Update app.py

app.py

@@ -1,35 +1,100 @@
-import os
-from transformers import AutoProcessor, AutoModelForCausalLM
-import gradio as gr
-import torch
+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 the Hugging Face token from environment variables
-hf_token = os.getenv("HF_AUTH_TOKEN")
-model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load the model and processor with authentication
-processor = AutoProcessor.from_pretrained(model_name, use_auth_token=hf_token, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=hf_token, torch_dtype=torch.float16, trust_remote_code=True).to(device)
-
-# Function to process image and text prompt
-def process_image(image, prompt="<ocr>"):
-    inputs = processor(images=image, text=prompt, return_tensors="pt").to(device)
-    outputs = model.generate(**inputs, max_new_tokens=1024)
-    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    return generated_text
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=process_image,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(value="<ocr>", label="Prompt"),
-    ],
-    outputs="text",
-    title="OCR with Llama-3.2-11B-Vision-Instruct",
-    description="Upload an image and input a prompt (e.g., '<ocr>') to extract text.",
-)
 
-iface.launch()
+@spaces.GPU
+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:
+        
+        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)