Initial release

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import keras
+import tensorflow as tf
+
+sep_model = keras.models.load_model("goodmodel.h5")
+
+def process(input_img):
+    list_form = np.array([input_img])
+    print(list_form.shape)
+    results = sep_model.predict([list_form])
+    results = list(results[0])
+    print(results)
+    classification = ""
+    if results[0] > results[1] and results[0] > results[2]:
+        classification = "Benign"
+    elif results[1] > results[0] and results[1] > results[2]:
+        classification = "Malignant"
+    else:
+        classification = "Non-neoplastic"
+    print(classification)
+    return classification
+        
+demo = gr.Interface(process, gr.Image(shape=(240, 240)), "text", 
+    title="Optimized Image Classification Models on Dark Skin Lesions",
+    description="Images that are not of skin lesions will throw off the model, as it will try to classify all images within the three categories.",
+    examples=[
+        ["examples/b1.jpeg"],
+            ["examples/b2.jpeg"],
+            ["examples/m1.jpeg"],
+            ["examples/m2.jpeg"],
+            ["examples/nn1.jpeg"],
+            ["examples/nn2.jpeg"]
+        
+    ],
+    interpretation="default"
+)
+demo.launch(share=True)

goodmodel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c49516718055efe0ebff3e088e3da58835853e67feb91d6588f047b2dc07a10
+size 79730528

requirements.txt

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+numpy==1.21.5
+pandas==1.4.4
+keras==2.12.0
+tensorflow==2.12.0