Update app.py

app.py

@@ -1,64 +1,104 @@
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # "jax", "torch" or "tensorflow"
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+import keras_nlp
+import keras
+import spaces
+import torch
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+from typing import Iterator
+# import time
 
+# from chess_board import Game
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+print(f"Is CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
 
-    messages.append({"role": "user", "content": message})
+MAX_INPUT_TOKEN_LENGTH = 4096
 
-    response = ""
+MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 128
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+model_id = "hf://google/gemma-2b-keras"
 
-        response += token
-        yield response
 
+model = keras_nlp.models.GemmaCausalLM.from_preset(model_id)
+tokenizer = model.preprocessor.tokenizer
 
+DESCRIPTION = """
+# Gemma 2B
+**Welcome to the Gemma Chess Chatbot!**
+
+This game mode allows you to play a game against Gemma, the input must be in algebraic notation. \n
+If you need help learning algebraic notation ask Gemma!
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+
+# @spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[dict],
+    max_new_tokens: int = 1024,
+    ) -> Iterator[str]:
+
+    input_ids = tokenizer.tokenize(message)
+    
+    if len(input_ids) > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[-MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+
+    response = model.generate(message, max_length=max_new_tokens)
+
+    outputs = ""
+    
+    for char in response:
+        outputs += char
+        yield outputs
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
         ),
     ],
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+    ],
+    cache_examples=False,
+    type="messages",
 )
 
+with gr.Blocks(css_paths="./style.css", fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+        
+    play_match = Game()
+
+    # chess_png = gr.Image(play_match.display_board())
+    with gr.Row():
+        board_image = gr.HTML(play_match.display_board())
+        with gr.Column():
+            chat_interface.render()
+
+    move_input = gr.Textbox(label="Enter your move in algebraic notation (e.g., e4, Nf3, Bxc4)")
+
+    btn = gr.Button("Submit Move")
+    btn.click(play_match.generate_moves, inputs=move_input, outputs=board_image)
+
+    reset_btn = gr.Button("Reset Game")
+    reset_btn.click(play_match.reset_board, outputs=board_image)
+
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()