Delete visionBias

visionBias/app.py

@@ -1,6 +0,0 @@
-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.")

visionBias/pages/1_Clothing_Bias.py

@@ -1,90 +0,0 @@
-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)