Update app.py

app.py

@@ -3,9 +3,6 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-import json
-import re
-from typing import Dict, Any
 from dotenv import load_dotenv
 from openai import OpenAI
 from tenacity import retry, stop_after_attempt, wait_exponential
@@ -16,9 +13,7 @@ load_dotenv()
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-OPENAI_MODEL = (
-    "gpt-4-turbo-preview"  # Using OpenAI's latest model for better performance
-)
+OPENAI_MODEL = "openai/gpt-4.1"  # or "gpt-3.5-turbo" based on your preference
 
 
 # --- Basic Agent Definition ---
@@ -27,39 +22,37 @@ class BasicAgent:
     def __init__(self):
         """Initialize the agent with OpenAI client and setup."""
         print("BasicAgent initializing...")
-        self.client = OpenAI(api_key="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",base_url="https://models.github.ai/inference")
-        self.question_history: Dict[str, Any] = {}  # Store question context
+        self.client = OpenAI(
+            api_key="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",
+            base_url="https://models.github.ai/inference",
+        )
+        if not os.getenv("OPENAI_API_KEY"):
+            raise ValueError("OPENAI_API_KEY environment variable is not set")
         print("BasicAgent initialized successfully.")
 
-    def _format_answer(self, raw_answer: str) -> str:
-        """Format the answer to improve exact matching success."""
-        # Remove any explanations or reasoning
-        if "Answer:" in raw_answer:
-            answer = raw_answer.split("Answer:")[-1].strip()
-        elif "Final answer:" in raw_answer:
-            answer = raw_answer.split("Final answer:")[-1].strip()
-        else:
-            answer = raw_answer.strip()
-
-        # Clean up formatting
-        answer = re.sub(
-            r"\s+", " ", answer
-        )  # Replace multiple spaces with single space
-        answer = answer.strip("\"'")  # Remove quotes
-        answer = answer.strip(".")  # Remove trailing periods
-
-        return answer.strip()
-
     @retry(
         stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10)
     )
-    def _get_completion(self, messages: list) -> str:
+    def _get_completion(self, prompt: str) -> str:
         """Get completion from OpenAI with retry logic."""
         try:
             response = self.client.chat.completions.create(
                 model=OPENAI_MODEL,
-                messages=messages,
-                temperature=0.1,  # Lower temperature for more consistent outputs
+                messages=[
+                    {
+                        "role": "system",
+                        "content": """You are a Expert helpful AI assistant designed to answer questions from the GAIA benchmark. 
+                     Follow these guidelines:
+                     1. Provide clear, concise, and accurate answers
+                     2. If a question requires specific steps or calculations, show them clearly
+                     3. Format your response in a clean, readable way
+                     4. Be precise and avoid ambiguity
+                     5. If you're not completely sure about an answer, state your confidence level
+                     Remember: Your answers will be evaluated through exact matching.""",
+                    },
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=0.5,  # Lower temperature for more consistent outputs
                 max_tokens=1000,
             )
             return response.choices[0].message.content.strip()
@@ -67,70 +60,37 @@ class BasicAgent:
             print(f"Error in OpenAI API call: {e}")
             raise
 
-    def _analyze_question(self, question: str) -> dict:
-        """Analyze the question to determine its type and required approach."""
-        system_msg = """You are an expert at analyzing questions. For the given question:
-1. Identify the question type (e.g., factual, calculation, reasoning)
-2. Identify key entities and concepts
-3. Determine if external knowledge is needed
-4. Suggest the best approach to answer it
-Provide your analysis in JSON format."""
-
-        messages = [
-            {"role": "system", "content": system_msg},
-            {"role": "user", "content": f"Analyze this question: {question}"},
-        ]
+    def _preprocess_question(self, question: str) -> str:
+        """Preprocess the question to enhance clarity and context."""
+        enhanced_prompt = f"""Please analyze and answer the following question from the GAIA benchmark.
+        Question: {question}
 
-        try:
-            analysis = self._get_completion(messages)
-            return json.loads(analysis)
-        except:
-            return {"type": "unknown", "approach": "direct"}
-
-    def _get_answer(self, question: str, analysis: dict) -> str:
-        """Get the answer based on question analysis."""
-        system_prompt = f"""You are an AI assistant specialized in answering GAIA benchmark questions.
-Your task is to provide EXACT, PRECISE answers that can be matched against a ground truth.
-
-Guidelines:
-1. Provide ONLY the final answer, no explanations
-2. Be extremely precise and consistent in formatting
-3. For numerical answers, use digits (e.g., "42" not "forty-two")
-4. For lists, use comma-separated values without spaces after commas
-5. For yes/no questions, answer only with "Yes" or "No"
-6. Remove any punctuation from the end of your answer
-7. Keep your answer as concise as possible while being complete
-
-Question type: {analysis.get('type', 'unknown')}
-Approach: {analysis.get('approach', 'direct')}
-
-Remember: Your answer will be compared EXACTLY with the ground truth. Format matters!"""
-
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": question},
-        ]
-
-        raw_answer = self._get_completion(messages)
-        return self._format_answer(raw_answer)
+        Provide a clear, specific answer that can be evaluated through exact matching.
+        If the question requires multiple steps, please show your reasoning but ensure the final answer is clearly stated.
+        """
+        return enhanced_prompt
 
     def __call__(self, question: str) -> str:
         """Process the question and return an answer."""
         print(f"Agent received question (first 50 chars): {question[:50]}...")
 
         try:
-            # Analyze the question
-            analysis = self._analyze_question(question)
-            print(f"Question analysis: {json.dumps(analysis, indent=2)}")
+            # Preprocess the question
+            enhanced_prompt = self._preprocess_question(question)
+
+            # Get completion from OpenAI
+            response = self._get_completion(enhanced_prompt)
 
-            # Get and format the answer
-            answer = self._get_answer(question, analysis)
-            print(f"Generated answer: {answer}")
+            # Extract the final answer
+            # If the response contains multiple lines or explanations,
+            # we'll try to extract just the final answer
+            answer_lines = response.strip().split("\n")
+            final_answer = answer_lines[-1].strip()
 
-            # Store question context
-            self.question_history[question] = {"analysis": analysis, "answer": answer}
+            # Log the response for debugging
+            print(f"Agent generated answer: {final_answer[:100]}...")
 
-            return answer
+            return final_answer
 
         except Exception as e:
             print(f"Error processing question: {e}")