remove ui

ui.py

@@ -1,75 +0,0 @@
-
-import gradio as gr
-import numpy as np
-from model import BaselineCNN
-from data_pipeline import val_test_transform, IMAGE_SIZE
-import torch 
-
-from datasets import load_dataset
-dataset = load_dataset("DScomp380/plant_village", split="train")
-CLASS_NAMES = dataset.features["label"].names
-
-
-#load the model
-CLASSES = 39
-model = None 
-
-
-def predict(input_image):
-    global model
-    if model is None:
-        model = BaselineCNN(num_classes=CLASSES)
-        model.load_state_dict(torch.load("best_model.pth", map_location=torch.device('cpu')))
-        model.eval()
-    #resize to models image size, convert to tensor, normalize values
-    image_tensor = val_test_transform(input_image)
-
-    #add new dimension at index 0 so each image has a batch size of atleast 1
-    image_tensor = image_tensor.unsqueeze(0)
-
-    #run inference 
-    with torch.no_grad():
-        #pass the batch through the model
-        output = model(image_tensor)
-    
-    #convert to probabilitiees
-    probabilities = torch.nn.functional.softmax(output,dim=1)[0]
-
-    numPredictionsToShow = 10
-
-    #get the top 5 predictions
-    topProbs, TopClassIndicies = torch.topk(probabilities, numPredictionsToShow)
-    #returns 5 largest probabilities
-
-    #create the output dictionary
-    result = {}
-    for rank in range(numPredictionsToShow):#loop through top 5
-        classIndex = TopClassIndicies[rank].item()#get the int value from the tensor at index rank
-        className = CLASS_NAMES[classIndex]#get human readable class name
-        probabilityValue = topProbs[rank].item()#convert prob from tensor to python float
-
-        result[className] = probabilityValue
-        
-    return result
-
-
-with gr.Blocks(title="Plant Disease Classifier") as app:
-    gr.Markdown("# Plant Disease Classification")
-    gr.Markdown("Upload an image of a plant leaf to classify its disease.")
-
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Leaf Image")
-        label_output = gr.Label(label="Predicted Disease")
-    
-    gr.Examples(
-        examples =[], inputs=image_input)
-    
-    submit_btn = gr.Button("Submit")
-    submit_btn.click(fn=predict, inputs=image_input, outputs=label_output)
-
-    #fn=predict, 
-    # inputs=gr.Image(type="pil"),
-    # outputs=gr.Label(num_top_classes=3))
-
-
-