Add application file

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import torch
+from PIL import Image
+import open_clip
+
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+labels = ["vehicle accident", "fire", "a cat"]
+text = tokenizer(labels)
+
+
+
+def image_classifier(inp):
+    print(type(inp))
+    # image = preprocess(Image.open("accident.jpg")).unsqueeze(0)
+    image = preprocess(Image.fromarray(inp)).unsqueeze(0)
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text)
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+        text_features /= text_features.norm(dim=-1, keepdim=True)
+
+        text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+        print("Label probs:", text_probs)  # prints: [[1., 0., 0.]]
+        text_probs = text_probs[0]
+        print(text_probs[0])
+        
+        maxProb = 0
+        ansIndex = ""
+        for idx, probs in enumerate(text_probs):
+            if probs > maxProb:
+                ansIndex = idx
+                maxProb = probs
+
+        obj = {}
+        for i in range(len(labels)):
+            currLabel = labels[i]
+            currProb = text_probs[i]
+            obj[currLabel]=currProb
+        print(obj)
+        return {labels[ansIndex] : 1}
+
+
+image_input = gr.inputs.Image(shape=(224, 224))
+output = gr.outputs.Label()
+demo = gr.Interface(fn=image_classifier, inputs=image_input, outputs=output)
+demo.launch(share=False)
+