Update app.py

app.py

@@ -1,87 +1,31 @@
-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
+import gradio as gr
+from fastai import *
+from fastai.vision.all import *
 
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            </style>
-            """
-            
-st.markdown(hide_streamlit_style, unsafe_allow_html = True)
+import pathlib
+plt = platform.system()
+if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
 
-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.")
+learn = load_learner('Pickle_SD_Model.pkl') 
 
-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))
+labels = learn.dls.vocab
 
-def predict_class(image) :
-    with st.spinner('Loading Model...'):
-        classifier_model = keras.models.load_model(r'model_2.h5', compile = False)
+set_label = gr.outputs.Textbox(label="Predicted Class")
 
-    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
+set_prob = gr.outputs.Label(num_top_classes=4, label="Predicted Probability Per Class")
 
-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()
-    
+
+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))}
+
+title = "Tomato Disease Classifier"
+description = "Classify Tomato Disease from leaf"
+interpretation='default'
+
+enable_queue=True
+
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3), 
+                    examples_per_page = 2
+                    ).launch(share=True)