Update app.py

app.py

@@ -1,62 +1,134 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-client = InferenceClient("thanawatpi/appherb-treatment-pred-beta")
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+from unsloth import FastLanguageModel
+from huggingface_hub import snapshot_download
+from transformers import TextIteratorStreamer, StoppingCriteria, StoppingCriteriaList
+import torch
+from threading import Thread
+import io
+from contextlib import redirect_stdout
+
+# Ensure environment variables are set for the model (optional)
+import os
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+
+# Define constants and model name
+MODEL_NAME = "thanawatpi/appherb-treatment-pred-beta"
+
+# Define the preset Alpaca prompt (you can change the wording if needed)
+ALPACA_PROMPT = """
+You are an AI assistant trained to provide helpful, accurate, and friendly responses. Your tone should be polite, clear, and concise. 
+If you don't know the answer, respond honestly and suggest alternatives or request clarification. 
+
+For this session, your role is to assist the user with general queries, help with problem-solving, and guide them through different topics.
+"""
+
+# Initialize the model and tokenizer
+print("Loading model ... Please wait 1 more minute! ...")
+with redirect_stdout(io.StringIO()):
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = MODEL_NAME,
+        max_seq_length = None,
+        dtype = None,
+        load_in_4bit = True,
+    )
+    FastLanguageModel.for_inference(model)
+
+# Define stop token handler
+class StopOnTokens(StoppingCriteria):
+    def __init__(self, stop_token_ids):
+        self.stop_token_ids = tuple(set(stop_token_ids))
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        return input_ids[0][-1].item() in self.stop_token_ids
+
+# Asynchronous function to handle chatbot responses
+def async_process_chatbot(message, history):
+    eos_token = tokenizer.eos_token
+    stop_on_tokens = StopOnTokens([eos_token])
+    text_streamer  = TextIteratorStreamer(tokenizer, skip_prompt=True)
+
+    history_transformer_format = history + [[message, ""]]
+    messages = []
+    for item in history_transformer_format:
+        messages.append({"role": "user", "content": item[0]})
+        messages.append({"role": "assistant", "content": item[1]})
     
+    # Remove last assistant response
+    messages.pop(-1)
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to("cuda", non_blocking=True)
+
+    generation_kwargs = dict(
+        input_ids=input_ids,
+        streamer=text_streamer,
+        max_new_tokens=1024,
+        stopping_criteria=StoppingCriteriaList([stop_on_tokens]),
+        temperature=0.7,
+        do_sample=True,
+    )
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    generated_text = ""
+    for new_text in text_streamer:
+        if new_text.endswith(eos_token):
+            new_text = new_text[:len(new_text) - len(eos_token)]
+        generated_text += new_text
+        yield generated_text
+
+# Define the response function for Gradio
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    # Add preset Alpaca prompt as the system message if it's not provided
+    if system_message == "":
+        system_message = ALPACA_PROMPT
+
+    # Prepare the system message
+    system_message = [{"role": "system", "content": system_message}]
+    
+    # Convert history into the correct message format for chat
     for val in history:
         if val[0]:
-            messages.append({"role": "user", "content": val[0]})
+            system_message.append({"role": "user", "content": val[0]})
         if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+            system_message.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+    # Add user message to the conversation
+    system_message.append({"role": "user", "content": message})
 
+    # Stream response from Unsloth model
     response = ""
-
-    # Debugging: printing the messages to verify the format
-    print("Messages sent to model:", messages)
-
     try:
-        for message in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            print(message)  # Debugging line to see the response structure
-            token = message.choices[0].delta.content
-            response += token
+        async_gen = async_process_chatbot(message, history)
+        for generated_text in async_gen:
+            response += generated_text
             yield response
     except Exception as e:
-        print("Error during API request:", e)
+        print(f"Error during API request: {e}")
         yield "An error occurred during the request."
 
-demo = gr.ChatInterface(
-    respond,
-    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"),
+# Setup the Gradio interface
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(label="User Message"),
+        gr.Chatbot(label="Chat History", height=325),
+        gr.Textbox(value=ALPACA_PROMPT, label="System Message (Leave blank to use default)"),
+        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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
     ],
+    outputs=[gr.Chatbot()],
+    theme="compact",
+    live=True
 )
 
+# Launch the app
 if __name__ == "__main__":
     demo.launch()
+