Update app.py

app.py

@@ -1,21 +1,27 @@
-import tensorflow as tf
-import requests
+import tf_keras as keras
 import gradio as gr
+import cv2
+import os
 
-inception_net = tf.keras.applications.MobileNetV2()
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
 
-def classify_image(inp):
-  inp = inp.reshape((-1, 224, 224, 3))
-  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
-  prediction = inception_net.predict(inp).flatten()
-  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+print(os.listdir('./model'))
+used_model = keras.models.load_model('./model')
+new_classes = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+
+def classify_image(img_dt):
+  img_dt = cv2.resize(img_dt,(256,256))
+  img_dt = img_dt.reshape((-1,256,256,3)) 
+  prediction = used_model.predict(img_dt).flatten()
+  confidences = {new_classes[i]: float(prediction[i]) for i in range (4) }
   return confidences
 
-demo = gr.Interface(fn=classify_image,
-             inputs=gr.Image(type="pil"),
-             outputs=gr.Label(num_top_classes=3),
-            )
+
+with gr.Blocks() as demo:
+    signal = gr.Markdown(''' Welcome to Maize Classifier,This model can identify if a leaf is 
+        **HEALTHY**, has **'Potato___Early_blight'** or **Potato_late___blight**''')
+    with gr.Row():
+        inp = gr.Image()
+        out = gr.Label()
+        inp.upload(fn= classify_image, inputs = inp, outputs = out)
 
 demo.launch()