Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import easyocr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Define a function to extract text from an image using easyocr
+def extract_text(image):
+    reader = easyocr.Reader(['en'])
+    extracted_text = reader.readtext(image)
+    return ' '.join([result[1] for result in extracted_text])
+
+# Define a function to generate an explanation using the "HuggingFaceH4/zephyr-7b-alpha" model
+def explain_text(text, tokenizer, model):
+    input_ids = tokenizer.encode(text, return_tensors="pt", max_length=256, truncation=True)
+    explanation_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)
+    explanation = tokenizer.decode(explanation_ids[0], skip_special_tokens=True)
+    return explanation
+
+st.title("Text Extraction and Explanation")
+
+# Allow users to upload an image
+uploaded_file = st.file_uploader("Upload an image:")
+
+# Load the text generation model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
+model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
+
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = Image.open(uploaded_file)
+
+    # Extract text from the image using easyocr
+    extracted_text = extract_text(image)
+
+    # Explain the extracted text using the "HuggingFaceH4/zephyr-7b-alpha" model
+    explanation = explain_text(extracted_text, tokenizer, model)
+
+    # 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.")