Update app.py

app.py

@@ -1,70 +1,304 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+import requests
+import pandas as pd
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+import logging
+
+# Настройка логирования
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# --- Константы ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Internet-Enabled Agent Definition ---
+class InternetAgent:
+    def __init__(self):
+        print("🌐 InternetAgent initializing with web search capabilities...")
+        
+        try:
+            # Используем модель от Hugging Face и инструмент поиска
+            self.model = HfApiModel("Qwen/Qwen2.5-Coder-32B-Instruct")
+            self.search_tool = DuckDuckGoSearchTool()
+            
+            # Создаем агента с доступом к поиску
+            self.agent = CodeAgent(
+                tools=[self.search_tool],
+                model=self.model,
+                max_steps=6,  # Ограничиваем шаги для скорости
+                add_base_tools=False  # Используем только наши инструменты
+            )
+            print("✅ InternetAgent initialized successfully with web search")
+            
+        except Exception as e:
+            print(f"❌ Error initializing InternetAgent: {e}")
+            self.agent = None
+            # Резервная база знаний на случай ошибки
+            self.fallback_knowledge = {
+                "capital of france": "Paris",
+                "capital of germany": "Berlin", 
+                "capital of uk": "London",
+                "capital of usa": "Washington D.C.",
+                "2+2": "4",
+                "largest planet": "Jupiter",
+            }
+
+    def __call__(self, question: str) -> str:
+        print(f"🤖 Processing: {question}")
+        
+        if not self.agent:
+            # Используем резервную базу знаний если агент не инициализирован
+            question_lower = question.lower()
+            for key, answer in self.fallback_knowledge.items():
+                if key in question_lower:
+                    return answer
+            return "I need internet access to answer this question properly."
+        
+        try:
+            # Создаем оптимизированный промпт для лучших результатов
+            optimized_prompt = f"""
+            Please provide a clear, concise, and accurate answer to the following question.
+            If you need to search for information, use the search tool.
+            Keep your answer brief and to the point.
+            
+            Question: {question}
+            
+            Answer:
+            """
+            
+            # Запускаем агента
+            response = self.agent.run(optimized_prompt)
+            
+            # Очищаем ответ
+            clean_response = self.clean_response(response)
+            print(f"✅ Answer: {clean_response[:100]}...")
+            
+            return clean_response
+            
+        except Exception as e:
+            print(f"❌ Error in agent execution: {e}")
+            return f"I encountered an error while searching for the answer: {str(e)}"
+
+    def clean_response(self, response: str) -> str:
+        """Очищает ответ от лишней информации"""
+        # Удаляем мета-комментарии агента
+        lines = response.split('\n')
+        clean_lines = []
+        
+        for line in lines:
+            # Пропускаем строки с инструментами или процессами
+            if any(term in line.lower() for term in ['tool:', 'searching', 'step', 'using tool']):
+                continue
+            # Пропускаем пустые строки в начале
+            if not clean_lines and not line.strip():
+                continue
+            clean_lines.append(line)
+        
+        clean_response = '\n'.join(clean_lines).strip()
+        
+        # Если ответ слишком длинный, берем первую часть
+        if len(clean_response) > 500:
+            clean_response = clean_response[:497] + "..."
+            
+        return clean_response if clean_response else "I couldn't find a clear answer to that question."
+
+# --- Упрощенная версия для тестирования ---
+class LiteInternetAgent:
+    def __init__(self):
+        print("🌐 LiteInternetAgent initializing...")
+        self.search_tool = DuckDuckGoSearchTool()
+        
+    def __call__(self, question: str) -> str:
+        try:
+            # Прямой поиск через инструмент
+            result = self.search_tool(question)
+            return f"According to web search: {result[:300]}..." if len(result) > 300 else result
+        except Exception as e:
+            return f"Search failed: {str(e)}"
+
