Update app.py

app.py

@@ -1,38 +1,26 @@
 import streamlit as st
+import io
+from PIL import Image
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from easyocr import Reader
 
-# Load the OCR model and text explanation model (gpt-2 as an example)
 ocr_reader = Reader(['en'])
 explainer = AutoModelForSequenceClassification.from_pretrained("gpt2")
-
-# Define a function to extract text from an image
 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("gpt2")
     input_ids = tokenizer.encode(text, return_tensors="pt")
     explanation = explainer(input_ids)
     return explanation
-
-# 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)
+    image = Image.open(uploaded_file)
+    
     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)