-        task_id = q_data.get('task_id', '')
-        file_name = q_data.get('file_name', '')
-        
-        # Process through agent
+    # Run Agent on each question
+    results_log = []
+    answers_payload = []
+    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:
-            answer = process_question(question, task_id, file_name)
-            answers[str(i)] = answer
-            
-            results.append(f"**Q{i}:** {question}")
-            results.append(f"**A{i}:** {answer}")
-            results.append("---")
-            
+            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:
-            error_msg = f"Error processing Q{i}: {str(e)}"
-            answers[str(i)] = error_msg
-            results.append(f"**Q{i}:** {question}")
-            results.append(f"**A{i}:** {error_msg}")
-            results.append("---")
-    
-    # Format results
-    formatted_results = "\n".join(results)
-    
-    # Save answers for potential submission
-    global cached_answers, cached_username
-    cached_answers = answers
-    cached_username = username
-    
-    return f"🤖 **PROCESSING COMPLETED FOR {username}**\n\n{formatted_results}\n\n✅ **Ready for submission!**"
+            print(f"Error on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})
 
-def submit_all_answers():
-    """Submit all processed answers"""
-    global cached_answers, cached_username
-    
-    if not cached_answers or not cached_username:
-        return "No answers to submit. Please process questions first."
-    
-    # Submit to API
-    result = submit_answers(
-        username=cached_username,
-        agent_code="LangGraphQwen3Agent", 
-        answers=cached_answers
-    )
-    
-    if "error" in result:
-        return f"❌ Submission failed: {result['error']}"
-    else:
-        score = result.get('score', 'Unknown')
-        return f"🎉 **SUBMISSION SUCCESSFUL!**\n\nScore: {score}\nResult: {json.dumps(result, indent=2)}"
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "No answers to submit.", pd.DataFrame(results_log)
+
+    # Submit answers
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    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"Message: {result_data.get('message', '')}"
+        )
+        return final_status, pd.DataFrame(results_log)
+    except Exception as e:
+        print(f"Submission failed: {e}")
+        return f"Submission failed: {e}", pd.DataFrame(results_log)
 
-# Global variables for caching
-cached_answers = {}
-cached_username = ""
+# --- Build Gradio Interface ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
 
-# =============================================================================
-# GRADIO APP
-# =============================================================================
+        1. Log in with your Hugging Face account using the button below.
+        2. Click 'Run Evaluation & Submit All Answers' to fetch, run, and submit.
+        """
+    )
 
