Delete app.py

app.py

@@ -1,238 +0,0 @@
-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"
-
-
-
-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
-    # 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)
-
-    # 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:
-            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:
-    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__":
-    
-    # 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")