Update app.py

app.py

@@ -1,50 +1,2 @@
-import torch
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-import gradio as gr
-from PIL import Image
 
-# Use a valid model identifier. Here we use "google/matcha-base".
-model_name = "google/matcha-base"
-
-# Load the pre-trained Pix2Struct model and processor
-model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
-processor = Pix2StructProcessor.from_pretrained(model_name)
-
-# Move model to GPU if available and set to evaluation mode
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-model.eval()
-
-def solve_math_problem(image):
-    # Preprocess the image and include a prompt.
-    inputs = processor(images=image, text="Solve the math problem:", return_tensors="pt")
-    # Move all tensors to the same device as the model
-    inputs = {key: value.to(device) for key, value in inputs.items()}
-    
-    # Generate the solution using beam search within a no_grad context
-    with torch.no_grad():
-        predictions = model.generate(
-            **inputs,
-            max_new_tokens=150,  # Increase this if longer answers are needed
-            num_beams=5,         # Beam search for more stable outputs
-            early_stopping=True,
-            temperature=0.5      # Lower temperature for more deterministic output
-        )
-    
-    # Decode the generated tokens to a string, skipping special tokens
-    solution = processor.decode(predictions[0], skip_special_tokens=True)
-    return solution
-
-# Set up the Gradio interface
-demo = gr.Interface(
-    fn=solve_math_problem,
-    inputs=gr.Image(type="pil", label="Upload Handwritten Math Problem"),
-    outputs=gr.Textbox(label="Solution"),
-    title="Handwritten Math Problem Solver",
-    description="Upload an image of a handwritten math problem and the model will attempt to solve it.",
-    theme="soft"
-)
-
-if __name__ == "__main__":
-    demo.launch()