Update app.py

app.py

@@ -1,64 +1,88 @@
+from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
+from PIL import Image
+import requests
+import torch
+from threading import Thread
 import gradio as gr
-from huggingface_hub import InferenceClient
+from gradio import FileData
+import time
+import spaces
+ckpt = "Xkev/Llama-3.2V-11B-cot"
+model = MllamaForConditionalGeneration.from_pretrained(ckpt,
+    torch_dtype=torch.bfloat16).to("cuda")
+processor = AutoProcessor.from_pretrained(ckpt)
 
-"""
-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")
 
+@spaces.GPU
+def bot_streaming(message, history, max_new_tokens=250):
+    
+    txt = message["text"]
+    ext_buffer = f"{txt}"
+    
+    messages= [] 
+    images = []
+    
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    for i, msg in enumerate(history): 
+        if isinstance(msg[0], tuple):
+            messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "image"}]})
+            messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
+            images.append(Image.open(msg[0][0]).convert("RGB"))
+        elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
+            # messages are already handled
+            pass
+        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str): # text only turn
+            messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
+            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # add current message
+    if len(message["files"]) == 1:
+        
+        if isinstance(message["files"][0], str): # examples
+            image = Image.open(message["files"][0]).convert("RGB")
+        else: # regular input
+            image = Image.open(message["files"][0]["path"]).convert("RGB")
+        images.append(image)
+        messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image"}]})
+    else:
+        messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})
 
-    messages.append({"role": "user", "content": message})
 
-    response = ""
+    texts = processor.apply_chat_template(messages, add_generation_prompt=True)
 
-    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
+    if images == []:
+        inputs = processor(text=texts, return_tensors="pt").to("cuda")
+    else:
+        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
+        
+    generation_kwargs = dict(inputs, max_new_tokens=max_new_tokens)
+    generated_text = ""
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    
+    for new_text in streamer:
+        buffer += new_text
+        generated_text_without_prompt = buffer
+        time.sleep(0.01)
+        yield buffer
 
-        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()
+demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA-CoT",
+      textbox=gr.MultimodalTextbox(), 
+      additional_inputs = [gr.Slider(
+              minimum=512,
+              maximum=1024,
+              value=512,
+              step=1,
+              label="Maximum number of new tokens to generate",
+          )
+        ],
+      cache_examples=False,
+      description="Upload an image, and start chatting about it. To learn more about LLaVA-CoT, visit [oir GitHub page](https://github.com/PKU-YuanGroup/LLaVA-CoT). ",
+      stop_btn="Stop Generation", 
+      fill_height=True,
+    multimodal=True)
+    
+demo.launch(debug=True)