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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 | |
# | |
# 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="ζθΏ°εη", page_icon="π", layout="wide") | |
st.title("ζθΏ°εη") | |
uploaded_file = st.file_uploader("Choose an image...", type=["jpg","png","jpeg"]) | |
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) | |
with st.expander("ζδΈθ§ηεηζθΏ°"): | |
st.write(scenario) | |
if __name__ == '__main__': | |
main() |