Create app.py

app.py

@@ -20,7 +20,7 @@ GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
 print(f"GOOGLE_API_KEY (first 5 chars): {GOOGLE_API_KEY[:5] if GOOGLE_API_KEY else 'None'}...") # Debugging API key
 
 # --- Define the default API URL ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # <--- URL ATUALIZADA AQUI!
+DEFAULT_API_URL = "https://agent-challenge.hf.space/agent_challenge" # Or the correct API URL if different
 
 
 # --- Google Generative AI LLM Initialization ---
@@ -78,7 +78,6 @@ def web_search(query: str) -> list[dict]:
         search = GoogleSearch(params)
         search_results_dict = search.get_dict() # Get results as a dictionary
         print(f"SerpAPI raw response keys: {search_results_dict.keys() if isinstance(search_results_dict, dict) else 'Response is not a dictionary'}") # Debugging response keys
-        print(f"SerpAPI full response (first 500 chars): {str(search_results_dict)[:500]}...") # DEBUG: Print full SerpAPI response
 
         # Extract organic results
         # Add check that search_results_dict and organic_results are valid
@@ -98,7 +97,7 @@ def web_search(query: str) -> list[dict]:
         else:
             print(f"No 'organic_results' key found or invalid format in SerpAPI response. Response type: {type(search_results_dict)}")
             # Print the whole response if no organic_results are found for debugging
-            print(f"SerpAPI response (no organic results or invalid format): {search_results_dict}") # DEBUG: Print full response if no organic_results
+            # print(f"SerpAPI response (no organic results): {search_results_dict}")
 
 
     except Exception as e:
@@ -107,7 +106,6 @@ def web_search(query: str) -> list[dict]:
         return []
 
     print(f"web_search returning {len(results)} results.") # Debugging return count
-    print(f"web_search results content (first 2 items): {results[:2]}") # DEBUG: Print content of results
     return results # Always return a list (empty or with results)
 
 
@@ -422,16 +420,11 @@ Answer:"""
                      # Return the old style answer if LLM is not available, but only if search results exist
                      print("Returning basic answer based on search results (LLM not available).")
                      answer_parts = []
-                     # DEBUG: Check what's in search_results before formatting
-                     print(f"DEBUG: search_results in fallback (first item): {search_results[0] if search_results else 'empty'}")
-
                      for i, result in enumerate(search_results[:3]):
                         # Add check for None or non-dict result item before accessing keys
                         if result is None or not isinstance(result, dict):
                             print(f"Skipping invalid result at index {i} in basic answer formatting: {result}")
                             continue
-                        # The issue is likely here: if 'snippet' is missing, it might be using the question as a default
-                        # Ensure we only use actual snippets or titles from the search results
                         if result.get('snippet'):
                            # Limit snippet length to avoid overly long responses
                            snippet = result['snippet']
@@ -440,7 +433,6 @@ Answer:"""
                            answer_parts.append(f"Snippet {i+1}: {snippet}")
                         elif result.get('title'):
                              answer_parts.append(f"Result {i+1} Title: {result['title']}")
-                         # If neither snippet nor title, skip this result for the answer_parts
                      if answer_parts:
                          return "Based on web search (LLM not available):\n" + "\n".join(answer_parts)
                      else:
@@ -464,10 +456,6 @@ Answer:"""
             return fixed_answer
 
 
-# --- Gradio Interface Setup ---
-# Define the Gradio interface
-
-# MOVIDO PARA CIMA: A função run_and_submit_all precisa ser definida antes de ser usada no Gradio.
 def run_and_submit_all( profile: gr.OAuthProfile | None, other_arg=None): # Modified to accept 2 arguments
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -482,10 +470,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, other_arg=None): # Modi
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
-        # --- SIMULAÇÃO PARA COLAB ---
-        username = "colab_test_user" # <--- Adicione esta linha para simular um usuário no Colab
-        print(f"User not logged in, simulating for Colab: {username}")
-        # return "Please Login to Hugging Face with the button.", None # Comente esta linha para testar no Colab
+        return "Please Login to Hugging Face with the button.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
@@ -500,7 +485,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, other_arg=None): # Modi
         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" if space_id else "No SPACE_ID found, cannot generate agent_code URL."
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
     # 2. Fetch Questions
@@ -610,34 +595,50 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, other_arg=None): # Modi
         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
 
 
-with gr.Blocks() as demo:
+# Move Gradio interface definition and launch outside the function
+with gr.Blocks(theme=gr.themes.Soft(), title="Basic Agent Evaluation Runner") as demo:
     gr.Markdown(
         """
-        # Agente de IA para o Desafio Hugging Face
-        Este agente usa o Google Gemini para responder perguntas e a SerpAPI para realizar buscas na web.
-        Ele também tem uma funcionalidade (não ativada pelo desafio) para processar vídeos.
+        # Basic Agent Evaluation Runner
+        This application fetches a set of questions from a scoring API,
+        runs your custom agent against each question, and submits the answers for scoring.
+
+        **Instructions:**
+        1. Ensure your agent logic is defined in the `BasicAgent` class above.
+        2. **Get a SerpAPI key and a Google AI API key and add them as environment variables in your runtime environment (e.g., as secrets in your Hugging Face Space settings).**
+        3. Log in to Hugging Face using the button below.
+        4. Click the "Run Evaluation & Submit All Answers" button.
+        5. The application will fetch questions, run your agent, submit answers, and display the results below.
         """
     )
+    login_btn = gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button.interactive = True # Re-enable the button
+
+    status_output = gr.Textbox(label="Run Status", interactive=False, lines=5)
+    results_output = gr.DataFrame(label="Evaluation Results")
+
+    # Add Gradio components for video input
+    video_input = gr.Video(label="Upload Video or Paste URL (Optional)")
 
-    with gr.Row():
-        with gr.Column():
-            question_input = gr.Textbox(label="Sua Pergunta", placeholder="Ex: O que é inteligência artificial?")
-            # Não incluímos o input de vídeo aqui, pois o desafio não o usa diretamente
-            # video_input = gr.Video(label="Upload de Vídeo (Opcional)", type="filepath")
-            submit_button = gr.Button("Executar Agente e Submeter Respostas")
-        with gr.Column():
-            status_output = gr.Textbox(label="Status da Execução", interactive=False)
-            results_dataframe = gr.Dataframe(label="Resultados Detalhados", interactive=False, headers=["Task ID", "Question", "Submitted Answer"])
-
-    # Conectar o botão à função run_and_submit_all
-    # O componente gr.Login() fornece o 'profile' automaticamente quando o usuário faz login
-    submit_button.click(
-        fn=run_and_submit_all,
-        inputs=[], # Removido gr.Login(every=None)
-        outputs=[status_output, results_dataframe]
+    run_button.click(
+        run_and_submit_all,
+        # Pass the video_input to the function
+        inputs=[login_btn], # Modified to exclude video_input for now as run_and_submit_all doesn't use it
+        outputs=[status_output, results_output]
     )
+    # Add a separate button or modify the existing one to handle video processing
+    # For this subtask, we are just adding the video processing capability to the agent,
+    # not fully integrating it into the Gradio submission flow yet.
 
-# Lançar o aplicativo Gradio
-demo.launch(share=True) # share=True gera um link público para o seu Space (útil para testes)
+# Ensure the app launches when the script is run
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0") # Ensure binding to all interfaces