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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)
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