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| from dotenv import find_dotenv, load_dotenv | |
| from transformers import pipeline | |
| from langchain import PromptTemplate, LLMChain, OpenAI | |
| import requests | |
| import os | |
| import streamlit as st | |
| load_dotenv (find_dotenv()) | |
| HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| #img2text | |
| def img2text(url): | |
| image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") | |
| text = image_to_text(url)[0]["generated_text"] | |
| print(text) | |
| return text | |
| #llm | |
| def generate_story(scenario): | |
| template = """ | |
| You are a story teller; | |
| You can generate a short story based on a simple narrative, the story should be no more than 50 words; | |
| CONTEXT: {scenario} | |
| STORY: | |
| """ | |
| prompt = PromptTemplate(template=template, input_variables=["scenario"]) | |
| story_llm = LLMChain(llm=OpenAI( | |
| model_name="gpt-3.5-turbo", temperature=1), prompt=prompt, verbose=True) | |
| story = story_llm.predict(scenario=scenario) | |
| print(story) | |
| return story | |
| #text to speech | |
| def text2speech(message): | |
| #API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits" | |
| API_URL = "https://api-inference.huggingface.co/models/facebook/mms-tts-fra" | |
| headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"} | |
| payloads = { | |
| "inputs": message | |
| } | |
| response = requests.post(API_URL, headers=headers, json=payloads) | |
| with open('audio.wav', 'wb') as file: | |
| file.write(response.content) | |
| #scenario = img2text("mmd.png") | |
| #story = generate_story(scenario) | |
| #en_fr_translator = pipeline("translation_en_to_fr") | |
| #story_fr = en_fr_translator(story)[0]["translation_text"] | |
| #print(story_fr) | |
| #text2speech(story_fr) | |
| def main(): | |
| st.set_page_config(page_title="Img 2 audio story") | |
| st.header("Turn img into audio story") | |
| uploaded_file = st.file_uploader("Choose an image....", type="jpg") | |
| if uploaded_file is not None: | |
| print(uploaded_file) | |
| bytes_data = uploaded_file.getvalue() | |
| with open(uploaded_file.name, "wb") as file: | |
| file.write(bytes_data) | |
| st.image(uploaded_file, caption='Uploaded Image.', | |
| use_column_width=True) | |
| scenario = img2text(uploaded_file.name) | |
| story = generate_story(scenario) | |
| en_fr_translator = pipeline("translation_en_to_fr") | |
| story_fr = en_fr_translator(story)[0]["translation_text"] | |
| text2speech(story_fr) | |
| with st.expander("scenario"): | |
| st.write(scenario) | |
| with st.expander("story"): | |
| st.write(story_fr) | |
| st.audio("audio.wav") | |
| if __name__ == '__main__': | |
| main() | |