Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model and processor
+@st.cache_resource
+def load_model():
+    model_name = "tjoab/latex_finetuned"
+    processor = AutoProcessor.from_pretrained(model_name)
+    model = AutoModelForVision2Seq.from_pretrained(model_name).to(device)
+    return processor, model
+
+
+
+processor, model = load_model()
+
+st.title("LaTeX Image to Text Converter")
+st.write("Upload an image containing a handwritten or printed math expression, and get the LaTeX code.")
+
+# TODO: Add .png support (doesnt work as is with PIL Image.open())
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess image as model expects, then run inference
+    with st.spinner("Processing..."):
+        preproc_image = processor.image_processor(image, return_tensors="pt").pixel_values
+        preproc_image = preproc_image.to(device)
+
+        pred_ids = model.generate(preproc_image, max_length=128)
+        latex_pred = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]
+
+    st.subheader("Predicted LaTeX Code:")
+    st.code(latex_pred, language="latex")