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import streamlit as st |
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from roboflow import Roboflow |
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from PIL import Image |
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import numpy as np |
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rf = Roboflow(api_key="uhog8pgJ2wfEPN5SrmBG") |
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project = rf.workspace().project("arabic-sl") |
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model = project.version(17).model |
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st.title("Image Classification with Streamlit") |
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option = st.selectbox( |
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'Select the approach to test the model', |
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('UploadImage', 'CameraInput')) |
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if option == 'UploadImage': |
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uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) |
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if uploaded_image is not None: |
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with open("uploaded_image.png", "wb") as f: |
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f.write(uploaded_image.read()) |
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st.success("Image saved successfully!") |
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saved_image = st.image("uploaded_image.png", caption='Uploaded Image.', use_column_width=True) |
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predicted = model.predict( |
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"uploaded_image.png", |
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confidence=20, overlap=30).json() |
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st.write("Predicted Class:", predicted) |
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elif option == 'CameraInput': |
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st.write("Camera Input") |
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picture = st.camera_input("Take a picture") |
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if picture is not None: |
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img = Image.open(picture) |
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st.image(img, caption='Uploaded Image.', use_column_width=True) |
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img_array=np.array(img) |
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img_array.resize((640, 640)) |
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predicted = model.predict(img_array, |
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confidence=20, overlap=30).json() |
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st.write("Predicted Class:", predicted) |
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