Create app.py

app.py

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+import numpy as np
+import clip
+import torch
+import gradio as gr
+from PIL import Image
+import os
+import base64
+from io import BytesIO
+
+# Load the CLIP model
+model, preprocess = clip.load("ViT-B/32")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device).eval()
+
+# Define the Business Listing variable
+Business_Listing = "Air Guide"
+
+def find_similarity(image_base64, text_input):
+    # Decode the base64 image string to bytes
+    image_bytes = base64.b64decode(image_base64)
+    image = Image.open(BytesIO(image_bytes))
+
+    # Preprocess the image
+    image = preprocess(image).unsqueeze(0).to(device)
+    
+    # Prepare input text
+    text_tokens = clip.tokenize([text_input]).to(device)
+
+    # Encode image and text features
+    with torch.no_grad():
+        image_features = model.encode_image(image).float()
+        text_features = model.encode_text(text_tokens).float()
+
+    # Normalize features and calculate similarity
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    similarity = (text_features @ image_features.T).cpu().numpy()
+
+    return similarity[0, 0]
+
+# Define a Gradio interface
+iface = gr.Interface(
+    fn=find_similarity,
+    inputs=["text", gr.inputs.Textbox(lines=3, label="Enter Base64 Image"), "text"],
+    outputs="number",
+    live=True,
+    interpretation="default",
+    title="CLIP Model Image-Text Cosine Similarity",
+    description="Enter a base64-encoded image and text to find their cosine similarity.",
+)
+
+iface.launch()