Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, TextIteratorStreamer
+from threading import Thread
+import re
+import time 
+from PIL import Image
+import torch
+import spaces
+
+processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+
+model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-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]
+  
+  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=100)
+  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 Next](https://huggingface.co/papers/2310.03744) in this demo. Upload an image and start chatting about it, or simply try one of the examples below.",
+                        stop_btn="Stop Generation", multimodal=True)
+demo.launch(debug=True)