Create app.py

app.py

@@ -0,0 +1,98 @@
+import time
+from threading import Thread
+
+import gradio as gr
+import torch
+from PIL import Image
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+from transformers import TextIteratorStreamer
+
+import spaces
+
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://cdn-uploads.huggingface.co/production/uploads/64ccdc322e592905f922a06e/DDIW0kbWmdOQWwy4XMhwX.png" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">LLaVA-Llama-3-8B</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Llava-Llama-3-8b is a LLaVA model fine-tuned from Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner</p>
+</div>
+"""
+
+
+model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+)
+
+model.to("cuda:0")
+model.generation_config.eos_token_id = 128009
+
+
+@spaces.GPU
+def bot_streaming(message, history):
+    print(message)
+    if message["files"]:
+        # message["files"][-1] is a Dict or just a string
+        if type(message["files"][-1]) == dict:
+            image = message["files"][-1]["path"]
+        else:
+            image = message["files"][-1]
+    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:
+        # Handle the case where image is None
+        image = "http://files.tatl.tv/ignore.png"
+
+    prompt = f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    # print(f"prompt: {prompt}")
+    image = Image.open(image)
+    inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)
+
+    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": False, "skip_prompt": True})
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False)
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    text_prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    # print(f"text_prompt: {text_prompt}")
+
+    buffer = ""
+    time.sleep(0.5)
+    for new_text in streamer:
+        # find <|eot_id|> and remove it from the new_text
+        if "<|eot_id|>" in new_text:
+            new_text = new_text.split("<|eot_id|>")[0]
+        buffer += new_text
+
+        # generated_text_without_prompt = buffer[len(text_prompt):]
+        generated_text_without_prompt = buffer
+        # print(generated_text_without_prompt)
+        time.sleep(0.06)
+        # print(f"new_text: {generated_text_without_prompt}")
+        yield generated_text_without_prompt
+
+
+chatbot=gr.Chatbot(placeholder=PLACEHOLDER,scale=1)
+chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
+with gr.Blocks(fill_height=True, ) as demo:
+    gr.ChatInterface(
+    fn=bot_streaming,
+    description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).",
+    stop_btn="Stop Generation",
+    multimodal=True,
+    textbox=chat_input,
+    chatbot=chatbot,
+    )
+
+demo.queue(api_open=False)
+demo.launch(show_api=False, share=False)