Create app.py

app.py

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+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+
+# Load the CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+def clip_inference(input_img, input_text):
+    # Split input_text into a list of text entries
+    text_entries = [text.strip() for text in input_text.split(",")]
+
+    # Prepare inputs for CLIP model
+    inputs = processor(text=text_entries, images=input_img, return_tensors="pt", padding=True)
+
+    # Get similarity scores
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image
+    probs = logits_per_image.softmax(dim=1)
+
+    # Format the output probabilities as a comma-separated string
+    output_prob = ', '.join([str(prob.item()) for prob in probs[0]])
+
+    return output_prob
+
+title = "CLIP OpenAI Model"
+description = "Find similarity between images and multiple text entries (separated by commas)."
+text_examples = ["a sky with full of stars, painting image",
+                 "a dog playing in the garden, a dog sleeping in the garden",
+                 "a small girl dancing, a small girl playing guitar",
+                 "a small family cooking in the kitchen,family watching the movie",
+                 "students inside the class,students playing in the ground ",
+                 "a traffic signal, a lot of cars",
+                 "a theatre, a football stadium",
+                 "group of animals, group of birds",
+                 "yellow sunflowers, red roses",
+                 "sunset across the lake, sky with full of stars"]
+
+examples = [["examples/images_" + str(i) + ".jpg", text] for i, text in enumerate(text_examples)]
+
+demo = gr.Interface(
+    clip_inference,
+    inputs=[
+        gr.Image(label="Input image"),
+        gr.Textbox(placeholder="Input text : Multiple entries separated by commas"),
+    ],
+    outputs=[gr.Textbox(label="similarity scores")],
+    title=title,
+    description=description,
+    examples=examples
+)
+
+demo.launch()
+