Spaces:
Running
Running
| import os | |
| import requests | |
| import streamlit as st | |
| from dotenv import find_dotenv, load_dotenv | |
| from transformers import pipeline | |
| from langchain import PromptTemplate, LLMChain | |
| from langchain.llms import GooglePalm | |
| load_dotenv(find_dotenv()) | |
| llm = GooglePalm(temperature=0.9, google_api_key=os.getenv("GOOGLE_API_KEY")) | |
| # Iamge to Text | |
| def image_to_text(url): | |
| #load a transformer | |
| 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 very good story teller and a very rude person: | |
| you can generate a short fairy tail based on a single narrative, the story should take 5 seconds to read. | |
| CONTEXT: {scenario} | |
| STORY: | |
| """ | |
| prompt = PromptTemplate(template=template, input_variables=["scenario"]) | |
| story_llm = LLMChain(llm=llm, prompt=prompt, verbose=True) | |
| story = story_llm.predict(scenario=scenario) | |
| print(story) | |
| return story | |
| # text to speech | |
| def text_to_speech(message): | |
| API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits" | |
| headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_TOKEN')}"} | |
| payload = {"inputs": message} | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| print(response.content) | |
| with open('audio.mp3', 'wb') as audio_file: | |
| audio_file.write(response.content) | |
| def main(): | |
| st.set_page_config(page_title="Image to Story", page_icon="📚", layout="wide") | |
| st.title("Image to Story") | |
| uploaded_file = st.file_uploader("Choose an image...", type="png") | |
| if uploaded_file is not None: | |
| 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 = image_to_text(uploaded_file.name) | |
| story = generate_story(scenario) | |
| text_to_speech(story) | |
| with st.expander("scenerio"): | |
| st.write(scenario) | |
| with st.expander("story"): | |
| st.write(story) | |
| st.audio("audio.mp3") | |
| if __name__ == '__main__': | |
| main() |