Update app.py

app.py

@@ -2,7 +2,8 @@ from transformers import MllamaForConditionalGeneration, AutoProcessor
 from PIL import Image
 import torch
 import gradio as gr
-import spaces
+import requests
+from io import BytesIO
 
 # Initialize model and processor
 ckpt = "unsloth/Llama-3.2-11B-Vision-Instruct"
@@ -12,52 +13,57 @@ model = MllamaForConditionalGeneration.from_pretrained(
 ).to("cuda")
 processor = AutoProcessor.from_pretrained(ckpt)
 
-@spaces.GPU
-def extract_text(image):
-    # Convert image to RGB
-    image = Image.open(image).convert("RGB")
-    
-    # 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"}
-            ]
-        }
-    ]
-    
-    # Process input
-    texts = processor.apply_chat_template(messages, add_generation_prompt=True)
-    inputs = processor(text=texts, images=[image], return_tensors="pt").to("cuda")
-
-    
-    # Generate output
-    outputs = model.generate(**inputs, max_new_tokens=250)
-    result = processor.decode(outputs[0], skip_special_tokens=True)
-
-    print(result)
-    
-    # Clean up the output to remove the prompt and assistant text
-    if "assistant" in result.lower():
-        result = result[result.lower().find("assistant") + len("assistant"):].strip()
-    
-    # Remove any remaining conversation markers
-    result = result.replace("user", "").replace("Extract handwritten text from the image and output only the extracted text without any additional description or commentary in output", "").strip()
-
-    print(result)
-    
-    return result
+def extract_text(image_input):
+    """
+    Extract handwritten text from the given image.
+    `image_input` can be a file path or a URL.
+    """
+    try:
+        # Handle file upload or URL
+        if isinstance(image_input, str) and image_input.startswith("http"):
+            response = requests.get(image_input)
+            response.raise_for_status()  # Check for errors
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+        else:
+            image = Image.open(image_input).convert("RGB")
+
+        # 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"}
+                ]
+            }
+        ]
+
+        # Process input
+        texts = processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = processor(text=texts, images=[image], return_tensors="pt").to("cuda")
+
+        # Generate output
+        outputs = model.generate(**inputs, max_new_tokens=250)
+        result = processor.decode(outputs[0], skip_special_tokens=True)
+
+        # Clean up the output
+        if "assistant" in result.lower():
+            result = result[result.lower().find("assistant") + len("assistant"):].strip()
+        result = result.replace("user", "").strip()
+
+        return result
+
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Create Gradio interface
 demo = gr.Interface(
     fn=extract_text,
-    inputs=gr.Image(type="filepath", label="Upload Image"),
+    inputs=gr.Textbox(label="Image URL or Upload Image"),
     outputs=gr.Textbox(label="Extracted Text"),
     title="Handwritten Text Extractor",
-    description="Upload an image containing handwritten text to extract its content.",
+    description="Provide an image URL or upload an image containing handwritten text to extract its content.",
 )
 
 # Launch the app
-demo.launch(debug=True)
+demo.launch(debug=True)