Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+import torch
+
+# Load Jina CLIP model
+model_name = "jinaai/jina-clip-v1"
+model = CLIPModel.from_pretrained(model_name)
+processor = CLIPProcessor.from_pretrained(model_name)
+
+def compute_similarity(input1, input2, type1, type2):
+    inputs = []
+    
+    # Process input1
+    if type1 == "Image":
+        image1 = Image.open(input1).convert("RGB")
+        inputs.append(processor(images=image1, return_tensors="pt"))
+    else:
+        inputs.append(processor(text=[input1], return_tensors="pt"))
+    
+    # Process input2
+    if type2 == "Image":
+        image2 = Image.open(input2).convert("RGB")
+        inputs.append(processor(images=image2, return_tensors="pt"))
+    else:
+        inputs.append(processor(text=[input2], return_tensors="pt"))
+    
+    # Compute embeddings
+    with torch.no_grad():
+        if type1 == "Image":
+            embedding1 = model.get_image_features(**inputs[0])
+        else:
+            embedding1 = model.get_text_features(**inputs[0])
+        
+        if type2 == "Image":
+            embedding2 = model.get_image_features(**inputs[1])
+        else:
+            embedding2 = model.get_text_features(**inputs[1])
+    
+    # Compute similarity
+    similarity = torch.nn.functional.cosine_similarity(embedding1, embedding2)
+    return similarity.item()
+
+with gr.Blocks() as demo:
+    gr.Markdown("# CLIP-based Similarity Comparison")
+    
+    with gr.Row():
+        type1 = gr.Radio(["Image", "Text"], label="Input 1 Type", value="Image")
+        type2 = gr.Radio(["Image", "Text"], label="Input 2 Type", value="Text")
+    
+    with gr.Row():
+        input1 = gr.File(label="Upload Image 1 or Enter Text")
+        input2 = gr.File(label="Upload Image 2 or Enter Text")
+    
+    compare_btn = gr.Button("Compare")
+    output = gr.Textbox(label="Similarity Score")
+    
+    compare_btn.click(compute_similarity, inputs=[input1, input2, type1, type2], outputs=output)
+
+demo.launch()