Update app.py

app.py

@@ -1,98 +1,238 @@
-from agent import BasicAgent
-
 import os
 import gradio as gr
 import requests
+
 import pandas as pd
+import os
+from agents import manager_agent
+from datetime import datetime
+from typing import Optional
+import time
 
+# (Keep Constants as is)
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    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"
-
-    try:
-        agent = BasicAgent()
-    except Exception as e:
+
+
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.agent = manager_agent
+        self.verbose = True
+
+    def __call__(self, question: str, files: list[str] = None) -> str:
+        print(f"Agent received question: {question[:50]}... with files: {files}")
+        result = self.answer_question(question, files)
+        print(f"Agent returning answer: {result}")
+        #time.sleep(60)
+        return result
+    def answer_question(self, question: str, task_file_path: Optional[str] = None) -> str:
+        """
+        Process a GAIA benchmark question and return the answer
+        
+        Args:
+            question: The question to answer
+            task_file_path: Optional path to a file associated with the question
+            
+        Returns:
+            The answer to the question
+        """
+        try:
+            if self.verbose:
+                print(f"Processing question: {question}")
+                if task_file_path:
+                    print(f"With associated file: {task_file_path}")
+            
+            # Create a context with file information if available
+            context = question
+            
+            # If there's a file, read it and include its content in the context
+            if task_file_path:
+                try:
+                    context = f"""
+Question: {question}
+
+This question has an associated file. You can download the file from 
+{DEFAULT_API_URL}/files/{task_file_path}
+using the download_file_from_url tool.
+
+Analyze the file content above to answer the question.
+"""
+                except Exception as file_e:
+                    context = f"""
+Question: {question}
+
+This question has an associated file at path: {task_file_path}
+However, there was an error reading the file: {file_e}
+You can still try to answer the question based on the information provided.
+"""
+            
+            # Check for special cases that need specific formatting
+            # Reversed text questions
+            if question.startswith(".") or ".rewsna eht sa" in question:
+                context = f"""
+This question appears to be in reversed text. Here's the reversed version:
+{question[::-1]}
+
+Now answer the question above. Remember to format your answer exactly as requested.
+"""
+            
+            # Add a prompt to ensure precise answers
+            full_prompt = f"""{context}
+
+When answering, provide ONLY the precise answer requested. 
+Do not include explanations, steps, reasoning, or additional text.
+Be direct and specific. GAIA benchmark requires exact matching answers.
+For example, if asked "What is the capital of France?", respond simply with "Paris".
+"""
+            
+            # Run the agent with the question
+            answer = self.agent.run(full_prompt)
+            
+            # Clean up the answer to ensure it's in the expected format
+            # Remove common prefixes that models often add
+            answer = self._clean_answer(answer)
+            
+            if self.verbose:
+                print(f"Generated answer: {answer}")
+                
+            return answer
+        except Exception as e:
+            error_msg = f"Error answering question: {e}"
+            if self.verbose:
+                print(error_msg)
+            return error_msg
+    
+    def _clean_answer(self, answer: any) -> str:
+        """
+        Clean up the answer to remove common prefixes and formatting
+        that models often add but that can cause exact match failures.
+        
+        Args:
+            answer: The raw answer from the model
+            
+        Returns:
+            The cleaned answer as a string
+        """
+        # Convert non-string types to strings
+        if not isinstance(answer, str):
+            # Handle numeric types (float, int)
+            if isinstance(answer, float):
+                # Format floating point numbers properly
+                # Check if it's an integer value in float form (e.g., 12.0)
+                if answer.is_integer():
+                    formatted_answer = str(int(answer))
+                else:
+                    # For currency values that might need formatting
+                    if abs(answer) >= 1000:
+                        formatted_answer = f"${answer:,.2f}"
+                    else:
+                        formatted_answer = str(answer)
+                return formatted_answer
+            elif isinstance(answer, int):
+                return str(answer)
+            else:
+                # For any other type
+                return str(answer)
+        
+        # Now we know answer is a string, so we can safely use string methods
+        # Normalize whitespace
+        answer = answer.strip()
+        
+        # Remove common prefixes and formatting that models add
+        prefixes_to_remove = [
+            "The answer is ", 
+            "Answer: ",
+            "Final answer: ",
+            "The result is ",
+            "To answer this question: ",
+            "Based on the information provided, ",
+            "According to the information: ",
+        ]
+        
+        for prefix in prefixes_to_remove:
+            if answer.startswith(prefix):
+                answer = answer[len(prefix):].strip()
+        
+        # Remove quotes if they wrap the entire answer
+        if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")):
+            answer = answer[1:-1].strip()
+        
+        return answer
+
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    # 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://github.com/ssgrummons/huggingface_final_assignment"
     print(agent_code)
 
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        return f"Error fetching questions: {e}", None
-
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    # 2. Fetch Questions
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+        files = item.get("file_name")
         if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            if files is None or files == '':
+                print(files)
+                submitted_answer = agent(question_text)
+            else:
+                submitted_answer = agent(question_text, task_id)
             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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    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"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.')}"
-        )
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: {e}"
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluation")
-    gr.Markdown("""
-    **Instructions:**
-    1. Clone this Space and modify the agent logic in `agent.py`.
-    2. Log in to Hugging Face below.
-    3. Click "Run Evaluation & Submit All Answers" to evaluate.
-    """)
-
-    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)
-
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+    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).
+        outputs=[status_output, results_table]
+    )
+
+import sys
+from pathlib import Path
+
+class Tee:
+    def __init__(self, file_path):
+        log_path = Path(file_path)
+        log_path.parent.mkdir(parents=True, exist_ok=True)
+        self.terminal_stdout = sys.__stdout__
+        self.terminal_stderr = sys.__stderr__
+        self.log = open(log_path, "a")
+
+    def write(self, message):
+        self.terminal_stdout.write(message)
+        self.log.write(message)
+
+    def flush(self):
+        self.terminal_stdout.flush()
+        self.log.flush()
+
+    def isatty(self):
+        return self.terminal_stdout.isatty()
+
 
 if __name__ == "__main__":
-    print("Launching Gradio Interface for Boosted GAIA Agent...")
-    demo.launch(debug=True, share=False)
+    
+    # Redirect stdout and stderr
+    log_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_file = f"./logs/output_{log_timestamp}.log"
+    tee = Tee(log_file)
+    sys.stdout = tee
+    sys.stderr = tee
+
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")