Create app.py

app.py

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+import streamlit as st
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from PIL import Image
+import torch
+
+# Load model and processor
+@st.cache_resource  # Cache model to avoid reloading
+def load_model():
+    processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
+    model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM-Instruct")
+    return processor, model
+
+# Extract text from image using SmolVLM
+def extract_text(image, processor, model):
+    # Preprocess image
+    inputs = processor(images=image, text="What is the text in this image?", return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(**inputs)
+
+    result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    return result
+
+# Streamlit UI
+def main():
+    st.title("🖼️ OCR App using SmolVLM")
+    st.write("Upload an image, and I will extract the text for you!")
+
+    # Load the model and processor
+    processor, model = load_model()
+
+    # File uploader
+    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Open image
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Extract text
+        with st.spinner("Extracting text..."):
+            extracted_text = extract_text(image, processor, model)
+
+        # Display result
+        st.subheader("📝 Extracted Text:")
+        st.write(extracted_text)
+
+if __name__ == "__main__":
+    main()