Update app.py

app.py

@@ -13,55 +13,47 @@ model = MllamaForConditionalGeneration.from_pretrained(
 processor = AutoProcessor.from_pretrained(ckpt)
 
 @spaces.GPU
-def extract_text(image_path):
-    try:
-        # Handle different types of image inputs
-        if isinstance(image_path, dict) and "path" in image_path:
-            image_path = image_path["path"]
-        
-        # Convert image to RGB
-        image = Image.open(image_path).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", "").replace("Extract handwritten text from the image and output only the extracted text without any additional description or commentary in output", "").strip()
-        
-        return f"\n{result}\n"
-        
-    except Exception as e:
-        return f"Error processing image: {str(e)}"
+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)
+
+    # 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()
+    
+    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", lines=10),  # Increased lines for better visibility
+    outputs=gr.Textbox(label="Extracted Text"),
     title="Handwritten Text Extractor",
     description="Upload an image containing handwritten text to extract its content.",
-    examples=[],  # Add example images here if needed
 )
 
-# Launch the app with queue enabled
-demo.queue()  # Enable queuing
+# Launch the app
 demo.launch(debug=True)