Update app.py

app.py

@@ -1,22 +1,22 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-interpreter = tf.lite.Interpreter(model_path="leaf_model.tflite")
-interpreter.allocate_tensors()
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-
-def classify(image):
-    img = image.resize((224, 224)).convert("RGB")
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)
-
-    interpreter.set_tensor(input_details[0]['index'], img_array)
-    interpreter.invoke()
-    output = interpreter.get_tensor(output_details[0]['index'])[0][0]
-    return "Unhealthy" if output > 0.5 else "Healthy"
-
-iface = gr.Interface(fn=classify, inputs=gr.Image(type="pil"), outputs="text")
-iface.launch()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+interpreter = tf.lite.Interpreter(model_path="leaf_model_85_percent.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+def classify(image):
+    img = image.resize((224, 224)).convert("RGB")
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)
+
+    interpreter.set_tensor(input_details[0]['index'], img_array)
+    interpreter.invoke()
+    output = interpreter.get_tensor(output_details[0]['index'])[0][0]
+    return "Unhealthy" if output > 0.45 else "Healthy"
+
+iface = gr.Interface(fn=classify, inputs=gr.Image(type="pil"), outputs="text")
+iface.launch()