Spaces:
Running
Running
File size: 3,022 Bytes
4f7c2c3 aff9d06 e97dbab aff9d06 4f7c2c3 aff9d06 e97dbab 4f7c2c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
import gradio as gr
import torch
from torch import nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image
import os
import logging
import requests
from io import BytesIO
# Setup logging
logging.basicConfig(level=logging.INFO)
# Define the number of classes
num_classes = 3
# Download model from Hugging Face
def download_model():
model_path = hf_hub_download(repo_id="jays009/Resnet3", filename="model.pth")
return model_path
# Load the model from Hugging Face
def load_model(model_path):
model = models.resnet50(pretrained=False)
num_features = model.fc.in_features
model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(num_features, 3) # 3 classes
)
checkpoint = torch.load(model_path, map_location=torch.device("cpu"))
model.load_state_dict(checkpoint['model_state_dict'])
# Rename keys to match the model definition
state_dict['fc.weight'] = state_dict.pop('fc.1.weight')
state_dict['fc.bias'] = state_dict.pop('fc.1.bias')
# Load the modified state dict
model.load_state_dict(state_dict)
model.eval()
return model
# Path to your model
model_path = hf_hub_download(repo_id="jays009/Resnet3", filename="model.pth")
model = load_model(model_path)
# Download the model and load it
model_path = download_model()
model = load_model(model_path)
# Define the transformation for the input image
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
# Prediction function for an uploaded image
def predict_from_image_url(image_url):
try:
# Download the image from the provided URL
response = requests.get(image_url)
response.raise_for_status()
image = Image.open(BytesIO(response.content))
# Apply transformations
image_tensor = transform(image).unsqueeze(0)
# Perform prediction
with torch.no_grad():
outputs = model(image_tensor)
predicted_class = torch.argmax(outputs, dim=1).item()
# Interpret the result
if predicted_class == 0:
return {"result": "The photo is of Fall Army Worm with problem ID 126."}
elif predicted_class == 1:
return {"result": "The photo shows symptoms of Phosphorus Deficiency with Problem ID 142."}
elif predicted_class == 2:
return {"result": "The photo shows symptoms of Bacterial Leaf Blight with Problem ID 203."}
else:
return {"error": "Unexpected class prediction."}
except Exception as e:
return {"error": str(e)}
demo = gr.Interface(
fn=predict_from_image_url,
inputs="text",
outputs="json",
title="Maize Disease Classification",
description="Enter a URL to an image for classification (Fall Army Worm, Phosphorus Deficiency, or Bacterial Leaf Blight).",
)
if __name__ == "__main__":
demo.launch()
|