Update app.py

app.py

@@ -1,41 +1,87 @@
-from fastai.vision.all import *
-import gradio as gr
+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+import tensorflow_hub as hub
 
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+            
+st.markdown(hide_streamlit_style, unsafe_allow_html = True)
 
-# Cargamos el learner
-learn = load_learner('Pickle_SD_Model.pkl')
+st.title('SKIN DISEASE PREDICTION')
+st.write("An automated system is proposed for the diagnosis of #23 common skin diseases by using data from clinical images and patient information.")
 
-# Definimos las etiquetas de nuestro modelo
-labels = learn.dls.vocab
+def main() :
+    file_uploaded = st.file_uploader('Choose an image...', type = 'jpg')
+    if file_uploaded is not None :
+        image = Image.open(file_uploaded)
+        st.write("Uploaded Image.")
+        figure = plt.figure()
+        plt.imshow(image)
+        plt.axis('off')
+        st.pyplot(figure)
+        result, confidence = predict_class(image)
+        st.write('Prediction : {}'.format(result))
+        st.write('Confidence : {}%'.format(confidence))
 
+def predict_class(image) :
+    with st.spinner('Loading Model...'):
+        classifier_model = keras.models.load_model(r'model_2.h5', compile = False)
 
-# Definimos una función que se encarga de llevar a cabo las predicciones
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-    
-
-# launching the interface
-
-title = "SKIN DISEASE PREDICTION"
-description ="An automated system is proposed for the diagnosis of #23 common skin diseases by using data from clinical images and patient information."
+    shape = ((256,256,3))
+    model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)])     # ye bhi kaam kar raha he
+    test_image = image.resize((256, 256))
+    test_image = keras.preprocessing.image.img_to_array(test_image)
+    test_image /= 256.0
+    test_image = np.expand_dims(test_image, axis = 0)
+    class_name = ['Acne and Rosacea Photos',
+                  'Actinic Keratosis Basal Cell Carcinoma and other Malignant Lesions', 
+                  'Atopic Dermatitis Photos', 'Bullous Disease Photos', 'Cellulitis Impetigo and other Bacterial Infections', 
+                  'Eczema Photos', 'Exanthems and Drug Eruptions', 'Hair Loss Photos Alopecia and other Hair Diseases', 
+                  'Herpes HPV and other STDs Photos', 'Light Diseases and Disorders of Pigmentation', 'Lupus and other Connective Tissue diseases',
+                  'Melanoma Skin Cancer Nevi and Moles', 'Nail Fungus and other Nail Disease', 'Poison Ivy Photos and other Contact Dermatitis', 
+                  'Psoriasis pictures Lichen Planus and related diseases', 'Scabies Lyme Disease and other Infestations and Bites', 
+                  'Seborrheic Keratoses and other Benign Tumors', 'Systemic Disease', 'Tinea Ringworm Candidiasis and other Fungal Infections', 
+                  'Urticaria Hives', 'Vascular Tumors', 'Vasculitis Photos', 'Warts Molluscum and other Viral Infections']
+    prediction = model.predict_generator(test_image)
+    confidence = round(100 * (np.max(prediction[0])), 2)
+    final_pred = class_name[np.argmax(prediction)]
+    return final_pred, confidence
 
-examples = [
-                ['123.jpg'],
-                ['acne-closed-comedo-2.jpg'],
-                ['cherry-angioma-16.jpg'],
-                ['distal-subungual-onychomycosis-86.jpg'],
-                ['malignant-melanoma-16.jpg'],  
-                ['tinea-primary-lesion-15.jpg']               
-           ]
-
-# launching the interface
-gr.Interface( fn=predict,
-                  inputs = gr.inputs.Image(shape=(256, 256)),
-                  outputs = gr.outputs.Label(num_top_classes=3),
-                  capture_session = True,
-                  title=title,
-                  description= description,
-                  examples = examples
-                  ).launch(share=True)
+footer = """
+<style>
+a:link , a:visited{
+    color: white;
+    background-color: transparent;
+    text-decoration: None;
+}
+a:hover,  a:active {
+    color: red;
+    background-color: transparent;
+    text-decoration: None;
+}
+.footer {
+    position: fixed;
+    left: 0;
+    bottom: 0;
+    width: 100%;
+    background-color: transparent;
+    color: black;
+    text-align: center;
+}
+<div class="footer">
+<p align="center"> Developed with ❤ by Mato</p>
+</div>
+</style>
+"""
+st.markdown(footer, unsafe_allow_html = True) 
+if __name__ == "__main__": 
+    main()
+