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from dotenv import find_dotenv, load_dotenv |
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from transformers import pipeline |
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import os |
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import requests |
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import streamlit as st |
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load_dotenv(find_dotenv()) |
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HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN") |
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pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") |
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def img_to_text(url): |
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text = pipe(url)[0]["generated_text"] |
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print(text) |
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return text |
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def text_to_speech(message): |
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API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits" |
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headers = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"} |
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payloads = { |
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"inputs":message |
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} |
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response = requests.post(API_URL, headers=headers, json=payloads) |
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with open('audio.flac', 'wb') as file: |
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file.write(response.content) |
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def main(): |
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st.set_page_config(page_title="Image to Text", page_icon="🎙️") |
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st.header("Image to Text") |
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image = "narrator.jpeg" |
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left_co, cent_co, last_co = st.columns(3) |
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with cent_co: |
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st.image(image=image) |
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uploaded_file = st.file_uploader("Choose an image: ", type=["jpg", "jpeg", "png"]) |
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if uploaded_file is not None: |
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print(uploaded_file) |
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bytes_data = uploaded_file.getvalue() |
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with open(uploaded_file.name, "wb") as file: |
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file.write(bytes_data) |
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st.image(uploaded_file, caption='Uploaded image', use_column_width=True) |
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scenario=img_to_text(uploaded_file.name) |
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text_to_speech(scenario) |
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with st.expander("scenatio"): |
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st.write(scenario) |
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st.audio("audio.flac") |
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if __name__== "__main__": |
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main() |
