Update app.py

app.py

@@ -1,32 +1,43 @@
 import streamlit as st
-import easyocr
-from gtts import gTTS
-from IPython.display import Audio
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from easyocr import Reader
 
-# Create Streamlit app title
-st.title("Image Text-to-Speech App")
+# Load the OCR model and text explanation model (gpt-2 as an example)
+ocr_reader = Reader(['en'])
+explainer = AutoModelForSequenceClassification.from_pretrained("gpt2")
 
-# Upload image
-uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+# Define a function to extract text from an image
+def extract_text(image):
+    return ocr_reader.readtext(image)
 
-if uploaded_image is not None:
-    image = uploaded_image.read()
+# 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
 
-    # Perform OCR on the uploaded image
-    st.header("Text Extracted from Image:")
-    reader = easyocr.Reader(['en'])
-    result = reader.readtext(image)
-    extracted_text = " ".join([res[1] for res in result])
-    st.write(extracted_text)
+# Create a Streamlit layout
+st.title("Text Classification Model")
 
-    # Perform text-to-speech conversion
-    st.header("Text-to-Speech:")
-    tts = gTTS(extracted_text)
-    tts.save("output.mp3")
+# Allow users to upload an image
+uploaded_file = st.file_uploader("Upload an image:")
 
-    # Display the audio player
-    st.audio("output.mp3")
+# Extract text from the uploaded image
+if uploaded_file is not None:
+    image = torch.from_numpy(uploaded_file.read()).unsqueeze(0)
+    extracted_text = extract_text(image)
 
-# Information for the user
-st.info("Upload an image, and this app will extract the text and convert it to speech.")
+    # Explain the extracted text
+    explanation = explain_text(extracted_text)
 
+    # 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.")