Delete app.py

app.py

@@ -1,46 +0,0 @@
-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.")