Update app.py

app.py

@@ -1,114 +1,96 @@
 from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
 from PIL import Image
+import requests
 import torch
 from threading import Thread
 import gradio as gr
+from gradio import FileData
 import time
 import spaces
 import re
-
 ckpt = "Xkev/Llama-3.2V-11B-cot"
-model = MllamaForConditionalGeneration.from_pretrained(
-    ckpt, torch_dtype=torch.bfloat16
-).to("cuda").eval()
+model = MllamaForConditionalGeneration.from_pretrained(ckpt,
+    torch_dtype=torch.bfloat16).to("cuda")
 processor = AutoProcessor.from_pretrained(ckpt)
-tokenizer = processor.tokenizer
-
-def _build_messages_and_images(history, curr_message):
-    messages = []
-    images = []
-
-    for user_msg, assistant_msg in history:
-        user_text = ""
-        user_image = None
-
-        if isinstance(user_msg, dict):
-            user_text = user_msg.get("text") or ""
-            files = user_msg.get("files") or []
-            if files:
-                fp = files[0] if isinstance(files[0], str) else files[0]["path"]
-                user_image = Image.open(fp).convert("RGB")
-        elif isinstance(user_msg, str):
-            user_text = user_msg
-
-        # user
-        content = [{"type": "text", "text": user_text}]
-        if user_image is not None:
-            content.append({"type": "image"})
-            images.append(user_image)
-        messages.append({"role": "user", "content": content})
-
-        # assistant
-        if isinstance(assistant_msg, str):
-            messages.append({"role": "assistant", "content": [{"type": "text", "text": assistant_msg}]})
-
-    curr_text = curr_message.get("text") or ""
-    files = curr_message.get("files") or []
-    content = [{"type": "text", "text": curr_text}]
-    if len(files) >= 1:
-        fp = files[0] if isinstance(files[0], str) else files[0]["path"]
-        img = Image.open(fp).convert("RGB")
-        images.append(img)
-        content.append({"type": "image"})
-    messages.append({"role": "user", "content": content})
-
-    return messages, images
 
 @spaces.GPU
 def bot_streaming(message, history, max_new_tokens=250):
-    try:
-        messages, images = _build_messages_and_images(history, message)
-
-        # chat template
-        texts = processor.apply_chat_template(messages, add_generation_prompt=True)
-
-        if images:
-            inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
-        else:
-            inputs = processor(text=texts, return_tensors="pt").to("cuda")
-
-        streamer = TextIteratorStreamer(
-            tokenizer, skip_special_tokens=True, skip_prompt=True)
-
-        generation_kwargs = dict(
-            inputs,
-            streamer=streamer,
-            max_new_tokens=int(max_new_tokens),
-            do_sample=True,
-            temperature=0.6,
-            top_p=0.9,
-        )
-
-        thread = Thread(target=model.generate, kwargs=generation_kwargs, daemon=True)
-        thread.start()
-
-
-        for new_text in streamer:
-            safe = re.sub(r"</?(\w+)>", lambda m: m.group(0).replace("<", "\\<").replace(">", "\\>"), new_text)
-            yield safe
-            time.sleep(0.005)
-
-        thread.join(timeout=0.1)
-
-    except Exception as e:
-        yield f"[Error] {type(e).__name__}: {e}"
-
-demo = gr.ChatInterface(
-    fn=bot_streaming,
-    title="LLaVA-CoT",
-    textbox=gr.MultimodalTextbox(),
-    additional_inputs=[
-        gr.Slider(minimum=64, maximum=1024, value=512, step=1, label="Maximum number of new tokens")
-    ],
-    examples=[
-        [{"text": "What is on the flower?", "files": ["./Example1.webp"]}, 512],
-        [{"text": "How to make this pastry?", "files": ["./Example2.png"]}, 512],
-    ],
-    cache_examples=False,
-    description="Upload an image, and start chatting about it. To learn more about LLaVA-CoT, visit our GitHub.",
-    stop_btn="Stop Generation",
-    fill_height=True,
-    multimodal=True,
-)
-
-demo.launch(debug=True)
+    
+    txt = message["text"]
+    ext_buffer = f"{txt}"
+    
+    messages= [] 
+    images = []
+    
+
+    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]}]})
+
+    # 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}]})
+
+
+    texts = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+    if images == []:
+        inputs = processor(text=texts, return_tensors="pt").to("cuda")
+    else:
+        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
+        
+    streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
+
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=0.6, top_p=0.9)
+    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)
+    
+        buffer = re.sub(r"<(\w+)>", r"\<\1\>", buffer)  
+        buffer = re.sub(r"</(\w+)>", r"\</\1\>", buffer)    
+    
+        yield buffer
+
+
+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",
+          )
+        ],
+      examples=[[{"text": "What is on the flower?", "files": ["./Example1.webp"]},512],
+                [{"text": "How to make this pastry?", "files": ["./Example2.png"]},512]],
+      cache_examples=False,
+      description="Upload an image, and start chatting about it. To learn more about LLaVA-CoT, visit [our GitHub page](https://github.com/PKU-YuanGroup/LLaVA-CoT).",
+      stop_btn="Stop Generation", 
+      fill_height=True,
+    multimodal=True)
+    
+demo.launch(debug=True)