Prepared space

app.py

@@ -1,22 +1,52 @@
 import gradio as gr
-import torch
-import requests
-from torchvision import transforms
-
-model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def predict(inp):
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
-  return confidences
-
-demo = gr.Interface(fn=predict, 
-             inputs=gr.inputs.Image(type="pil"),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             )
-             
-demo.launch()
+import yolov5
+import os
+from transformers import pipeline
+
+imageClassifier = pipeline(task="image-classification",
+                           model="Ara88/timri-model")
+
+model = yolov5.load('./gentle-meadow.pt', device="cpu")
+
+def predict(image):
+    # results = model([image], size=224)
+    predictions = imageClassifier(image)
+    print(predictions)
+
+    return predictions
+
+
+title = "Detecting Tumors in MRI Images"
+description = """
+  Try the examples at bottom to get started.
+"""
+examples = [
+    [os.path.join(os.path.abspath(''), './examples/sample_1.png')],
+    [os.path.join(os.path.abspath(''), './examples/sample_2.png')],
+    [os.path.join(os.path.abspath(''), './examples/sample_3.jpg')],
+    [os.path.join(os.path.abspath(''), './examples/sample_4.jpg')],
+    [os.path.join(os.path.abspath(''), './examples/sample_5.jpg')],
+    [os.path.join(os.path.abspath(''), './examples/sample_6.jpg')],
+    [os.path.join(os.path.abspath(''), './examples/sample_7.jpg')],
+    [os.path.join(os.path.abspath(''), './examples/sample_8.jpg')],
+]
+
+inputs = gr.Image(type="pil", shape=(224, 224),
+                  label="Upload your image for detection")
+
+outputs = [
+    # gr.Image(type="pil", label="Tumor Detections"),
+    gr.Label(label="Tumor Classification")
+]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    description=description,
+    cache_examples=True,
+    theme='huggingface'
+)
+interface.launch(debug=True, enable_queue=True)

requirements.txt

@@ -1,4 +1,5 @@
-gradio
 torch
-requests
-torchvision
+yolov5
+transformers
+opencv-python
+gradio