Update app.py

app.py

@@ -0,0 +1,71 @@
+import torch
+from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
+import gradio as gr
+from PIL import Image
+
+# Use a publicly available high-capacity model.
+# For instance, we use "google/pix2struct-docvqa-large". 
+# (If you need a different model or a private one, adjust accordingly and add authentication if necessary.)
+model_name = "google/pix2struct-docvqa-large"
+
+model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
+processor = Pix2StructProcessor.from_pretrained(model_name)
+
+def solve_problem(image):
+    try:
+        # Ensure the image is in RGB.
+        image = image.convert("RGB")
+        
+        # Preprocess image and text prompt.
+        inputs = processor(
+            images=[image],
+            text="Solve the following problem:",
+            return_tensors="pt",
+            max_patches=2048
+        )
+        
+        # Generate prediction.
+        predictions = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            early_stopping=True,
+            num_beams=4,
+            temperature=0.2
+        )
+        
+        # Decode the prompt (input IDs) and the generated output.
+        problem_text = processor.decode(
+            inputs["input_ids"][0],
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True
+        )
+        solution = processor.decode(
+            predictions[0],
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True
+        )
+        return f"Problem: {problem_text}\nSolution: {solution}"
+    except Exception as e:
+        return f"Error processing image: {str(e)}"
+
+# Set up the Gradio interface.
+iface = gr.Interface(
+    fn=solve_problem,
+    inputs=gr.Image(type="pil", label="Upload Your Problem Image", image_mode="RGB"),
+    outputs=gr.Textbox(label="Solution", show_copy_button=True),
+    title="Problem Solver with Pix2Struct",
+    description=(
+        "Upload an image (for example, a handwritten math or logic problem) "
+        "and get a solution generated by a high-capacity Pix2Struct model.\n\n"
+        "Note: For best results on domain-specific tasks, consider fine-tuning on your own dataset."
+    ),
+    examples=[
+        ["example_problem1.png"],
+        ["example_problem2.jpg"]
+    ],
+    theme="soft",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()