examples n app

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+
+clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+def inference(input_img, captions):
+    captions_list = captions.split(",")
+    inputs = processor(text=captions_list, images=input_img, return_tensors="pt", padding=True)
+    outputs = clip(**inputs)
+    # this is the image-text similarity score
+    logits_per_image = outputs.logits_per_image
+    probs = logits_per_image.softmax(dim=1)
+    probabilities_percentages = ', '.join(['{:.2f}%'.format(prob.item() * 100) for prob in probs[0]])
+    return probabilities_percentages
+
+title = "CLIP Inference: Application using a pretrained CLIP model"
+description = "An application using Gradio interface that accepts an image and some captions, and displays a probability score with which each caption describes the image "
+
+examples = [
+    ["examples/woman_standing.jpg","woman standing inside a house, a photo of dog, running water, cupboard, home interiors"],
+    ["examples/city.jpg","long shot of a city, sunsetting on a urban place, river with animals"],
+    ["examples/dinning_tables.jpg","a bunch of dinning tables, cricket ground with players, movie theater, plants with music"],
+    ["examples/giraffe.jpg","tall giraffe standing and turning back, luxurious car on a road, a bunch of people standing"],
+    ["examples/dogs.jpg","a couple of dogs standing, woman standing inside a house, a photo of MJ"]
+]
+
+demo = gr.Interface(
+    inference,
+    inputs = [
+        gr.Image(shape=(416, 416), label="Input Image"),
+        gr.Textbox(placeholder="List of captions")],
+    outputs = [gr.Textbox(label="Probability score on captions likely describing the image")],
+    title = title,
+    description = description,
+    examples = examples,
+)
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+torch
+torchvision
+pillow
+numpy
+gradio
+pytorch-lightning
+transformers