Update app.py

app.py

@@ -1,28 +1,24 @@
 import streamlit as st
 from PIL import Image
 import io
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
 from easyocr import Reader
 
-# Load the OCR model and text explanation model
 ocr_reader = Reader(['en'])
+text_generator = AutoModelForCausalLM.from_pretrained("bart")
+text_tokenizer = AutoTokenizer.from_pretrained("bart")
+explainer = AutoModelForSeq2SeqLM.from_pretrained("bart-explainer")
 
-# Load the text explanation model
-text_generator = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
-text_tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
-
-# Define a function to extract text from an image using OCR
 def extract_text(image):
     return ocr_reader.readtext(image)
 
-# Define a function to explain the extracted text using text generation
-def explain_text(text, text_generator, text_tokenizer):
+def explain_text(text, explainer, text_tokenizer):
     # Extracted text
     extracted_text = " ".join([res[1] for res in text])
 
     # Generate an explanation using the text explanation model
     input_ids = text_tokenizer.encode(extracted_text, return_tensors="pt")
-    explanation_ids = text_generator.generate(input_ids, max_length=100, num_return_sequences=1)
+    explanation_ids = explainer.generate(input_ids, max_length=100, num_return_sequences=1)
     explanation = text_tokenizer.decode(explanation_ids[0], skip_special_tokens=True)
     
     return explanation
@@ -37,7 +33,7 @@ uploaded_file = st.file_uploader("Upload an image:")
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
     ocr_results = extract_text(image)
-    explanation = explain_text(ocr_results, text_generator, text_tokenizer)
+    explanation = explain_text(ocr_results, explainer, text_tokenizer)
 
     st.markdown("**Extracted text:**")
     st.markdown(" ".join([res[1] for res in ocr_results]))