Update app.py

app.py

@@ -1,34 +1,15 @@
 import gradio as gr
-import spaces
 import torch
-from torch.cuda.amp import autocast
-import subprocess
-from huggingface_hub import InferenceClient
-import os
-import psutil
-
-
-
-"""
-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
-"""
-
-from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
-from accelerate import Accelerator
-
-
-subprocess.run(
-    "pip install psutil",
-   
-    shell=True,
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
 )
-
-import bitsandbytes as bnb  # Import bitsandbytes for 8-bit quantization
-
-
-
-from datetime import datetime
-
+import os
+from threading import Thread
+import spaces
+import time
+import subprocess
 
 subprocess.run(
     "pip install flash-attn --no-build-isolation",
@@ -36,191 +17,90 @@ subprocess.run(
     shell=True,
 )
 
-# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-# pip install 'git+https://github.com/huggingface/transformers.git'
-
-
-
-token=os.getenv('token')
-print('token = ',token)
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import transformers
-
-# model_id = "mistralai/Mistral-7B-v0.3"
-
-model_id = "microsoft/Phi-3-medium-4k-instruct"
-# model_id = "microsoft/phi-4"
-
-# model_id = "Qwen/Qwen2-7B-Instruct"
-
-
-tokenizer = AutoTokenizer.from_pretrained(
-    # model_id
-    model_id,
-    # use_fast=False
-    token= token,
-trust_remote_code=True)
-
-
-accelerator = Accelerator()
-
-model = AutoModelForCausalLM.from_pretrained(model_id, token= token, 
-                                                 # torch_dtype= torch.uint8, 
-                                             torch_dtype=torch.bfloat16,
-                                              # load_in_8bit=True,
-                                             # #  # torch_dtype=torch.fl,
-                                             attn_implementation="flash_attention_2",
-                                             low_cpu_mem_usage=True,
-                                             trust_remote_code=True,
-                                             device_map='cuda',
-                                             # device_map=accelerator.device_map,
-                                             
-                                            )
+token = os.environ["HF_TOKEN"]
 
 
-
-
-
-# 
-model = accelerator.prepare(model)
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/phi-4",
+    token=token,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16
 )
+tok = AutoTokenizer.from_pretrained("microsoft/phi-4", token=token)
+terminators = [
+    tok.eos_token_id,
+]
+
+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")
+
+model = model.to(device)
+# Dispatch Errors
+
+
+@spaces.GPU(duration=60)
+def chat(message, history, temperature, do_sample, max_tokens):
+    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)
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(
+        tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True
+    )
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        eos_token_id=terminators,
+    )
+
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
+
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+    yield partial_text
 
 
-
-
-# pipeline = transformers.pipeline(
-#     "text-generation",
-#     model="microsoft/phi-4",
-#     model_kwargs={"torch_dtype": "auto"},
-#     device_map="auto",
-# )
-
-
-# device_map = infer_auto_device_map(model, max_memory={0: "79GB", "cpu":"65GB" })
-
-# Load the model with the inferred device map
-# model = load_checkpoint_and_dispatch(model, model_id, device_map=device_map, no_split_module_classes=["GPTJBlock"])
-# model.half()
-
-import json
-
-def str_to_json(str_obj):
-    json_obj = json.loads(str_obj)
-    return json_obj
-
-
-@spaces.GPU(duration=170)
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    # yield 'retuend'
-    # model.to(accelerator.device)
-
-    messages = []
-    json_obj = str_to_json(message)
-    print(json_obj)
-    
-    messages= json_obj
-
-    # input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(accelerator.device)
-    # input_ids2 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt") #.to('cuda')
-    # print(f"Converted input_ids dtype: {input_ids.dtype}")
-    # input_str= str(input_ids2)
-    # print('input str = ', input_str)
-
-    generation_args = {
-    "max_new_tokens": max_tokens,
-    "return_full_text": False,
-    "temperature": temperature,
-    "do_sample": False,
-}
-
-    output = pipe(messages, **generation_args)
-    print(output[0]['generated_text'])
-    gen_text=output[0]['generated_text']
-
-    # with torch.no_grad():
-    #     gen_tokens = model.generate(
-    # input_ids, 
-    # max_new_tokens=max_tokens, 
-    # # do_sample=True, 
-    # temperature=temperature,
-    # )
-
-    # gen_text = tokenizer.decode(gen_tokens[0])
-    # print(gen_text)
-    # gen_text= gen_text.replace(input_str,'')
-    # gen_text= gen_text.replace('<|im_end|>','')
-    
-    yield gen_text
-   
-  
-#     messages = [
-#     # {"role": "user", "content": "What is your favourite condiment?"},
-#     # {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-#     # {"role": "user", "content": "Do you have mayonnaise recipes?"}
-# ]
-
-    # inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
-
-    # outputs = model.generate(inputs, max_new_tokens=2000)
-    # gen_text=tokenizer.decode(outputs[0], skip_special_tokens=True)
-   
-    # print(gen_text)
-    # yield gen_text
-    # for val in history:
-    #     if val[0]:
-    #         messages.append({"role": "user", "content": val[0]})
-    #     if val[1]:
-    #         messages.append({"role": "assistant", "content": val[1]})
-
-    # messages.append({"role": "user", "content": message})
-
-    # response = ""
-
-    # 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
-
-    #     response += token
-    #     yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
-    respond,
+    fn=chat,
+    examples=[["Write me a poem about Machine Learning."]],
+    # multimodal=False,
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
     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)",
+            minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
+        ),
+        gr.Checkbox(label="Sampling", value=True),
+        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 [microsoft/phi-4](https://huggingface.co/microsoft/phi-4)",
 )
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()