Create run.py

run.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')
+material_list = []
+
+with open("results.txt", "r") as f:
+    results = f.readlines()
+    for line in results: 
+        material = line.split(" [")[0]
+        material_list.append(material.strip())  # Trim any leading/trailing whitespace
+    f.close()
+    
+text = tokenizer(material_list)
+
+def process_image(image_input):
+
+    results = {}
+    float_values = []
+    image = preprocess(image_input).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.]]
+
+    counter = 0
+    for row in text_probs:
+        for column in row:
+            float_values.append(float(column))
+            results[float(column)] = material_list[counter] 
+            counter += 1
+
+    sorted_float_values = sorted(float_values, reverse=True)
+    print(sorted_float_values)
+    return [["Material : " + str(results[sorted_float_values[0]]), "Confidence : " + str(sorted_float_values[0])], ["Material : " + str(results[sorted_float_values[1]]), "Confidence : " + str(sorted_float_values[1])]]
+
+inputs = gr.inputs.Image(type="pil")
+outputs = [gr.outputs.Textbox(label="Top Result"), gr.outputs.Textbox(label="Second Result")]
+
+interface = gr.Interface(fn=process_image, inputs=inputs, outputs=outputs)
+interface.launch(share=True)