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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 requests
import base64
from io import BytesIO
# Define the number of classes
num_classes = 2 # Update with the actual number of classes in your dataset
# Download model from Hugging Face
def download_model():
try:
model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
return model_path
except Exception as e:
print(f"Error downloading model: {e}")
return None
# Load the model from Hugging Face
def load_model(model_path):
try:
model = models.resnet50(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, num_classes)
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()
return model
except Exception as e:
print(f"Error loading model: {e}")
return None
# Download the model and load it
model_path = download_model()
model = load_model(model_path) if model_path else None
# 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]),
])
def predict(image):
# Check if the input contains a base64-encoded string
if isinstance(image, dict) and image.get("data"):
try:
image_data = base64.b64decode(image["data"])
image = Image.open(BytesIO(image_data))
except Exception as e:
return f"Error decoding base64 image: {e}"
elif isinstance(image, str):
try:
response = requests.get(image)
image = Image.open(BytesIO(response.content))
except Exception as e:
return f"Error fetching image from URL: {e}"
# Apply transformations
try:
image = transform(image).unsqueeze(0)
image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
with torch.no_grad():
outputs = model(image)
predicted_class = torch.argmax(outputs, dim=1).item()
if predicted_class == 0:
return "The photo you've sent is of fall army worm with problem ID 126."
elif predicted_class == 1:
return "The photo you've sent is of a healthy maize image."
else:
return "Unexpected class prediction."
except Exception as e:
return f"Error processing image: {e}"
# Create the Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Textbox(),
live=True,
title="Maize Anomaly Detection",
description="Upload an image of maize to detect anomalies like disease or pest infestation. You can provide local paths, URLs, or base64-encoded images."
)
# Launch the Gradio interface
iface.launch(share=True)
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