Update app.py

app.py

@@ -1,47 +1,100 @@
-import gradio as gr
-from transformers import AutoProcessor, AutoModelForImageTextToText
+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
+ckpt = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+model = MllamaForConditionalGeneration.from_pretrained(ckpt,
+    torch_dtype=torch.bfloat16).to("cuda")
+processor = AutoProcessor.from_pretrained(ckpt)
 
-# Load Vision-Instruct model
-processor = AutoProcessor.from_pretrained("alpindale/Llama-3.2-11B-Vision-Instruct")
-model = AutoModelForImageTextToText.from_pretrained("alpindale/Llama-3.2-11B-Vision-Instruct")
-
-# Move model to GPU
-model.to("cuda")
 
 @spaces.GPU
-def extract_text_from_image(image):
-    """
-    Function to extract text from a handwritten image using the Meta-Llama model.
-    """
-    try:
-        # Preprocess the image
-        inputs = processor(images=image, return_tensors="pt").to("cuda")
-        
-        # Generate predictions
-        outputs = model.generate(**inputs)
-        
-        # Decode the generated text
-        extracted_text = processor.decode(outputs[0], skip_special_tokens=True)
+def bot_streaming(message, history, max_new_tokens=250):
+    
+    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:
         
-        return extracted_text
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# Define Gradio interface
-title = "Handwritten Text Extraction"
-description = """
-Upload a handwritten image, and this app will use Meta-Llama Vision-Instruct to extract text from the image.
-"""
-
-demo = gr.Interface(
-    fn=extract_text_from_image,
-    inputs=gr.Image(type="pil", label="Upload Handwritten Image"),
-    outputs=gr.Textbox(label="Extracted Text"),
-    title=title,
-    description=description,
-)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+        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)
+    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
+
+
+demo = gr.ChatInterface(fn=bot_streaming, title="Multimodal Llama", examples=[
+    [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]},
+    200],
+    [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]},
+    250],
+    [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]},
+    250],
+    [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]},
+    250],
+    [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 
+    250],
+    ],
+      textbox=gr.MultimodalTextbox(), 
+      additional_inputs = [gr.Slider(
+              minimum=10,
+              maximum=500,
+              value=250,
+              step=10,
+              label="Maximum number of new tokens to generate",
+          )
+        ],
+      cache_examples=False,
+      description="Try Multimodal Llama by Meta with transformers in this demo. Upload an image, and start chatting about it, or simply try one of the examples below. To learn more about Llama Vision, visit [our blog post](https://huggingface.co/blog/llama32). ",
+      stop_btn="Stop Generation", 
+      fill_height=True,
+    multimodal=True)
+    
+demo.launch(debug=True)