Update app.py

app.py

@@ -1,31 +1,50 @@
+
 import streamlit as st
-import yt_dlp
-
-def download_video(url):
-    try:
-        ydl_opts = {
-            'format': 'best',
-            'outtmpl': 'downloads/%(title)s.%(ext)s',
-        }
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            info = ydl.extract_info(url, download=True)
-            return info.get('title', 'Video')
-    except Exception as e:
-        return str(e)
-
-st.title("YouTube Video Downloader")
-
-url = st.text_input("Enter the YouTube URL")
-
-if st.button("Download"):
-    if url:
-        with st.spinner("Downloading..."):
-            video_title = download_video(url)
-            if "https://" in url:
-                st.success(f"Downloaded: {video_title}")
-            else:
-                st.error("Invalid URL. Please enter a valid YouTube link.")
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import pandas as pd
+
+# Create the Streamlit app
+st.title("Transformer Tap Changer Optimization App")
+
+# Input fields for voltage and load
+voltage_in = st.number_input("Enter the Input Voltage (kV)", min_value=90, max_value=150, value=115)
+voltage_out = st.number_input("Enter the Output Voltage (kV)", min_value=80, max_value=130, value=100)
+load = st.number_input("Enter the Current Load (MW)", min_value=50, max_value=500, value=250)
+
+# Simple logic to determine the tap position based on voltage_out
+def optimize_tap_change(voltage_in, voltage_out, load):
+    # Example logic to decide when to change tap position
+    if voltage_out < 95:
+        return "Tap 1 (Increase voltage)"
+    elif voltage_out > 105:
+        return "Tap 5 (Decrease voltage)"
     else:
-        st.error("Please provide a YouTube URL.")
+        return "Tap 3 (Optimal position)"
+
+# Display the recommended tap position
+recommended_tap = optimize_tap_change(voltage_in, voltage_out, load)
+
+st.write(f"**Recommended Tap Position**: {recommended_tap}")
+
+# Create a simple dataset for machine learning model training
+# Example data (features: voltage_in, load, previous_tap_change)
+data = {
+    'voltage_in': [110, 115, 120, 125],
+    'load': [250, 300, 350, 400],
+    'tap_position': [3, 4, 2, 5]  # Target variable: tap position
+}
+df = pd.DataFrame(data)
+
+X = df[['voltage_in', 'load']]  # Features
+y = df['tap_position']  # Target variable
+
+# Train the Linear Regression model
+model = LinearRegression()
+model.fit(X, y)
+
+# Predict optimal tap position based on current input
+predicted_tap = model.predict([[voltage_in, load]])
 
+st.write(f"**Predicted Tap Position using Machine Learning**: Tap {int(predicted_tap[0])}")