Create app.py

app.py

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+import requests
+import numpy as np 
+import gradio as gr
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+
+def inference(input_img=None, input_text=None):
+    
+    if input_img is not None and input_text is not None:
+
+      inputs = processor(text=input_text.split(","), images=input_img, return_tensors="pt", padding=True)
+      outputs = model(**inputs)
+      logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+      probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+
+      output_prob = ', '.join([str(probs.detach().numpy()[0][i]) for i in range(np.shape(probs.detach().numpy()[0])[0])])
+
+    else: 
+        output_prob = None 
+         
+    return output_prob
+
+
+title = "CLIP OpenAI model"
+description = "A simple Gradio interface to find similarity between images and text"
+text_examples = ["A man and a dog, A man wearing a blue coat with a dog inside", 
+                 "Train tracks and a train, A dog playing in the field", 
+                 "An outdoor seating glass box, A movie theater",
+                 "A building", "A building and multiple cars on the road", 
+                 "A living area", 
+                 "A dining room, A football stadium", 
+                 "A red car", 
+                 "A chair and a book, A book and a chair", 
+                 "A man and a horse", 
+                 "A man and a horse"
+                 ]
+examples = [['examples/test_'+str(i)+'.jpg', ] for i in range(10)]
+
+demo = gr.Interface(inference,
+                    inputs = [gr.Image(label="Input image"), 
+                              gr.Textbox(placeholder="Input text (Multiple entries separated by commas)")], 
+                    outputs = [gr.Textbox(label="Similarity score between the input image and input text")],
+                    title = title,
+                    description = description,
+                    examples = examples
+                    )
+demo.launch()