feat: demo for 34b llava on zero gpu

app.py

@@ -1,63 +1,59 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, TextIteratorStreamer
+from threading import Thread
+import re
+import time 
+from PIL import Image
+import torch
+import spaces
 """
 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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    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,
-    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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-34b-hf")
+
+model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-34b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True)
+model.to("cuda:0")
+
+@spaces.GPU
+def bot_streaming(message, history):
+  print(message)
+  if message["files"]:
+    image = message["files"][-1]["path"]
+  else:
+    # if there's no image uploaded for this turn, look for images in the past turns
+    # kept inside tuples, take the last one
+    for hist in history:
+      if type(hist[0])==tuple:
+        image = hist[0][0]
+
+  if image is None:
+      gr.Error("You need to upload an image for LLaVA to work.")
+  prompt=f"[INST] <image>\n{message['text']} [/INST]"
+  image = Image.open(image).convert("RGB")
+  inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
+
+  streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
+  generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=300)
+  generated_text = ""
+
+  thread = Thread(target=model.generate, kwargs=generation_kwargs)
+  thread.start()
+
+  text_prompt =f"[INST]  \n{message['text']} [/INST]"
+  
+
+  buffer = ""
+  for new_text in streamer:
+    
+    buffer += new_text
+    
+    generated_text_without_prompt = buffer[len(text_prompt):]
+    time.sleep(0.04)
+    yield generated_text_without_prompt
+
+
+demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA NeXT", examples=[{"text": "What is on the flower?", "files":["./bee.jpg"]},
+                                                                      {"text": "How to make this pastry?", "files":["./baklava.png"]}], 
+                        description="Try [LLaVA 1.6 34b] in this demo. Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
+                        stop_btn="Stop Generation", multimodal=True)
+demo.launch(debug=True)

requirements.txt

@@ -1 +1,5 @@
-huggingface_hub==0.22.2
+spaces
+pillow
+accelerate
+torch
+transformers