Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, pipeline
+import torch
+import plotly.express as px
+import numpy as np
+from utils import visualize_attention, list_supported_models
+
+st.set_page_config(page_title="Transformer Visualizer", layout="wide")
+
+st.title("🧠 Transformer Visualizer")
+st.markdown("Explore how Transformer models process and understand language.")
+
+task = st.sidebar.selectbox("Select Task", ["Text Classification", "Text Generation", "Question Answering"])
+model_name = st.sidebar.selectbox("Select Model", list_supported_models(task))
+
+text_input = st.text_area("Enter input text", "The quick brown fox jumps over the lazy dog.")
+
+if st.button("Run"):
+    st.info(f"Loading model: `{model_name}`...")
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    if task == "Text Classification":
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, output_attentions=True)
+    else:
+        model = AutoModel.from_pretrained(model_name, output_attentions=True)
+    
+    inputs = tokenizer(text_input, return_tensors="pt")
+    outputs = model(**inputs)
+    attentions = outputs.attentions
+
+    st.success("Model inference complete!")
+
+    if attentions:
+        st.subheader("Attention Visualization")
+        fig = visualize_attention(attentions, tokenizer, inputs)
+        st.plotly_chart(fig, use_container_width=True)
+    else:
+        st.warning("This model does not return attention weights.")
+
+    if task == "Text Classification":
+        st.subheader("Prediction")
+        pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
+        prediction = pipe(text_input)
+        st.write(prediction)
+
+st.sidebar.markdown("---")
+st.sidebar.write("App by Rahiya Esar 💖")