streamlit app

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import torch
+import clip
+from PIL import Image
+import numpy as np
+
+# Load CLIP model and preprocessing
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+# Function to predict descriptions and probabilities
+def predict(image, descriptions):
+    image = preprocess(image).unsqueeze(0).to(device)
+    text = clip.tokenize(descriptions).to(device)
+
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text)
+
+        logits_per_image, logits_per_text = model(image, text)
+        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+    return descriptions[np.argmax(probs)], np.max(probs)
+
+# Streamlit app
+def main():
+    st.title("Image understanding model test")
+
+    # Instructions for the user
+    st.markdown("---")
+    st.markdown("### Upload an image to test how well the model understands it")
+
+    # Upload image through Streamlit with a unique key
+    uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"], key="uploaded_image")
+
+    if uploaded_image is not None:
+        # Convert the uploaded image to PIL Image
+        pil_image = Image.open(uploaded_image)
+
+        # Limit the height of the displayed image to 400px
+        st.image(pil_image, caption="Uploaded Image.", use_column_width=True, width=200)
+        
+        # Instructions for the user
+        st.markdown("### 2 Lies and 1 Truth")
+        st.markdown("Write 3 descriptions about the image, 1 must be true.")
+
+        # Get user input for descriptions
+        description1 = st.text_input("Description 1:", placeholder='A red apple')
+        description2 = st.text_input("Description 2:", placeholder='A car parked in a garage')
+        description3 = st.text_input("Description 3:", placeholder='An orange fruit on a tree')
+
+        descriptions = [description1, description2, description3]
+
+        # Button to trigger prediction
+        if st.button("Predict"):
+            if all(descriptions):
+                # Make predictions
+                best_description, best_prob = predict(pil_image, descriptions)
+
+                # Display the highest probability description and its probability
+                st.write(f"**Best Description:** {best_description}")
+                st.write(f"**Prediction Probability:** {best_prob:.2%}")
+
+                # Display progress bar for the highest probability
+                st.progress(float(best_prob))
+
+if __name__ == "__main__":
+    main()