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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+plant_disease_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# trained model load
+# Purana code: model = tf.keras.models.load_model("model.h5")
+# Naya code (Sahi naam ke saath):
+model = tf.keras.models.load_model("plant_disease_model.keras")
+
+
+# prediction function
+def predict_plant(img):
+
+    # resize same as training
+    img = img.resize((150,150))
+
+    img_array = np.array(img)/255.0
+
+    # batch dimension add
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)[0][0]
+
+    if prediction > 0.5:
+        return "Diseased Plant"
+    else:
+        return "Healthy Plant"
+
+
+# interface
+demo = gr.Interface(
+    fn=predict_plant,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Plant Disease Classifier"
+)
+
+demo.launch()

plant_disease_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:894352ae3fd774bd2fbd1db40492caea584c4cc91297b64dedbfb41def94cf35
+size 57989321

requirements.txt

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+tensorflow
+gradio
+numpy
+pillow
+tensorflow-datasets