Update app.py

app.py

@@ -4,6 +4,7 @@ import requests
 import inspect
 import pandas as pd
 from huggingface_hub import InferenceClient  
+from transformers import AutoTokenizer  # Import AutoTokenizer from transformers
 import chess
 import chess.engine
 from PIL import Image
@@ -11,7 +12,6 @@ from io import BytesIO
 import base64
 import re
 
-# (Keep Constants and BasicAgent class as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
@@ -24,6 +24,7 @@ class BasicAgent:
         
         os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_abcdefghijklmnopqrstuvwxyz"
         self.model = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")
+        self.tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")  # Initialize tokenizer
         SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and
         finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
         YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
@@ -59,7 +60,7 @@ class BasicAgent:
 
     def extract_board_from_image(self, image_data: str) -> chess.Board:
         # Stub implementation; replace with real image-to-FEN logic
-        # For now, simulate a board with forced mate
+        # For now, simulate board with forced mate
         board = chess.Board()
         board.set_fen("6k1/5ppp/8/8/8/8/5PPP/6K1 b - - 0 1")  # Example stub position
         return board
@@ -75,31 +76,22 @@ class BasicAgent:
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
 
+        # Tokenize input question using tokenizer
+        inputs = self.tokenizer(question, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
         if self.is_chess_question(question):
             print("Detected chess-related question. Attempting board extraction...")
-            # You would extract image info from the question or context here
-            # For now, we'll simulate board analysis
-            board = self.extract_board_from_image("")  # Replace with actual image
+            board = self.extract_board_from_image("")  # Replace with actual image extraction logic
             return self.find_best_move(board)
 
         if self.maybe_reversed(question):
             print("Detected likely reversed text. Attempting riddle solving...")
             return self.solve_riddle(question)
 
-        final_answer = self.model.chat_completion(question)
-        print(f"Agent returning final answer: {final_answer}")
-        return final_answer
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        if self.maybe_reversed(question):
-            print("Detected likely reversed text. Attempting riddle solving...")
-            return self.solve_riddle(question)
-        
-        final_answer = self.model.chat_completion(question)
-        print(f"Agent returning final answer: {final_answer}")
-        return final_answer
+        # Use the model to generate an answer
+        response = self.model.chat_completion(question)  # Assuming this works with raw text input
+        print(f"Agent returning final answer: {response}")
+        return response
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
@@ -121,13 +113,13 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent (modify this part to create your agent)
     try:
         agent = BasicAgent()
     except Exception as e:
         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)
+    # In the case of an app running as a hugging Face space, this link points toward your codebase (useful for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)