iab classifier app

.gitignore

@@ -0,0 +1,166 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+
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+models/
+data/

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+transformers
+torch
+numpy
+scikit-learn
+model2vec
+mohtml
+streamlit
+matplotlib
+transformers-interpret
+datasets
+huggingface_hub

src/streamlit_app.py

@@ -1,40 +1,188 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+from datasets import load_dataset
+import requests
+import gzip
+import json
 import streamlit as st
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+from model2vec import StaticModel
+import matplotlib.pyplot as plt
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from torch.nn.functional import sigmoid
+from transformers_interpret import SequenceClassificationExplainer
+
+
+# -- SETTINGS --
+LABELS = [
+    'inconclusive',
+    'animals',
+    'arts',
+    'autos',
+    'business',
+    'career',
+    'education',
+    'fashion',
+    'finance',
+    'food',
+    'government',
+    'health',
+    'hobbies',
+    'home',
+    'news',
+    'realestate',
+    'society',
+    'sports',
+    'tech',
+    'travel'
+]
+
+label2id = {label: idx for idx, label in enumerate(LABELS)}
+id2label = {idx: label for label, idx in label2id.items()}
+
+REPO_ID = "chidamnat2002/iab_training_dataset"
+
+@st.cache_data
+def load_csv_data():
+    dataset = load_dataset(REPO_ID, split="train", data_files="train_df_simple.csv")
+    df = pd.DataFrame(dataset)
+    return df
+
+@st.cache_resource
+def get_model_and_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("chidamnat2002/content-multilabel-iab-classifier")
+    model = AutoModelForSequenceClassification.from_pretrained("chidamnat2002/content-multilabel-iab-classifier")
+    return model, tokenizer
+
+@st.cache_resource
+def get_explainer():
+    model, tokenizer = get_model_and_tokenizer()
+    return SequenceClassificationExplainer(model, tokenizer)
+
+# -- LOAD MODEL & EMBEDDINGS --
+@st.cache_resource
+def load_model():
+    return StaticModel.from_pretrained("minishlab/potion-retrieval-32M")
+
+# st.markdown("### ✨ Encode all examples")
+@st.cache_resource
+def encode_texts_cached(corpus):
+    model = load_model()  # use cached model
+    return model.encode(corpus)
+
+@st.cache_data(show_spinner="Embedding reference", max_entries=50)
+def encode_reference(text: str):
+    model = load_model()
+    return model.encode([text])[0]
+
+@st.cache_resource
+def get_data_and_embeddings():
+    df = load_csv_data()
+    texts = df["text"].to_list()
+    prior_labels = df['label'].to_list()
+    X = encode_texts_cached(texts)
+    return texts, prior_labels, X
+
+
+st.set_page_config(page_title="IAB Classifier App", layout="wide")
+st.title("🧠 IAB Classifier App")
+
+# Load data
+texts, prior_labels, X = get_data_and_embeddings()
+st.markdown("### 🧭 Reference sentence for similarity")
+reference = st.text_area("Type something like 'business related'")
+prediction_choice = st.checkbox("try our iab model prediction for this")
+
+
+def predict_content_multilabel(text, threshold=0.5, verbose=False):
+    model, tokenizer = get_model_and_tokenizer()
+    model.eval()
+    text = text.replace("-", " ")
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)
+        logits = model(**inputs).logits
+        probs = sigmoid(logits).squeeze().cpu().numpy()
+
+        predicted_labels = [(id2label[i], round(float(p), 3)) for i, p in enumerate(probs) if p >= threshold]
+        probs_res = [prob for prob in probs if prob >= threshold]
+
+        if verbose:
+            st.write(f"Text: {text}")
+            st.write("Predicted Labels:")
+
+        return predicted_labels
+
+if reference:
+    st.write("Labels loaded:", len(prior_labels))
+
+    query = encode_reference(reference)
+    similarity = cosine_similarity([query], X)[0]
+
+    df_emb = pd.DataFrame({
+        "text": texts,
+        "sim": similarity,
+        "label": prior_labels,
+    }).sort_values("sim", ascending=False)
+
+    top_size = st.slider("number of similar items", 1, 100, 5)
+    top_candidates = [(row["text"], row["sim"], row["label"]) for row in df_emb.to_dict(orient="records")][:top_size]
+
+    st.markdown("### 🧪 Similar example(s)")
+    if not top_candidates:
+        st.info("No more similar examples.")
+    else:
+        st.write(pd.DataFrame(top_candidates, columns=['text', 'similarity_score', 'label']))
+        top_labelled_df = pd.DataFrame(top_candidates, columns=['text', 'similarity_score', 'label'])
+
+        preds = dict(predict_content_multilabel(reference, threshold=0.2))
+        st.write(f"preds = {preds}")
+
+        col1, col2 = st.columns(2)
+
+        # Left: What training data says
+        with col1:
+            st.markdown("#### 📚 What Training Data Says")
+            fig1, ax1 = plt.subplots()
+            top_labelled_df['label'].value_counts(normalize=True).sort_values().plot(kind='barh', ax=ax1, color="lightcoral")
+            ax1.set_title("Label Distribution")
+            ax1.set_xlabel("Proportion")
+            ax1.grid(True, axis='x', linestyle='--', alpha=0.5)
+            st.pyplot(fig1)
+
+        # Right: What model predicts
+        with col2:
+            st.markdown("#### 🤖 Model Predictions")
+            if len(preds) == 0 or not prediction_choice:
+                st.write("Model is unsure")
+            else:
+                fig2, ax2 = plt.subplots()
+                pd.Series(preds).sort_values().plot.barh(color="skyblue", ax=ax2)
+                ax2.set_title("Predicted Probabilities")
+                ax2.set_xlabel("Probability")
+                ax2.grid(True, axis='x', linestyle='--', alpha=0.5)
+                st.pyplot(fig2)
+
+if prediction_choice and reference:
+    st.markdown("### 🔍 Model Explanation (Top Predicted Class)")
+
+    explainer = get_explainer()
+    attributions = explainer(reference)
+
+    st.markdown(f"**Predicted label:** `{explainer.predicted_class_name}`")
+
+    # Token importance bar chart
+    fig, ax = plt.subplots(figsize=(12, 1.5))
+    tokens, scores = zip(*attributions)
+    ax.bar(range(len(scores)), scores)
+    ax.set_xticks(range(len(tokens)))
+    ax.set_xticklabels(tokens, rotation=90)
+    ax.set_ylabel("Attribution Score")
+    ax.set_title("Token Attribution (Integrated Gradients)")
+    st.pyplot(fig)
+
+    # HTML Highlighted Text
+    st.markdown("#### 🔎 Highlighted Text Importance")
+    html_output = explainer.visualize().data
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # Render in Streamlit
+    st.markdown(html_output, unsafe_allow_html=True)