lighting.ai transfer

.gitignore

@@ -1 +1,2 @@
-.python-version
+.python-version
+.streamlit

app.py

@@ -1,5 +1,96 @@
 import streamlit as st
+import torch
+import clip
+from PIL import Image
+import numpy as np
+import hmac
+
+
+# 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))
+
+# user has correct Password?
+def check_password():
+    def password_entered():
+        if hmac.compare_digest(st.session_state["password"], st.secrets["password"]):
+            st.session_state["password_correct"] = True
+            del st.session_state["password"]  # Don't store the password.
+        else:
+            st.session_state["password_correct"] = False
+
+    # Return True if the password is validated.
+    if st.session_state.get("password_correct", False):
+        return True
+
+    # Show input for password.
+    st.text_input("Password", type="password", on_change=password_entered, key="password")
+    if "password_correct" in st.session_state:
+        st.error("😕 Password incorrect")
+    return False
+
+
+if __name__ == "__main__":
+    if not check_password():
+        st.stop()  # Do not continue if check_password is not True.
+
+    # Main Streamlit app starts here
+    main()
+
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-print(x)