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README.md

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 ---
+title: AI Web App With OpenAI CLIP Model
+emoji: 📉
+colorFrom: green
+colorTo: red
+sdk: streamlit
+sdk_version: 1.33.0
+app_file: app.py
+pinned: false
 license: mit
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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))
+            else:
+                st.warning("Please provide all three descriptions.")
+
+if __name__ == "__main__":
+    main()

example_speed.txt

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+0.453

generate.py

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+import torch
+import time
+import clip
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-B/32", device=device)
+
+image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
+text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
+
+start_time = time.time()
+
+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()
+
+end_time = time.time()
+
+print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]
+
+print(f"Prediction time: {end_time - start_time} seconds")