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import streamlit as st
from transformers import pipeline
from PIL import Image
# Load pipelines
image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
text_to_speech = pipeline("text-to-speech", model="facebook/mms-tts-eng")
st.title("Image-to-Text and Text-to-Speech App")
# Image uploader
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_image:
image = Image.open(uploaded_image)
st.image(image, caption="Uploaded Image", use_container_width=True)
# Convert image to text
text_output = image_to_text(image)[0]['generated_text']
st.write("### Extracted Text:")
st.write(text_output)
# Convert text to speech
speech_output = text_to_speech(text_output)
st.write("### Listen to Speech Output:")
st.audio(speech_output['audio'],
format="audio/wav",
start_time=0,
sample_rate = speech_output['sampling_rate'])
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