Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from easyocr import Reader
+
+# Load the OCR model and explanation model
+ocr_reader = Reader(['en'])
+explainer = AutoModelForSequenceClassification.from_pretrained("bart-explainer")
+
+def extract_text(image):
+    return ocr_reader.readtext(image)
+
+# Define a function to explain the extracted text
+def explain_text(text):
+    tokenizer = AutoTokenizer.from_pretrained("bart-large")
+    encoded_text = tokenizer(text, return_tensors="pt")
+    explanation = explainer.generate(encoded_text)
+    return explanation[0]
+
+# Create a Streamlit layout
+st.title("Text Classification Model")
+
+# Allow users to upload an image
+uploaded_file = st.file_uploader("Upload an image:")
+
+# Extract text from the uploaded image
+if uploaded_file is not None:
+    image = torch.from_numpy(uploaded_file.read()).unsqueeze(0)
+    extracted_text = extract_text(image)
+
+    # Explain the extracted text
+    explanation = explain_text(extracted_text)
+
+    # Display the extracted text and explanation
+    st.markdown("**Extracted text:**")
+    st.markdown(extracted_text)
+
+    st.markdown("**Explanation:**")
+    st.markdown(explanation)
+
+else:
+    st.markdown("Please upload an image to extract text and get an explanation.")