Update app.py

app.py

@@ -1,42 +1,32 @@
 import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from easyocr import Reader
+import easyocr
+from gtts import gTTS
+from IPython.display import Audio
 
-# Load the OCR model and explanation model
-ocr_reader = Reader(['en'])
-explainer = AutoModelForSequenceClassification.from_pretrained("bart-explainer")
+# Create Streamlit app title
+st.title("Image Text-to-Speech App")
 
-def extract_text(image):
-    return ocr_reader.readtext(image)
+# Upload image
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
 
-# Define a function to explain the extracted text
-def explain_text(text):
-    tokenizer = AutoTokenizer.from_pretrained("bart-large")
-    encoded_text = tokenizer(text, return_tensors="pt")
-    explanation = explainer.generate(encoded_text)
-    return explanation[0]
+if uploaded_image is not None:
+    image = uploaded_image.read()
 
-# Create a Streamlit layout
-st.title("Text Classification Model")
+    # 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)
 
-# Allow users to upload an image
-uploaded_file = st.file_uploader("Upload an image:")
+    # Perform text-to-speech conversion
+    st.header("Text-to-Speech:")
+    tts = gTTS(extracted_text)
+    tts.save("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)
+    # Display the audio player
+    st.audio("output.mp3")
 
-    # Explain the extracted text
-    explanation = explain_text(extracted_text)
+# Information for the user
+st.info("Upload an image, and this app will extract the text and convert it to speech.")
 
-    # 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.")