Update app.py

app.py

@@ -1,10 +1,47 @@
 import gradio as gr
-from transformers import pipeline
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModel
 
-# Load your trained model (replace with your model ID)
-generator = pipeline("text-generation", model="DINGOLANI/luxury-autocomplete")
+# Load Fashion-CLIP model and processor
+model_id = "patrickjohncyh/fashion-clip"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModel.from_pretrained(model_id)
 
-def autocomplete(query):
-    return generator(query, max_length=50)[0]["generated_text"]
+def compute_embeddings(input_data, input_type="image"):
+    if input_type == "image":
+        image = Image.open(input_data)
+        inputs = processor(images=image, return_tensors="pt")
+        outputs = model.get_image_features(**inputs)
+    else:  # text
+        inputs = processor(text=input_data, return_tensors="pt")
+        outputs = model.get_text_features(**inputs)
+    return outputs.detach().numpy()
 
-gr.Interface(fn=autocomplete, inputs="textbox", outputs="textbox").launch()
+def image_text_search(query, image):
+    # Compute embeddings
+    text_emb = compute_embeddings(query, input_type="text")
+    image_emb = compute_embeddings(image, input_type="image")
+    
+    # Compute similarity (example: cosine similarity)
+    similarity = torch.nn.functional.cosine_similarity(
+        torch.tensor(text_emb), torch.tensor(image_emb), dim=1
+    )
+    return f"Similarity score: {similarity.item():.3f}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Fashion-CLIP Demo 🛍️")
+    with gr.Row():
+        text_input = gr.Textbox(label="Search Query", placeholder="e.g., 'red dress'")
+        image_input = gr.Image(label="Upload Fashion Item", type="filepath")
+    submit_btn = gr.Button("Search")
+    output = gr.Textbox(label="Similarity Score")
+    
+    submit_btn.click(
+        fn=image_text_search,
+        inputs=[text_input, image_input],
+        outputs=output
+    )
+
+demo.launch()