-def create_app():
-    """Create Gradio interface"""
-    
-    with gr.Blocks(title="🤖 LangGraph AI Agent", theme=gr.themes.Soft()) as app:
-        
-        gr.Markdown("""
-        # 🤖 LangGraph AI Agent with Qwen3-8B
-        
-        **AI-Driven Tool Calling Agent for Hugging Face Agents Course**
-        
-        🧠 **Brain:** Qwen3-8B via HuggingFace InferenceClient  
-        🔧 **Tools:** Audio transcription, OCR, file reading, Wikipedia search, math calculation, YouTube extraction  
-        🌐 **Architecture:** LangGraph workflow với dynamic AI routing  
-        ⚡ **Features:** Không hardcode - AI tự quyết định tool nào cần dùng!
-        """)
-        
-        with gr.Tabs():
-            
-            # Tab 1: Single Question Testing
-            with gr.Tab("🔍 Single Question Test"):
-                gr.Markdown("### Test individual questions with the AI agent")
-                
-                with gr.Row():
-                    with gr.Column():
-                        question_input = gr.Textbox(
-                            label="Question",
-                            placeholder="Enter your question here...",
-                            lines=2
-                        )
-                        task_id_input = gr.Textbox(
-                            label="Task ID (optional)",
-                            placeholder="e.g., task_123"
-                        )
-                        file_name_input = gr.Textbox(
-                            label="File Name (optional)", 
-                            placeholder="e.g., audio.mp3"
-                        )
-                        
-                        answer_btn = gr.Button("🤖 Get Answer", variant="primary")
-                    
-                    with gr.Column():
-                        answer_output = gr.Textbox(
-                            label="AI Agent Answer",
-                            lines=10,
-                            interactive=False
-                        )
-                
-                answer_btn.click(
-                    answer_question,
-                    inputs=[question_input, task_id_input, file_name_input],
-                    outputs=answer_output
-                )
-            
-            # Tab 2: View All Questions
-            with gr.Tab("📋 All Questions"):
-                gr.Markdown("### View all questions from the API")
-                
-                load_questions_btn = gr.Button("📥 Load All Questions", variant="secondary")
-                questions_display = gr.Markdown()
-                
-                load_questions_btn.click(
-                    load_all_questions,
-                    outputs=questions_display
-                )
-            
-            # Tab 3: Full Processing & Submission
-            with gr.Tab("🚀 Full Processing"):
-                gr.Markdown("### Process all questions and submit for scoring")
-                
-                with gr.Row():
-                    username_input = gr.Textbox(
-                        label="Username",
-                        placeholder="Enter your username",
-                        value=""
-                    )
-                
-                with gr.Row():
-                    process_all_btn = gr.Button("🔄 Process All Questions", variant="primary")
-                    submit_btn = gr.Button("📤 Submit Answers", variant="secondary")
-                
-                results_output = gr.Markdown()
-                
-                process_all_btn.click(
-                    process_all_questions,
-                    inputs=username_input,
-                    outputs=results_output
-                )
-                
-                submit_btn.click(
-                    submit_all_answers,
-                    outputs=results_output
-                )
-            
-            # Tab 4: Agent Info
-            with gr.Tab("ℹ️ Agent Info"):
-                gr.Markdown("""
-                ### 🤖 LangGraph AI Agent Architecture
-                
-                **Main Components:**
-                - **🧠 Qwen3-8B Brain:** HuggingFace InferenceClient cho reasoning
-                - **🔧 LangGraph Workflow:** Dynamic routing based on AI decisions
-                - **📡 Tool Nodes:** Groq Whisper, EasyOCR, Wikipedia, Math, YouTube, File processing
-                
-                **Flow:**
-                1. **AI Decision Node:** Qwen3-8B phân tích câu hỏi và quyết định action
-                2. **Dynamic Routing:** LangGraph route đến tool node tương ứng
-                3. **Tool Execution:** Node thực hiện tool và trả về kết quả  
-                4. **Final Answer:** AI tổng hợp kết quả và đưa ra câu trả lời cuối
-                
-                **Available Tools:**
-                - `transcribe_audio` - Groq Whisper cho audio files
-                - `ocr_image` - EasyOCR cho images với text
-                - `read_file` - Python libraries cho Excel/Python/text files
-                - `search_wikipedia` - Wikipedia API cho factual information
-                - `calculate_math` - Safe eval cho math calculations
-                - `get_youtube` - yt-dlp cho YouTube video info
-                - `answer_directly` - Direct reasoning cho simple questions
-                
-                **Key Features:**
-                ✅ **Pure AI-driven** - Không hardcode routing logic  
-                ✅ **Tool calling pattern** - AI quyết định tool nào cần dùng  
-                ✅ **LangGraph workflow** - Structured execution với state management  
-                ✅ **Error handling** - Robust fallbacks cho mọi tool  
-                ✅ **Dynamic parameters** - AI extract parameters cho tools  
-                
-                **Environment:**
-                - `HF_TOKEN` - HuggingFace API key cho Qwen3-8B
-                - `GROQ_API_KEY` - Groq API key cho Whisper transcription
-                """)
-        
-        # Footer
-        gr.Markdown("""
-        ---
-        *Developed for Hugging Face Agents Course Unit 4 - AI-Driven Tool Calling Agent*
-        """)
-    
-    return app
+    login_btn = 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)
 
-# =============================================================================
-# MAIN
-# =============================================================================
+    run_button.click(
+        fn=run_and_submit_all,
+        inputs=[login_btn],
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    app = create_app()
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True
-    )
+    demo.launch(debug=True, share=False)