Update main.py

main.py

@@ -1,8 +1,15 @@
+import sys
+import os
 import math
 import random
 import numpy as np
+import gradio as gr
 from rwkv.model import RWKV
-from rwkv.utils import PIPELINE
+from transformers import AutoTokenizer
+
+# Add RWKV directory to Python path
+rwkv_dir = os.path.join(os.getcwd(), 'RWKV-LM')
+sys.path.append(rwkv_dir)
 
 # Define the Node class for MCTS
 class Node:
@@ -111,13 +118,17 @@ class GameState:
 
 # Initialize the RWKV model and tokenizer
 model_name = "BlinkDL/rwkv-4-raven"
-model = RWKV(model=model_name, strategy="cuda fp16")
-pipeline = PIPELINE(model, "20B_tokenizer")
+tokenizer = AutoTokenizer.from_pretrained("gpt2")  # Use a tokenizer from a supported model
+
+# Load the RWKV model
+model = RWKV(model_name=model_name, strategy="cuda fp16")
 
 # Generate Chain-of-Thought
 def generate_cot(state):
     input_text = f"Current state: {state}\nWhat is the best move?"
-    cot = pipeline.generate(input_text, max_tokens=100)
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(inputs.input_ids, max_length=100, num_return_sequences=1)
+    cot = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return cot
 
 # Use CoT in MCTS
@@ -128,4 +139,7 @@ def mcts_with_cot(initial_state):
     return best_state, cot
 
 # Function to be called by Gradio
-def run_mcts_c
+def run_mcts_cot(initial_board):
+    initial_state = GameState(initial_board, 1)
+    best_state, cot = mcts_with_cot(initial_state)
+    return str(best_state), cot