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from PIL import Image |
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import requests |
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from io import BytesIO |
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from transformers import pipeline |
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import streamlit as st |
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def predict(image): |
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type_food = oracle(image, "What type of food is this?") |
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cal_est = oracle(image, "About how many calories are in this meal?") |
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guess1, guess2 = int(cal_est[0]['answer']), int(cal_est[1]['answer']) |
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return f"This is {type_food[0]['answer']}. I estimate this to contain {min(guess1, guess2)}-{max(guess1, guess2)} calories" |
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oracle = pipeline(model="dandelin/vilt-b32-finetuned-vqa") |
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def main(): |
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st.title("Image Question Answering App") |
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st.write("Upload an image and ask a question to get answers!") |
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uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) |
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if uploaded_image is not None: |
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image = Image.open(uploaded_image) |
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st.image(image, caption="Uploaded Image", use_column_width=True) |
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response = predict(image) |
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st.write(response) |
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if __name__ == "__main__": |
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main() |
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