Upload app.py

app.py

@@ -3,23 +3,24 @@ import logging
 import gradio as gr
 import pandas as pd
 from api import GAIAHFAPIClient
-from agents.agent import BasicAgent, SimpleGeminiAgent
+from agents.agent import BasicAgent, DeepResearchGeminiAgent, SimpleGeminiAgent
 import random
 
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S"
+    datefmt="%Y-%m-%d %H:%M:%S",
 )
 logger = logging.getLogger(__name__)
 
+
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
     api_client = GAIAHFAPIClient(profile=profile)  # Initialize the API client
-    agent = SimpleGeminiAgent()
+    agent = DeepResearchGeminiAgent()
     # 2. Fetch Questions
     questions_data, error = api_client.get_questions()
     if error is None or questions_data is None:
@@ -37,25 +38,48 @@ def run_and_submit_all(profile: gr.OAuthProfile | 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, "Submitted Answer": submitted_answer})
+            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:
             logger.error(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}"})
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer": f"AGENT ERROR: {e}",
+                }
+            )
 
     if not answers_payload:
         logger.warning("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": api_client.username.strip(), "agent_code": api_client.agent_code, "answers": answers_payload}
-    logger.info(f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'...")
+    # 4. Prepare Submission
+    submission_data = {
+        "username": api_client.username.strip(),
+        "agent_code": api_client.agent_code,
+        "answers": answers_payload,
+    }
+    logger.info(
+        f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'..."
+    )
 
     # 5. Submit
-    logger.info(f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}")
+    logger.info(
+        f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}"
+    )
     status_message, results_df = api_client.submit_answers(submission_data, results_log)
     return status_message, results_df
 
+
 def run_and_submit_one(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -72,32 +96,57 @@ def run_and_submit_one(profile: gr.OAuthProfile | None):
     # 3. Run your Agent
     results_log = []
     answers_payload = []
-    
+
     task_id = question_data.get("task_id")
     question_text = question_data.get("question")
     if not task_id or question_text is None:
-        logger.warning(f"Skipping item with missing task_id or question: {question_data}")
+        logger.warning(
+            f"Skipping item with missing task_id or question: {question_data}"
+        )
     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})
+        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:
         logger.error(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}"})
+        results_log.append(
+            {
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}",
+            }
+        )
 
     if not answers_payload:
         logger.warning("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": api_client.username.strip(), "agent_code": api_client.agent_code, "answers": answers_payload}
-    logger.info(f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'...")
+    # 4. Prepare Submission
+    submission_data = {
+        "username": api_client.username.strip(),
+        "agent_code": api_client.agent_code,
+        "answers": answers_payload,
+    }
+    logger.info(
+        f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'..."
+    )
 
     # 5. Submit
-    logger.info(f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}")
+    logger.info(
+        f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}"
+    )
     status_message, results_df = api_client.submit_answers(submission_data, results_log)
     return status_message, results_df
 
+
 def build_gradio_interface():
     # --- Build Gradio Interface using Blocks ---
     with gr.Blocks() as demo:
@@ -122,25 +171,26 @@ def build_gradio_interface():
         run_all_button = gr.Button("Run Evaluation & Submit All Answers")
         run_one_button = gr.Button("Run a Single Evaluation")
 
-        status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+        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_all_button.click(
-            fn=run_and_submit_all,
-            outputs=[status_output, results_table]
+            fn=run_and_submit_all, outputs=[status_output, results_table]
         )
         run_one_button.click(
-            fn=run_and_submit_one,
-            outputs=[status_output, results_table]
+            fn=run_and_submit_one, outputs=[status_output, results_table]
         )
     return demo
 
+
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    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
+    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -148,15 +198,19 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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")
+        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(
+            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
+        )
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo = build_gradio_interface()
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)