Delete app.py

app.py

@@ -1,45 +0,0 @@
-import streamlit as st
-from PIL import Image
-import io
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
-from easyocr import Reader
-
-ocr_reader = Reader(['en'])
-text_generator = AutoModelForCausalLM.from_pretrained("bart")
-text_tokenizer = AutoTokenizer.from_pretrained("bart")
-explainer = AutoModelForSeq2SeqLM.from_pretrained("bart-explainer")
-
-def extract_text(image):
-    return ocr_reader.readtext(image)
-
-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 = explainer.generate(input_ids, max_length=100, num_return_sequences=1)
-    explanation = text_tokenizer.decode(explanation_ids[0], skip_special_tokens=True)
-    
-    return explanation
-
-# Create a Streamlit layout
-st.title("Text Extraction and Explanation")
-
-# Allow users to upload an image
-uploaded_file = st.file_uploader("Upload an image:")
-
-# Extract text from the uploaded image and explain it
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    ocr_results = extract_text(image)
-    explanation = explain_text(ocr_results, explainer, text_tokenizer)
-
-    st.markdown("**Extracted text:**")
-    st.markdown(" ".join([res[1] for res in ocr_results]))
-
-    st.markdown("**Explanation:**")
-    st.markdown(explanation)
-
-else:
-    st.markdown("Please upload an image to extract text and get an explanation.")