Create app.py with Zero-Shot Image Classification

app.py

@@ -0,0 +1,75 @@
+from transformers import CLIPModel, CLIPProcessor
+from PIL import Image
+import time
+import gradio as gr
+
+
+openai_model_name = "openai/clip-vit-large-patch14"
+openai_model = CLIPModel.from_pretrained(openai_model_name)
+openai_processor = CLIPProcessor.from_pretrained(openai_model_name)
+
+patrickjohncyh_model_name = "patrickjohncyh/fashion-clip"
+patrickjohncyh_model = CLIPModel.from_pretrained(patrickjohncyh_model_name)
+patrickjohncyh_processor = CLIPProcessor.from_pretrained(patrickjohncyh_model_name)
+
+model_map = {
+    openai_model_name: (openai_model, openai_processor),
+    patrickjohncyh_model_name: (patrickjohncyh_model, patrickjohncyh_processor)
+}
+
+
+def gradio_process(model_name, image, text):
+    (model, processor) = model_map[model_name]
+    labels = text.split(", ")
+    print (labels)
+    start = time.time()
+    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    probs = outputs.logits_per_image.softmax(dim=1)[0]
+    end = time.time()
+    time_spent = end - start
+    probs = list(probs)
+    results = []
+    for i in range(len(labels)):
+      results.append(f"{labels[i]} - {probs[i].item():.4f}")
+    result = "\n".join(results)
+
+    return [result, time_spent]
+
+
+with gr.Blocks() as zero_shot_image_classification_tab:
+  gr.Markdown("# Zero-Shot Image Classification")
+
+  with gr.Row():
+      with gr.Column():
+          # Input components
+          input_image = gr.Image(label="Upload Image", type="pil")
+          input_text = gr.Textbox(label="Labels (comma separated)")
+          model_selector = gr.Dropdown([openai_model_name, patrickjohncyh_model_name],
+                                        label = "Select Model")
+
+           # Process button
+          process_btn = gr.Button("Classificate")
+
+      with gr.Column():
+          # Output components
+          elapsed_result = gr.Textbox(label="Seconds elapsed", lines=1)
+          output_text = gr.Textbox(label="Classification")
+
+  # Connect the input components to the processing function
+  process_btn.click(
+      fn=gradio_process,
+      inputs=[
+          model_selector,
+          input_image,
+          input_text
+      ],
+      outputs=[output_text, elapsed_result]
+  )
+
+
+with gr.Blocks() as app:
+  gr.TabbedInterface([zero_shot_image_classification_tab], ["Zero-Shot Classification"])
+
+
+app.launch()