y

app.py

@@ -4,10 +4,15 @@ from PIL import Image
 import numpy as np
 import tensorflow as tf
 import gradio as gr
+from huggingface_hub import hf_hub_download
 
-# Paths
-working_dir = os.path.dirname(os.path.abspath(__file__))
-model_path = f"{working_dir}/trained_model/plant_disease_model.tflite"
+# -----------------------------
+# Download TFLite model from Hugging Face model repo
+# -----------------------------
+model_path = hf_hub_download(
+    repo_id="sidd-harth011/checkingPDRMod",  # your model repo
+    filename="plant_disease_model.tflite"
+)
 
 # Load TFLite model
 interpreter = tf.lite.Interpreter(model_path=model_path)
@@ -17,10 +22,16 @@ interpreter.allocate_tensors()
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 
-# Load class indices
-class_indices = json.load(open(f"{working_dir}/class_indices.json"))
+# Download/load class indices
+class_indices_path = hf_hub_download(
+    repo_id="sidd-harth011/checkingPDRMod",
+    filename="class_indices.json"
+)
+class_indices = json.load(open(class_indices_path))
 
-# Function to preprocess the image
+# -----------------------------
+# Preprocessing function
+# -----------------------------
 def load_and_preprocess_image(image, target_size=(224, 224)):
     img = image.resize(target_size)
     img_array = np.array(img, dtype=np.float32)
@@ -28,29 +39,28 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
     img_array = img_array / 255.0
     return img_array
 
+# -----------------------------
 # Prediction function
+# -----------------------------
 def predict_image_class(image):
     preprocessed_img = load_and_preprocess_image(image)
-    
-    # Set input tensor
     interpreter.set_tensor(input_details[0]['index'], preprocessed_img)
     interpreter.invoke()
-    
-    # Get predictions
     predictions = interpreter.get_tensor(output_details[0]['index'])
     predicted_class_index = np.argmax(predictions, axis=1)[0]
     predicted_class_name = class_indices[str(predicted_class_index)]
-    
     return f"Prediction: {predicted_class_name}"
 
+# -----------------------------
 # Gradio Interface
+# -----------------------------
 interface = gr.Interface(
     fn=predict_image_class,
     inputs=gr.Image(type="pil", label="Upload an Image"),
     outputs=gr.Textbox(label="Prediction"),
     title="🌱 Plant Disease Classifier (TFLite)",
-    description="Upload a plant leaf image to classify its disease using a compressed TFLite model."
+    description="Upload a plant leaf image to classify its disease using a compressed TFLite model hosted on Hugging Face."
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()