resnet app

.gitignore

@@ -0,0 +1 @@
+temp.txt

app.py

@@ -1,7 +1,54 @@
 import gradio as gr
+import torch
+from torchvision import models, transforms
+from PIL import Image
 
-def greet(name):
-    return "Hello there, " + name + "!!"
+# Load the pre-trained ResNet model
+model = models.resnet50(pretrained=True)
+model.eval()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# Define the transformation for input images
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Define the labels for ImageNet classes (you may need to adjust this based on your model)
+LABELS_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
+labels = gr.utils.get_file(LABELS_URL, cache=True).read_text().splitlines()
+
+# Function to perform image classification
+def classify_image(input_image):
+    # Preprocess the image
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0)
+
+    # Make predictions
+    with torch.no_grad():
+        output = model(input_batch)
+
+    # Get the predicted class index
+    _, predicted_idx = torch.max(output, 1)
+
+    # Get the predicted label
+    predicted_label = labels[predicted_idx.item()]
+
+    return predicted_label
+
+# Gradio UI components
+image_input = gr.Image(preprocessing_fn=lambda img: Image.open(img.name))
+output_label = gr.Textbox()
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=image_input,
+    outputs=output_label,
+    live=True,
+    capture_session=True
+)
+
+# Launch the Gradio app
 iface.launch()

requirements.txt

@@ -1 +1,4 @@
-gradio
+gradio
+pillow
+torch
+torchvision