Update app.py

app.py

@@ -1,122 +1,146 @@
 import gradio as gr
-import torch
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    TextIteratorStreamer,
-    BitsAndBytesConfig,
-)
 import os
-from threading import Thread
 import spaces
-import time
+from transformers import GemmaTokenizer, AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
 
-token = os.environ["HF_TOKEN"]
+# Set an environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16
-)
 
-model = AutoModelForCausalLM.from_pretrained(
-    "chheplo/sft_8b_2_llama3", quantization_config=quantization_config, token=token
-)
-tok = AutoTokenizer.from_pretrained("chheplo/sft_8b_2_llama3", token=token)
-terminators = [
-    tok.eos_token_id,
-    tok.convert_tokens_to_ids("<|eot_id|>")
-]
+DESCRIPTION = '''
+<div>
+<h1 style="text-align: center;">Meta Llama3 8B</h1>
+<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
+<p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
+<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
+</div>
+'''
 
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
-else:
-    device = torch.device("cpu")
-    print("Using CPU")
+LICENSE = """
+<p/>
 
-# model = model.to(device)
-# Dispatch Errors
+---
+Built with Meta Llama 3
+"""
 
+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/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.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</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+</div>
+"""
 
-@spaces.GPU()
-def chat(message, history, temperature,do_sample, max_tokens):
-    prompt_template = """
-    You are a helpful Agricultural assistant for farmers. You are given the following input. Please complete the response briefly.
-    ## Question:
-    {}
-    
-    ## Response:
-    {}"""
-    start_time = time.time()
-    chat = []
-    # for item in history:
-    #     chat.append({"role": "user", "content": item[0]})
-    #     if item[1] is not None:
-    #         chat.append({"role": "assistant", "content": item[1]})
-    # chat.append({"role": "user", "content": message})
-    # messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+css = """
+h1 {
+  text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+}
+"""
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("chheplo/sft_8b_2_llama3")
+model = AutoModelForCausalLM.from_pretrained("chheplo/sft_8b_2_llama3", device_map="auto")  # to("cuda:0") 
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+@spaces.GPU(duration=120)
+def chat_llama3_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").to(model.device)
     
-    model_inputs = tok(prompt_template.format(
-        message, #input
-        "" # response
-    ), return_tensors="pt").to(device)
-    streamer = TextIteratorStreamer(
-        tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True
-    )
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+
     generate_kwargs = dict(
-        model_inputs,
+        input_ids= input_ids,
         streamer=streamer,
-        max_new_tokens=max_tokens,
+        max_new_tokens=max_new_tokens,
         do_sample=True,
         temperature=temperature,
-        repetition_penalty=1.2, 
-        use_cache=False,
         eos_token_id=terminators,
     )
-    
+    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
     if temperature == 0:
         generate_kwargs['do_sample'] = False
-    
+        
     t = Thread(target=model.generate, kwargs=generate_kwargs)
     t.start()
 
-    partial_text = ""
-    first_token_time = None
-    for new_text in streamer:
-        if not first_token_time:
-            first_token_time = time.time() - start_time
-        partial_text += new_text
-        yield partial_text
-
-    total_time = time.time() - start_time
-    tokens = len(tok.tokenize(partial_text))
-    tokens_per_second = tokens / total_time if total_time > 0 else 0
-
-    timing_info = f"\n\nTime taken to first token: {first_token_time:.2f} seconds\nTokens per second: {tokens_per_second:.2f}"
-    yield partial_text +  timing_info
-
-
-demo = gr.ChatInterface(
-    fn=chat,
-    examples=[["I'm a farmer from Odisha, how do I take care of whitefly in my cotton crop?"]],
-    # multimodal=False,
-    additional_inputs_accordion=gr.Accordion(
-        label="⚙️ Parameters", open=False, render=False
-    ),
-    additional_inputs=[
-        gr.Slider(
-            minimum=0, maximum=1, step=0.1, value=0.5, label="Temperature", render=False
-        ),
-        gr.Checkbox(label="Sampling",value=False),
-        gr.Slider(
-            minimum=128,
-            maximum=4096,
-            step=1,
-            value=512,
-            label="Max new tokens",
-            render=False,
-        ),
-    ],
-    stop_btn="Stop Generation",
-    title="Chat With LLMs",
-    description="Now Running [KissanAI/llama3-8b-dhenu-0.1-sft-16bit](https://huggingface.co/KissanAI/llama3-8b-dhenu-0.1-sft-16bit) in 4bit")
-demo.launch()
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        #print(outputs)
+        yield "".join(outputs)
+        
+
+# Gradio block
+chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
+
+with gr.Blocks(fill_height=True, css=css) as demo:
+    
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    gr.ChatInterface(
+        fn=chat_llama3_8b,
+        chatbot=chatbot,
+        fill_height=True,
+        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=[
+            ['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()
+