Update app.py

app.py

@@ -1,6 +1,5 @@
 import io
 import gradio as gr
-import matplotlib.pyplot as plt
 import requests, validators
 import torch
 import pathlib
@@ -12,8 +11,8 @@ import os
 
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("/content/drive/MyDrive/Week 1 Project/saved_model_files")
-model = AutoModelForImageClassification.from_pretrained("/content/drive/MyDrive/Week 1 Project/saved_model_files")
+feature_extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
 
 labels = ['angular_leaf_spot', 'bean_rust', 'healthy']
 
@@ -62,6 +61,8 @@ def set_example_url(example: list) -> dict:
 
 title = """<h1 id="title">Plant Health Classification with ViT</h1>"""
 
+gr.Image('images/Healthy.png',label = 'Healthy Plant'), gr.Image('images/sickie.png',label = 'Infected Plant')
+
 description = """
 This Plant Health classifier app was built to detect the health of plants using images of leaves by fine-tuning a Vision Transformer (ViT) [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [Beans](https://huggingface.co/datasets/beans) dataset.
 The finetuned model has an accuracy of 98.4% on the test (unseen) dataset and 100% on the validation dataset.