+# --- Основная функция ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    Fetches all questions, runs the InternetAgent on them, submits all answers,
+    and displays the results.
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    space_id = os.getenv("SPACE_ID")
+
+    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"
+
+    # 1. Instantiate our Internet Agent
+    try:
+        # Пробуем полную версию, если не работает - упрощенную
+        agent = InternetAgent()
+        if agent.agent is None:
+            agent = LiteInternetAgent()
+        print("✅ InternetAgent created successfully")
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"Agent code URL: {agent_code}")
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=30)
+        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 Exception as e:
+        error_msg = f"❌ Error fetching questions: {e}"
+        print(error_msg)
+        
+        # Демо-режим с разными типами вопросов
+        demo_questions = [
+            {"task_id": "demo1", "question": "What is the capital of France?"},
+            {"task_id": "demo2", "question": "What is the current weather in Tokyo?"},
+            {"task_id": "demo3", "question": "Who won the Nobel Prize in Physics in 2023?"},
+            {"task_id": "demo4", "question": "What is the population of Brazil?"},
+            {"task_id": "demo5", "question": "Explain quantum computing in simple terms"},
+        ]
+        questions_data = demo_questions
+        print("🚨 Using demo questions since API is unavailable")
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    
+    for i, item in enumerate(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
+            
+        print(f"🔍 Processing question {i+1}/{len(questions_data)}: {question_text[:50]}...")
+        
+        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. Processed {len(answers_payload)} answers for user '{username}'"
+    print(status_update)
+
+    # 5. Submit answers (только если вопросы реальные, не демо)
+    if "demo" not in str(questions_data[0].get("task_id", "")):
+        print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+        try:
+            response = requests.post(submit_url, json=submission_data, timeout=120)
+            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 Exception as e:
+            error_message = f"❌ Submission Failed: {str(e)}"
+            print(error_message)
+            results_df = pd.DataFrame(results_log)
+            return error_message, results_df
+    else:
+        # Демо-режим: показываем ответы но не отправляем
+        demo_status = (
+            f"🧪 DEMO MODE (API Unavailable)\n"
+            f"👤 User: {username}\n"
+            f"📊 Processed: {len(answers_payload)} demo questions\n"
+            f"🌐 Agent used web search for answers\n"
+            f"💬 Real submission disabled - API not accessible\n\n"
+            f"Check the web-powered answers below!"
+        )
+        print("✅ Demo completed - showing results without submission")
+        results_df = pd.DataFrame(results_log)
+        return demo_status, results_df
+
+# --- Gradio Interface ---
+with gr.Blocks(title="Internet-Enabled AI Agent") as demo:
+    gr.Markdown("""
+    # 🌐 Internet-Enabled AI Agent
+    **Powered by web search and large language models**
+    
+    ### 🔧 Capabilities:
+    - **Web Search**: Real-time information from DuckDuckGo
+    - **LLM Power**: Qwen2.5-32B model for understanding
+    - **Multi-step Reasoning**: Complex question answering
+    
+    ### 📚 Example questions:
+    - *"Current weather in any city"*
+    - *"Latest news headlines"* 
+    - *"Historical facts and data"*
+    - *"Scientific explanations"*
+    - *"Complex calculations"*
+    """)
+    
+    gr.Markdown("""
+    ### ⚠️ Important Notes:
+    - This agent requires internet access
+    - Responses may take longer due to web searches
+    - Some questions might not have clear online answers
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.LoginButton()
+            run_button = gr.Button("🚀 Run Evaluation", variant="primary")
+    
+    with gr.Row():
+        with gr.Column():
+            status_output = gr.Textbox(
+                label="Status", 
+                lines=4, 
+                interactive=False
+            )
+        with gr.Column():
+            results_table = gr.DataFrame(
+                label="Questions & Answers", 
+                wrap=True
+            )
 
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    print("🚀 Starting Internet-Enabled AI Agent...")
+    demo.launch(debug=True, share=False)