Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,46 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+from transformers import pipeline
+import os
+
+# Load the Hugging Face pipeline (with private token if needed)
+pipe = pipeline(
+    "text-classification",
+    model="t-Shr/SPAM_OR_HAM_SMS",  # Replace with your model path
+    use_auth_token=os.environ.get("HF_TOKEN")  # or just paste the token as string if running locally
+)
+
+# 🧠 Custom prediction logic with trust score fusion
+def predict(text, trust_score=0.5):
+    output = pipe(text)[0]
+    model_prob = output['score'] if output['label'] == 'LABEL_1' else 1 - output['score']
+
+    # Fused score = weighted confidence + inverse trust
+    alpha = 0.7
+    fused_score = alpha * model_prob + (1 - alpha) * (1 - trust_score)
+    risk_score = int(round(fused_score * 100))
+    label = "SPAM" if fused_score >= 0.5 else "HAM"
+
+    return label, round(model_prob, 4), round(fused_score, 4), risk_score
+
+# Streamlit UI
+st.set_page_config(page_title="📩 SMS Spam Classifier", layout="centered")
+st.title("📩 Real-Time SMS Spam Classifier")
+st.markdown("Detect whether an SMS is **spam** or **ham**, with model confidence, fused score and risk score.")
+
+# Text input
+sms_text = st.text_area("✉️ Enter SMS Text:", height=150)
+
+# Trust score slider
+trust_score = st.slider("🔐 Trust Score (user reliability)", 0.0, 1.0, 0.5, step=0.01)
+
+# Predict button
+if st.button("🔍 Predict"):
+    if sms_text.strip() == "":
+        st.warning("Please enter some text to analyze.")
+    else:
+        label, confidence, fused_score, risk_score = predict(sms_text, trust_score)
+
+        st.markdown(f"### ✅ Prediction: `{label}`")
+        st.metric(label="📊 Model Confidence", value=f"{confidence:.2f}")
+        st.metric(label="🔁 Fused Score", value=f"{fused_score:.2f}")
+        st.metric(label="⚠️ Risk Score", value=f"{risk_score}/100")