Update app.py

app.py

@@ -1,64 +1,153 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
 
+
+TITLE = '''
+<h1 style="text-align: center;">Google Gemma2 2B it <a href="https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_1_8b?duplicate=true" id="duplicate-button"><button style="color:white">Duplicate this Space</button></a></h1>
+'''
+
+DESCRIPTION = '''
+<div>
+</div>
+'''
+
+LICENSE = """
+<p/>
+---
+Built with Gemma
 """
-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
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/c21ff9c8e7ecb2f7d957a72f2ef03c610ac7bbc4/Meta_lockup_positive%20primary_RGB_small.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3.1</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+</div>
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+css = """
+h1 {
+  text-align: center;
+  display: block;
+  display: flex;
+    align-items: center;
+    justify-content: center;
+}
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+#duplicate-button {
+  margin-left: 10px;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+  font-size: 1rem;
+  padding: 3px 5px;
+}
+"""
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+model_id = "google/gemma-2-2b-it"
 
-    messages.append({"role": "user", "content": message})
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
 
-    response = ""
+MAX_INPUT_TOKEN_LENGTH = 1024
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+# Gradio inference function
+@spaces.GPU(duration=120)
+def chat_llama3_1_8b(message: str, 
+              history: list, 
+              temperature: float, 
+              max_new_tokens: int
+             ) -> str:
+    """
+    Generate a streaming response using the llama3-8b model.
+    Args:
+        message (str): The input message.
+        history (list): The conversation history used by ChatInterface.
+        temperature (float): The temperature for generating the response.
+        max_new_tokens (int): The maximum number of new tokens to generate.
+    Returns:
+        str: The generated response.
+    """
+    conversation = []
+    for user, assistant in history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+    if input_ids.shape[1] > 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.")
+    input_ids = input_ids.to(model.device)
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+
+    generate_kwargs = dict(
+        input_ids= input_ids,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=temperature != 0,  # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
         temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        eos_token_id=terminators,
+    )
 
-        response += token
-        yield response
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+        
 
-"""
-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)",
-        ),
-    ],
-)
+# Gradio block
+chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
 
+with gr.Blocks(fill_height=True, css=css) as demo:
 
+    gr.Markdown(TITLE)
+    gr.Markdown(DESCRIPTION)
+    #gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    gr.ChatInterface(
+        fn=chat_llama3_1_8b,
+        chatbot=chatbot,
+        fill_height=True,
+        examples_per_page=3,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Slider(minimum=0,
+                      maximum=1, 
+                      step=0.1,
+                      value=0.95, 
+                      label="Temperature", 
+                      render=False),
+            gr.Slider(minimum=128, 
+                      maximum=4096,
+                      step=1,
+                      value=512, 
+                      label="Max new tokens", 
+                      render=False ),
+            ],
+        examples=[
+            ["What is the best way to open a can of worms?"],
+            ["The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. "],
+            ['How to setup a human base on Mars? Give short answer.'],
+            ['Explain theory of relativity to me like I’m 8 years old.'],
+            ['What is 9,000 * 9,000?'],
+            ['Write a pun-filled happy birthday message to my friend Alex.'],
+            ['Justify why a penguin might make a good king of the jungle.']
+            ],
+        cache_examples=False,
+                     )
+    
+    gr.Markdown(LICENSE)
+    
 if __name__ == "__main__":
     demo.launch()