inititial checkin

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import CLIPProcessor, CLIPModel
+
+# Load model and processor
+model = CLIPModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+
+def calculate_similarity(image, text_prompt):
+    # Process inputs
+    inputs = processor(text=text_prompt, images=image, return_tensors="pt", padding=True)
+
+    # Forward pass
+    outputs = model(**inputs)
+
+    # Normalize and calculate cosine similarity
+    image_features = outputs.image_embeds / outputs.image_embeds.norm(dim=-1, keepdim=True)
+    text_features = outputs.text_embeds / outputs.text_embeds.norm(dim=-1, keepdim=True)
+    cosine_similarity = torch.nn.functional.cosine_similarity(image_features, text_features)
+
+    return {"Cosine Similarity": cosine_similarity.item()}
+
+# Set up Gradio interface
+iface = gr.Interface(fn=calculate_similarity,
+                     inputs=[gr.inputs.Image(type="pil"), gr.inputs.Textbox(label="Text Prompt")],
+                     outputs=[gr.outputs.Label(label="Cosine Similarity")],
+                     title="OpenClip Cosine Similarity Calculator",
+                     description="Upload an image and provide a text prompt to calculate the cosine similarity.")
+
+# Launch the interface locally for testing
+iface.launch()

requirements.txt

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+gradio
+torch
+transformers
+pillow