Create app.py

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+from transformers import CLIPModel, CLIPProcessor
+import torch
+from PIL import Image
+
+#################################
+#### FUNCTIONS
+
+def load_clip(model_size='large'):
+    if model_size == 'base':
+        MODEL_name = 'openai/clip-vit-base-patch32'
+    elif model_size == 'large':
+        MODEL_name = 'openai/clip-vit-large-patch14'
+    
+    model = CLIPModel.from_pretrained(MODEL_name)
+    processor = CLIPProcessor.from_pretrained(MODEL_name)
+
+    return processor, model
+
+def inference_clip(options, image):
+    
+    inputs = processor(text= options, images=image, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+        #logits_per_text = outputs.logits_per_text
+        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
+
+    max_prob_idx = torch.argmax(probs)
+    max_prob_option = options[max_prob_idx]
+    max_prob = probs[max_prob_idx].item()
+    return max_prob_option 
+
+#################################
+#### LAYOUT
+
+CLIP_large = load_clip(model_size='large')
+
+picture_file = st.file_uploader("Picture :", type=["jpg", "jpeg", "png"])
+if picture_file is not None:
+    image = Image.open(picture_file)
+    st.image(image, caption='Please upload an image of the damage', use_column_width=True)
+
+#image
+options = st.text_input(label="Please enter the classes", value="")
+options = list(options)
+
+# button to launch compute
+if st.button("Compute"):
+    clip_processor, clip_model = load_clip(model_size='large')
+    result = inference_clip(options = options, image = image)
+    st.write(result)