Update app.py

app.py

@@ -1,196 +1,293 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import google.generativeai as genai
+from typing import Optional
 
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+class ImprovedGAIAgent:
+    """Agente melhorado para o GAIA com Chain of Thought."""
+    
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("🚀 Inicializando agente melhorado...")
+        
+        # Configura Gemini
+        api_key = os.getenv("GOOGLE_API_KEY")
+        if api_key:
+            genai.configure(api_key=api_key)
+            self.model = genai.GenerativeModel("gemini-3.1-flash-lite")
+            print("✅ Gemini configurado")
+        else:
+            self.model = None
+            print("⚠️ GOOGLE_API_KEY não encontrada")
+    
     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 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") # Get the SPACE_ID for sending link to the code
+        """Responde à pergunta com Chain of Thought."""
+        
+        # Se não tiver modelo, retorna N/A
+        if not self.model:
+            return "N/A"
+        
+        # Prompt melhorado com instruções claras
+        prompt = f"""
+        You are an AI assistant solving GAIA benchmark questions.
+        
+        Question: {question}
+        
+        Instructions:
+        1. THINK STEP BY STEP about the problem.
+        2. Show your reasoning briefly.
+        3. END with the final answer in the format: FINAL ANSWER: [answer]
+        
+        Important rules:
+        - If it's a number, just output the number
+        - If it's a string, output just the string
+        - If it's a date, output in YYYY-MM-DD format
+        - No markdown, no extra text after FINAL ANSWER
+        
+        Let me solve this step by step:
+        """
+        
+        try:
+            response = self.model.generate_content(prompt)
+            text = response.text.strip()
+            
+            # Extrai a resposta final
+            if "FINAL ANSWER:" in text:
+                answer = text.split("FINAL ANSWER:")[-1].strip()
+            else:
+                # Fallback: pega a última linha
+                lines = [l.strip() for l in text.split('\n') if l.strip()]
+                answer = lines[-1] if lines else text
+            
+            # Remove markdown e aspas extras
+            answer = answer.strip('"').strip("'").strip()
+            answer = answer.replace("```", "").strip()
+            
+            print(f"✅ Resposta: {answer}")
+            return answer if answer else "N/A"
+            
+        except Exception as e:
+            print(f"❌ Erro no modelo: {e}")
+            return "N/A"
 
