Update app.py

app.py

@@ -1,100 +1,51 @@
-from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
+from transformers import MllamaForConditionalGeneration, AutoProcessor
 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 = "alpindale/Llama-3.2-11B-Vision-Instruct"
-model = MllamaForConditionalGeneration.from_pretrained(ckpt,
-    torch_dtype=torch.bfloat16).to("cuda")
-processor = AutoProcessor.from_pretrained(ckpt)
 
+# Initialize model and processor
+ckpt = "unsloth/Llama-3.2-11B-Vision-Instruct"
+model = MllamaForConditionalGeneration.from_pretrained(
+    ckpt,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+processor = AutoProcessor.from_pretrained(ckpt)
 
 @spaces.GPU
-def bot_streaming(message, history, max_new_tokens=250):
-    
-    txt = message["text"]
-    ext_buffer = f"{txt}"
+def extract_text(image):
+    # Convert image to RGB
+    image = Image.open(image).convert("RGB")
     
-    messages= [] 
-    images = []
+    # Create message structure
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Extract handwritten text from the image and output only the extracted text without any additional description or commentary in output"},
+                {"type": "image"}
+            ]
+        }
+    ]
     
-
-    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}]})
-
-
+    # Process input
     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 = ""
+    inputs = processor(text=texts, images=[image], return_tensors="pt").to("cuda")
     
-    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)
+    # Generate output
+    outputs = model.generate(**inputs, max_new_tokens=250)
+    result = processor.decode(outputs[0], skip_special_tokens=True)
     
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=extract_text,
+    inputs=gr.Image(type="filepath", label="Upload Image"),
+    outputs=gr.Textbox(label="Extracted Text"),
+    title="Handwritten Text Extractor",
+    description="Upload an image containing handwritten text to extract its content.",
+)
+
+# Launch the app
 demo.launch(debug=True)