Upload 2 files

app.py

@@ -0,0 +1,6 @@
+import streamlit as st
+
+st.set_page_config(page_title="Vision Bias App", layout="wide")
+st.title("The AI is **racist** too !")
+
+st.write("Use the sidebar to navigate through different Domians.")

pages/1_Clothing_Bias.py

@@ -0,0 +1,90 @@
+import streamlit as st
+from PIL import Image
+import requests
+from io import BytesIO
+import os
+import torch
+from transformers import CLIPProcessor, CLIPModel
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Set page config (optional)
+st.set_page_config(page_title="Clothing Bias in Scene Classification", layout="wide")
+
+# Load model once
+@st.cache_resource
+def load_clip_model():
+    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", use_fast=False)
+    return model, processor
+
+
+# Page title
+st.header("👖 Clothing Bias in Scene Classification 👗")
+st.markdown("""
+This application explores biases in scene classification models related to clothing attributes.
+It leverages the CLIP model to analyze and highlight these biases.
+""")
+
+# Image input
+input_method = st.selectbox("Select Input Method", ["Default Images", "Upload Image", "Use Image URL"], index=0)
+
+image = None
+if input_method == "Upload Image":
+    uploaded_file = st.file_uploader("Upload your own image", type=["jpg", "png", "jpeg"])
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert("RGB")
+elif input_method == "Use Image URL":
+    image_url = st.text_input("Paste an image URL")
+    if image_url:
+        try:
+            response = requests.get(image_url)
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+        except:
+            st.error("Couldn't load image from the provided URL.")
+elif input_method == "Default Images":
+    default_dir = "default_images/clothing_bias"
+    default_images = sorted([f for f in os.listdir(default_dir) if f.lower().endswith((".jpg", ".png", ".jpeg"))])
+    default_choice = st.selectbox("Choose from default images", default_images, index=0)
+    image = Image.open(os.path.join(default_dir, default_choice)).convert("RGB")
+
+# Display the image
+if image is not None:
+    st.image(image, caption="Input Image", width=250)
+
+# Prompt input
+st.subheader("📝 Candidate Scene Labels")
+default_prompts = ["a business executive", "a festival participant"]
+prompts_text = st.text_area("Enter one label per line:", "\n".join(default_prompts))
+labels = [label.strip() for label in prompts_text.strip().split("\n") if label.strip()]
+
+# Process and classify
+if st.button("🔍 Analyze Image"):
+    if image is None:
+        st.warning("⚠️ Please upload an image, paste a URL, or choose a default image before analysis.")
+    else:
+        model, processor = load_clip_model()
+        with st.spinner("Analyzing the image, please wait..."):
+            inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
+            with torch.no_grad():
+                outputs = model(**inputs)
+                probs = outputs.logits_per_image.softmax(dim=1)[0]
+
+        # Display classification
+        st.subheader("📊 Classification Probabilities")
+        df = pd.DataFrame({
+            "Label": labels,
+            "Probability": probs.numpy()
+        })
+        df.index += 1
+        st.table(df)
+        st.write("**Most likely label**:", labels[probs.argmax().item()])
+        st.write("\n")
+
+        # Show horizontal bar plot
+        fig, ax = plt.subplots(figsize=(6, 4))
+        ax.barh(labels, probs.numpy(), color='skyblue')
+        ax.set_xlim(0, 1)
+        ax.set_xlabel("Probability")
+        ax.set_title("Scene Classification")
+        st.pyplot(fig)