-    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"
+class SearchEnhancedAgent:
+    """Agente com ferramenta de busca (se disponível)."""
+    
+    def __init__(self):
+        print("🚀 Inicializando agente com busca...")
+        
+        # Configura Gemini
+        api_key = os.getenv("GOOGLE_API_KEY")
+        if api_key:
+            genai.configure(api_key=api_key)
+            self.model = genai.GenerativeModel("gemini-3.1-flash-lite")
+            print("✅ Gemini configurado")
+        else:
+            self.model = None
+            print("⚠️ GOOGLE_API_KEY não encontrada")
+        
+        # Tenta importar ferramentas de busca
+        self.search_tool = None
+        try:
+            from duckduckgo_search import DDGS
+            self.search_tool = DDGS()
+            print("✅ DuckDuckGo configurado")
+        except ImportError:
+            print("⚠️ DuckDuckGo não disponível")
+    
+    def search(self, query: str) -> str:
+        """Faz busca na web."""
+        if not self.search_tool:
+            return ""
+        try:
+            results = self.search_tool.text(query, max_results=3)
+            return "\n".join([f"- {r['body']}" for r in results])
+        except Exception as e:
+            print(f"⚠️ Erro na busca: {e}")
+            return ""
+    
+    def __call__(self, question: str) -> str:
+        """Responde à pergunta com busca se necessário."""
+        
+        if not self.model:
+            return "N/A"
+        
+        # Verifica se precisa de busca
+        search_terms = ["who", "what", "when", "where", "which", "how"]
+        needs_search = any(term in question.lower() for term in search_terms)
+        
+        search_results = ""
+        if needs_search and self.search_tool:
+            print("🔍 Buscando informações...")
+            search_results = self.search(question)
+        
+        prompt = f"""
+        You are an AI assistant solving GAIA benchmark questions.
+        
+        Question: {question}
+        
+        {f"Search results:\n{search_results}\n" if search_results else ""}
+        
+        Instructions:
+        1. Use the search results if available.
+        2. Think step by step.
+        3. END with FINAL ANSWER: [answer]
+        
+        Rules:
+        - Numbers: just the number
+        - Strings: just the text
+        - Dates: YYYY-MM-DD
+        - No markdown after FINAL ANSWER
+        
+        Let me solve this:
+        """
+        
+        try:
+            response = self.model.generate_content(prompt)
+            text = response.text.strip()
+            
+            if "FINAL ANSWER:" in text:
+                answer = text.split("FINAL ANSWER:")[-1].strip()
+            else:
+                lines = [l.strip() for l in text.split('\n') if l.strip()]
+                answer = lines[-1] if lines else text
+            
+            answer = answer.strip('"').strip("'").strip()
+            answer = answer.replace("```", "").strip()
+            
+            print(f"✅ Resposta: {answer}")
+            return answer if answer else "N/A"
+            
+        except Exception as e:
+            print(f"❌ Erro: {e}")
+            return "N/A"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+# ===== FUNÇÃO PRINCIPAL =====
+def run_and_submit(username: str, use_search: bool = True):
+    """Executa o agente e submete as respostas."""
+    
+    if not username or not username.strip():
+        return "❌ Digite seu username do Hugging Face.", None
+    
+    username = username.strip()
+    print(f"\n👤 Usuário: {username}")
+    
+    # Escolhe o agente
+    if use_search:
+        agent = SearchEnhancedAgent()
+    else:
+        agent = ImprovedGAIAgent()
+    
+    # Busca perguntas
     try:
-        response = requests.get(questions_url, timeout=15)
+        print("📥 Buscando perguntas...")
+        response = requests.get(f"{API_URL}/questions", 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
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
+        questions = response.json()
+        print(f"✅ {len(questions)} perguntas carregadas")
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+        return f"❌ Erro ao buscar perguntas: {e}", None
+    
+    # Processa perguntas
+    results = []
+    answers = []
+    correct = 0
+    
+    for i, item in enumerate(questions, 1):
         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}")
+        question = item.get("question")
+        
+        if not task_id:
             continue
+        
+        print(f"\n[{i}/{len(questions)}] Task {task_id[:8]}...")
+        print(f"   Pergunta: {question[:100]}...")
+        
         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})
+            answer = agent(question)
+            answers.append({"task_id": task_id, "submitted_answer": answer})
+            results.append({"Task ID": task_id, "Resposta": answer})
+            print(f"   ✅ Resposta: {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. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+            error_msg = f"ERRO: {e}"
+            results.append({"Task ID": task_id, "Resposta": error_msg})
+            print(f"   ❌ {error_msg}")
+    
+    if not answers:
+        return "❌ Nenhuma resposta gerada.", pd.DataFrame(results)
+    
+    # Submete
+    space_id = os.getenv("SPACE_ID", "seu-usuario/seu-space")
+    payload = {
+        "username": username,
+        "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
+        "answers": answers
+    }
+    
+    print(f"\n📤 Submetendo {len(answers)} respostas...")
+    
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(f"{API_URL}/submit", json=payload, 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 requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, 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
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {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("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
+        data = response.json()
+        
+        status = f"""
+✅ SUBMISSÃO CONCLUÍDA!
+👤 Usuário: {data.get('username', username)}
+📊 Score: {data.get('score', 'N/A')}%
+✅ Acertos: {data.get('correct_count', '?')}/{data.get('total_attempted', '?')}
+📝 Mensagem: {data.get('message', '')}
 
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+📋 Detalhes:
+- Total perguntas: {len(questions)}
+- Respostas submetidas: {len(answers)}
+- Agente: {'com busca' if use_search else 'sem busca'}
+"""
+        return status, pd.DataFrame(results)
+        
+    except Exception as e:
+        return f"❌ Erro na submissão: {e}", pd.DataFrame(results)
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
+# ===== INTERFACE GRADIO =====
+with gr.Blocks(title="GAIA Agent - Busca+CoT") as demo:
+    gr.Markdown("""
+    # 🎯 GAIA Agent - Versão Melhorada
+    
+    **Agente com Chain of Thought e busca na web!**
+    
+    Instruções:
+    1. Digite seu username do Hugging Face
+    2. Selecione se quer usar busca na web
+    3. Clique em Executar
+    4. Veja seu score!
+    """)
+    
+    with gr.Row():
+        username_input = gr.Textbox(
+            label="Seu username do Hugging Face",
+            placeholder="ex: nayaracardoso",
+            scale=2
+        )
+        search_checkbox = gr.Checkbox(
+            label="🔍 Usar busca na web",
+            value=True
+        )
+        run_btn = gr.Button("🚀 Executar", variant="primary", scale=1)
+    
+    status_output = gr.Textbox(label="Status", lines=15)
+    results_table = gr.DataFrame(label="Resultados")
+    
+    run_btn.click(
+        fn=run_and_submit,
+        inputs=[username_input, search_checkbox],
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch()