import streamlit as st
import transformers
from transformers import pipeline
import PIL
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification

pipe = pipeline("summarization", model="google/pegasus-xsum")
agepipe = pipeline("image-classification", model="dima806/facial_age_image_detection")
imgpipe = pipeline("zero-shot-image-classification", model="google/siglip-so400m-patch14-384")


st.title("NLP APP")
option = st.sidebar.selectbox(
    "Choose a task",
    ("Summarization", "Age Detection", "Emotion Detection", "Image Classification")
)
if option == "Summarization":
    st.title("Text Summarization")
    text = st.text_area("Enter text to summarize")
    if st.button("Summarize"):
        if text:
            st.write("Summary:", pipe(text)[0]["summary_text"])
        else:
            st.write("Please enter text to summarize.")
elif option == "Age Detection":
    st.title("Welcome to age detection")

    uploaded_files = st.file_uploader("Choose a image file",type="jpg")

    if uploaded_files is not None:
        Image=Image.open(uploaded_files)

        st.write(agepipe(Image)[0]["label"])
elif option == "Image Classification":
    st.title("Welcome to object detection")

    uploaded_files = st.file_uploader("Choose a image file",type=["jpg","jpeg"])
    text=st.text_area("Enter possible class names(comma separated")
    candidate_lables=[t.strip() for t in text.split(',')]
    if uploaded_files is not None:
        Image=Image.open(uploaded_files)
        
        outputs = imgpipe(uploaded_files,candidate_lables)

        st.write(output["label"])
        

else:
    st.title("None")