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import gradio as gr
import torch
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
# Load model and feature extractor
model = ViTForImageClassification.from_pretrained('shahmi0519/fypvit', num_labels=30, ignore_mismatched_sizes=True)
feature_extractor = ViTFeatureExtractor.from_pretrained('shahmi0519/fypvit')
# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
# Class labels (modify according to your model)
class_labels = [
"Bellpepper_fresh",
"Bellpepper_intermediate_fresh",
"Bellpepper_rotten",
"Carrot_fresh",
"Carrot_intermediate_fresh",
"Carrot_rotten",
"Cucumber_fresh",
"Cucumber_intermediate_fresh",
"Cucumber_rotten",
"Potato_fresh",
"Potato_intermediate_fresh",
"Potato_rotten",
"Tomato_fresh",
"Tomato_intermediate_fresh",
"Tomato_rotten",
"ripe_apple",
"ripe_banana",
"ripe_mango",
"ripe_oranges",
"ripe_strawberry",
"rotten_apple",
"rotten_banana",
"rotten_mango",
"rotten_oranges",
"rotten_strawberry",
"unripe_apple",
"unripe_banana",
"unripe_mango",
"unripe_oranges",
"unripe_strawberry"
]
def predict_freshness(image):
# Preprocess image
inputs = feature_extractor(images=image, return_tensors="pt").to(device)
# Predict
model.eval()
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
# Get label
try:
label = class_labels[predicted_class_idx]
except IndexError:
label = f"Class {predicted_class_idx}"
return label
# Create Gradio interface
title = "Freshness Detector"
description = "Upload an image of fruit/vegetable to detect its freshness state"
examples = [
["apple.jpeg"],
["banana.jpeg"],
["tomato.jpeg"]
]
iface = gr.Interface(
fn=predict_freshness,
inputs=gr.Image(type="pil", label="Upload Image"),
outputs=gr.Label(label="Freshness State"),
title=title,
description=description,
examples=examples
)
iface.launch(share=True) |