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pip install transformers |
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import streamlit as st |
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import transformers |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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from transformers import AutoProcessor, AutoModelForZeroShotImageClassification |
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pipe = pipeline("summarization", model="google/pegasus-xsum") |
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agepipe = pipeline("image-classification", model="dima806/facial_age_image_detection") |
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imgpipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32") |
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emopipe = pipeline("text-classification", model="michellejieli/emotion_text_classifier") |
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transpipe = pipeline("translation_en_to_fr") |
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st.title("NLP APP") |
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option = st.sidebar.selectbox( |
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"Choose a task", |
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("Summarization", "Age Detection", "Emotion Detection", "Image Classification", "Translation") |
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) |
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if option == "Summarization": |
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st.title("Text Summarization") |
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text = st.text_area("Enter text to summarize") |
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if st.button("Summarize"): |
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if text: |
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st.write("Summary:", pipe(text)[0]["summary_text"]) |
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else: |
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st.write("Please enter text to summarize.") |
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elif option == "Age Detection": |
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st.title("Welcome to age detection") |
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uploaded_files = st.file_uploader("Choose a image file", type="jpg") |
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if uploaded_files is not None: |
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image = Image.open(uploaded_files) |
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st.write("Detected age is ", agepipe(image)[0]["label"]) |
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elif option == "Image Classification": |
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st.title("Welcome to object detection") |
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uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"]) |
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text = st.text_area("Enter possible class names (comma-separated)") |
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if st.button("Submit"): |
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if uploaded_file is not None and text: |
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candidate_labels = [t.strip() for t in text.split(',')] |
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image = Image.open(uploaded_file) |
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st.image(image, caption="Uploaded Image", use_column_width=True) |
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classification_result = imgpipe(image, candidate_labels=candidate_labels) |
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for result in classification_result: |
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st.write(f"Label: {result['label']}, Score: {result['score']}") |
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else: |
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st.write("Please upload an image file and enter class names.") |
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elif option == "Emotion Detection": |
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st.title("Detect your emotion") |
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text = st.text_area("Enter your text") |
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if st.button("Submit"): |
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if text: |
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emotion = emopipe(text)[0]["label"] |
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if emotion == "sadness": |
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st.write("Emotion : ", emotion, "😢") |
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elif emotion == "joy": |
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st.write("Emotion : ", emotion, "😃") |
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elif emotion == "fear": |
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st.write("Emotion : ", emotion, "😨") |
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elif emotion == "anger": |
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st.write("Emotion : ", emotion, "😡") |
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elif emotion == "neutral": |
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st.write("Emotion : ", emotion, "😐") |
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elif emotion == "disgust": |
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st.write("Emotion : ", emotion, "🤢") |
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elif emotion == "surprise": |
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st.write("Emotion : ", emotion, "😲") |
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else: |
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st.write("Please enter text.") |
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elif option == "Translation": |
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st.title("Text Translation") |
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text = st.text_area("Enter text to translate from English to French") |
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if st.button("Translate"): |
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if text: |
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translation = transpipe(text)[0]["translation_text"] |
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st.write("Translation:", translation) |
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else: |
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st.write("Please enter text to translate.") |
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else: |
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st.title("None") |
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