Update app.py

app.py

@@ -1,104 +1,52 @@
-import time
-from threading import Thread
-
 import gradio as gr
-import torch
+from llava_llama3.serve.cli import chat_llava
+from llava_llama3.model.builder import load_pretrained_model
 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 = "TheFinAI/FinLLaVA"
-
-processor = AutoProcessor.from_pretrained(model_id)
+import torch
 
-model = LlavaForConditionalGeneration.from_pretrained(
-    model_id,
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True,
+model_path = "TheFinAI/FinLLaVA"
+device = "cuda"
+conv_mode = "llama_3"
+temperature = 0
+max_new_tokens = 512
+load_8bit = False
+load_4bit = False
+
+tokenizer, llava_model, image_processor, context_len = load_pretrained_model(
+    model_path, 
+    None, 
+    'llava_llama3', 
+    load_8bit, 
+    load_4bit, 
+    device=device
 )
 
-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]
-    try:
-        if image is None:
-            # Handle the case where image is None
-            gr.Error("You need to upload an image for LLaVA to work.")
-    except NameError:
-        # Handle the case where 'image' is not defined at all
-        gr.Error("You need to upload an image for LLaVA to work.")
-
-    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
-
+def predict(image, text):
+    output = chat_llava(
+        args=None,
+        image_file=image,
+        text=text,
+        tokenizer=tokenizer,
+        model=llava_model,
+        image_processor=image_processor,
+        context_len=context_len
+    )
+    return output
 
-chatbot=gr.Chatbot(placeholder=PLACEHOLDER,scale=1)
+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:
+
+with gr.Blocks(fill_height=True) as demo:
     gr.ChatInterface(
-    fn=bot_streaming,
-    title="LLaVA Llama-3-8B",
-    examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
-              {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
-    description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). 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,
-    textbox=chat_input,
-    chatbot=chatbot,
+        fn=predict,
+        title="FinLLaVA",
+        examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
+                  {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
+        description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). 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,
+        textbox=chat_input,
+        chatbot=chatbot,
     )
 
 demo.queue(api_open=False)