Init

src/streamlit_app.py

@@ -1,40 +1,61 @@
-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])),
-    ))
+import joblib
+from huggingface_hub import hf_hub_download
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.feature_selection import RFE
+
+# Constants
+MODEL_REPO = "thatblackfox/civil"
+MODEL_FILE = "model.joblib"
+
+# ==== Load Model with Caching ====
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
+    model = joblib.load(model_path)
+    return model
+
+# ==== Streamlit UI ====
+st.set_page_config(page_title="Backward Linear Regression", layout="centered")
+st.title("Backward Linear Regression")
+st.markdown("Please enter the values of each feature")
+
+radius = st.number_input('RADIUS: ')
+sentry = st.number_input('Speed @ Entry: ')
+sexit  = st.number_input('Speed @ Exit : ')
+ls = st.number_input('Ls: ')
+tlength = st.number_input('Tan Length: ')
+e = st.number_input('e: ')
+sdistance = st.number_input('Sight distance: ')
+dangle = st.number_input('D Angle: ')
+total_width = st.number_input('Total width: ')
+cw_width = st.number_input('CW Width: ')
+lshoulder_width = st.number_input('Shoulder width (L) : ')
+rshoulder_width  = st.number_input('Shoulder width (R) : ')
+long_chord = st.number_input('Long Chord (Lc): ')
+appex_distance = st.number_input('Appex Distance (Es): ')
+mid_speed = st.number_input('Mid Speed: ')
+pcu = st.number_input('PCU: ')
+
+if st.button("Generate"):
+    try:
+        model = load_model()
+        ins = { 'Speed @ Entry': [sentry],
+                'e': [e],
+                'Shoulder width (L) ': [lshoulder_width],
+                'Shoulder width (R) ': [rshoulder_width],
+                'Mid Speed': [mid_speed]}
+        in_df = pd.DataFrame.from_dict(ins)
+        predict = model.predict(in_df)
+        # ==== Display Output ====
+        st.success("✅ Prediction generated successfully!")
+        st.write("### **Predicted Value:**")
+        st.metric(label="Model Output", value=round(predict, 3))
+
+        # Optional: Show input summary
+        with st.expander("Show Input Data"):
+            st.dataframe(in_df)
+
+    except Exception as err:
+        st.error(f"⚠️ Error: {err}")