Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,35 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-"""
-# 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])),
-    ))
+# Load your model from Hugging Face Hub
+model_name = "laiBatool/laiba-spam-classifier-bert"  # replace with your actual model repo name
+
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs, dim=1).item()
+    return "Spam" if pred == 1 else "Not Spam"
+
+# Streamlit UI
+st.title("📧 Spam Detector - BERT")
+st.write("Paste an email message and check if it's spam.")
+
+user_input = st.text_area("Email content", height=200)
+
+if st.button("Classify"):
+    if not user_input.strip():
+        st.warning("Please enter some text.")
+    else:
+        result = predict(user_input)
+        st.success(f"Prediction: {result}")