initial commit

index.py

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+import streamlit as st
+import keras.utils as image
+import numpy as np
+import io
+import tensorflow as tf
+import base64
+
+# Load TFLite model and allocate tensors
+interpreter = tf.lite.Interpreter(model_path="model/model_float16_quant.tflite")
+interpreter.allocate_tensors()
+
+st.title("Image Classifier")
+
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
+img_data = None
+
+if uploaded_file is not None:
+    # Use in-memory image
+    image_stream = io.BytesIO(uploaded_file.read())
+    img = image.load_img(image_stream, target_size=(224, 224))
+
+    # Convert PIL Image to data URL
+    img_buffer = io.BytesIO()
+    img.save(img_buffer, format="PNG")
+    img_data = base64.b64encode(img_buffer.getvalue()).decode()
+
+    # Preprocess the image
+    img_array = image.img_to_array(img)
+    x_mean = img_array.mean()
+    x_std = img_array.std()
+    img_array = (img_array - x_mean) / x_std
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Set input tensor for the TFLite interpreter
+    input_details = interpreter.get_input_details()
+    interpreter.set_tensor(input_details[0]['index'], img_array)
+
+    # Invoke the interpreter
+    interpreter.invoke()
+
+    # Get the prediction result
+    output_details = interpreter.get_output_details()
+    predictions = interpreter.get_tensor(output_details[0]['index'])
+    predicted_class = np.argmax(predictions, axis=1)
+
+    class_dict = {
+        0: 'Actinic keratoses',
+        1: 'Basal cell carcinoma',
+        2: 'Benign keratosis-like lesions',
+        3: 'Dermatofibroma_df',
+        4: 'Melanocytic nevi',
+        5: 'Vascular lesions',
+        6: 'Dermatofibroma_mel'
+    }
+
+    result = class_dict[predicted_class[0]]
+
+    st.write(f"Prediction: {result}")
+
+    # Display the uploaded image
+    st.image(img, caption='Uploaded Image', use_column_width=True)

model/model_float16_quant.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c112cc0308137a5ab6321c054e40c6e0282f74b3ec81ae4b157272145060cb3d
+size 51239848