initial commit

app.py

@@ -1,30 +1,20 @@
+from fastai.vision.all import *
 import gradio as gr
-import tensorflow as tf 
-import numpy as np
-import requests
-
-model = tf.keras.models.load_model('model_2.h5')
-
-#function
-def example(image):
-    image = image.reshape(-1, 256, 256, 3)
-    prediction = model.predict(image).flatten()
-#return {class_names[i]: float(prediction[i]) for i in range(4)}
-    class_names = ['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']
-    return {class_names[i]: float(prediction[i]) for i in range(23)}
- 
- 
- # initializing the input component
-image = gr.inputs.Image(shape = (256, 256)) 
-# initializing the output component 
-label = gr.outputs.Label(num_top_classes = 4)
+
+
+# Cargamos el learner
+learn = load_learner('Pickle_SD_Model.pkl')
+
+# Definimos las etiquetas de nuestro modelo
+labels = learn.dls.vocab
+
+
+# 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
 
@@ -41,8 +31,11 @@ examples = [
            ]
 
 # launching the interface
-gr.Interface( fn=example,inputs = image,outputs = label,capture_session = True,
+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,inbrowser=True)
+                  ).launch(share=True)