Update app.py

app.py

@@ -1,39 +1,3 @@
 import gradio as gr
-import torch
-from torchvision import transforms as tt
-from PIL import Image
-from predict import predict_potato, predict_tomato  # Assuming you have functions to predict for potato and tomato
-from model import model_potato, model_tomato  # Assuming you have two separate models
 
-# Load models (assuming they are saved in the "models" directory)
-model_potato.load_state_dict(torch.load("models/potato_model_statedict__f.pth", map_location=torch.device('cpu')))
-model_tomato.load_state_dict(torch.load("models/tomato_model_statedict__f.pth", map_location=torch.device('cpu')))
-
-# Define a function to make predictions based on the selected model
-def classify_image(image, plant_type):
-    if plant_type == "Potato":
-        class_name, probability = predict_potato(image, model_potato)
-    else:
-        class_name, probability = predict_tomato(image, model_tomato)
-
-    return {
-        "Predicted Class": class_name,
-        "Probability (%)": f"{probability * 100:.2f}%"
-    }
-
-# Set up Gradio interface
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=[
-        gr.inputs.Image(type="file", label="Upload an Image"),
-        gr.inputs.Radio(["Potato", "Tomato"], label="Select Plant Type")
-    ],
-    outputs=[
-        gr.outputs.Label(num_top_classes=1, label="Prediction Results")
-    ],
-    title="Potato and Tomato Disease Classification",
-    description="Upload an image of a potato or tomato leaf to classify the disease."
-)
-
-# Launch Gradio interface
-iface.launch()
+gr.load("models/hassaanik/Plant_Disease_Classification").launch()