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from transformers import pipeline |
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import requests |
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import os |
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os.environ['Hugging_face']='Hugging_face' |
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HUGGINGFACEHUB_API_TOKEN = os.getenv("Hugging_face") |
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os.environ['OPENAI_API_KEY']='openAPI' |
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import streamlit as st |
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import tempfile |
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def img2text(url): |
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image_to_text = pipeline('image-to-text', model="Salesforce/blip-image-captioning-base", max_new_tokens=100) |
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text = image_to_text(url) |
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return text[0]["generated_text"] |
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from langchain.chains import LLMChain |
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from langchain.llms import OpenAI |
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from langchain.prompts import PromptTemplate |
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def generate_story(scenario): |
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template= """ |
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You are a story teller |
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You can generate a short story based on a simple narrative, the story shoule be no more than 100 words: |
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CONTEXT: {scenario} |
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STORY: |
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""" |
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prompt = PromptTemplate( |
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input_variables=["scenario"], |
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template=template, |
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) |
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chain = LLMChain(llm=OpenAI(temperature=1), prompt=prompt) |
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story = chain.run(scenario) |
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return story |
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def text2speech(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 {HUGGINGFACEHUB_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.mp3', 'wb') as file: |
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file.write(response.content) |
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def main(): |
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st.header("Turn _Images_ into Audio :red[Stories]") |
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uploaded_file = st.file_uploader("Choose an image..", type='jpg') |
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if uploaded_file is not None: |
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bytes_data = uploaded_file.getvalue() |
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with tempfile.NamedTemporaryFile(delete=False) as file: |
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file.write(bytes_data) |
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file_path = file.name |
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st.image(uploaded_file, caption='Uploaded Image',use_column_width=True) |
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scenario = img2text(file_path) |
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story = generate_story(scenario) |
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text2speech(story) |
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with st.expander("Scenario"): |
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st.write(scenario) |
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with st.expander("Story"): |
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st.write(story) |
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st.audio("audio.mp3") |
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if __name__ == "__main__": |
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main() |
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