Update app.py

app.py

@@ -1,63 +1,93 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+from unsloth.chat_templates import get_chat_template
 
-"""
-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")
+# Initialize the model
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
 
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="umair894/llama3",
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template="llama-3",
+    mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"},
+    map_eos_token=True,
+)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
 
+# VIKK introduction prompt
+vikk_intro = """Consider you self a legal assistant in USA and your name is VIKK. You are very knowledgeable about all aspects of the law...
+"""
+
+# Function to get chat response
+def get_response(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}] if system_message else []
+    if not history:
+        history = [{"role": "assistant", "content": vikk_intro}]
+    
+    for msg in history:
+        if msg[0]:
+            messages.append({"role": "user", "content": msg[0]})
+        if msg[1]:
+            messages.append({"role": "assistant", "content": msg[1]})
+    
     messages.append({"role": "user", "content": message})
 
-    response = ""
+    formatted_messages = [{"from": "assistant", "value": vikk_intro}]
+    for msg in messages[1:]:
+        role = "human" if msg["role"] == "user" else "assistant"
+        formatted_messages.append({"from": role, "value": msg["content"]})
 
-    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
+    inputs = tokenizer.apply_chat_template(
+        formatted_messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to("cuda")
 
-        response += token
-        yield response
+    text_streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    
+    output = ""
+    for out in model.generate(input_ids=inputs["input_ids"], streamer=text_streamer, max_new_tokens=max_tokens, use_cache=True):
+        output += out
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-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"),
-        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)",
-        ),
-    ],
-)
+    response = tokenizer.decode(output, skip_special_tokens=True).split(">>> Assistant: ")[-1].strip()
+    
+    return response
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Chatbot Interface")
+
+    with gr.Row():
+        with gr.Column():
+            system_message = gr.Textbox(value="You are a friendly Chatbot.", label="System message")
+            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        with gr.Column():
+            chatbot = gr.Chatbot()
+
+            user_input = gr.Textbox(label="You:")
+            send_button = gr.Button("Send")
+
+            def respond(message, history, system_message, max_tokens, temperature, top_p):
+                response = get_response(message, history, system_message, max_tokens, temperature, top_p)
+                history.append((message, response))
+                return history
 
+            send_button.click(respond, [user_input, chatbot, system_message, max_tokens, temperature, top_p], chatbot)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()