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import streamlit as st | |
from funs import * | |
from transformers import pipeline | |
import torch | |
from PIL import Image | |
feel = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions") #text classifier, it feels | |
knower = pipeline("text-generation", model="bigscience/bloom") #text generation, it handles | |
st.title("Comp-anion") | |
st.subheader("Comp-anion is a computer companion! Upload either text or a photo from your journal to get some insight and compassion from your comp-anion!") | |
u_file = st.file_uploader("Choose a file") | |
if u_file is not None:#when file gets uploaded | |
seer = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") #image to text , it sees | |
u_file = preprocess(u_file) | |
if precheck(get_context(seer,u_file)):#precheck preprocessed img | |
st.write("Submission:") | |
the_context=get_content(seer, u_file) | |
st.write(the_context) #write out the img to text | |
emotion_found = emotion(feel,the_context) | |
st.write(emotion_found) | |
handle(knower,emotion_found,the_context) | |
st.subheader("Pictures aren't your style? Paste your text below and hit analyze!") | |
text_box=st.text_input("Paste Your Text Here :)", value="I've had a really nice day today") | |
if st.button("Analyze"): | |
prompt="Give advice based on these inputs emotion={} , text given={} " | |
emotion_found=emotion(feel,text_box) | |
prompt = prompt.format(emotion_found , prompt) | |
st.write(prompt) | |
st.write(emotion_found) | |
generation=handle(knower,prompt) | |
st.write(generation[:len(text_box)]) |