Update app.py

app.py

@@ -1,70 +1,62 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-from psx import Stocks
+import requests
 from tensorflow.keras.models import load_model
-from datetime import datetime, timedelta
 
-# ---------------------------
-# LOAD MODEL
-# ---------------------------
+# Load your saved model
 model = load_model("model.h5")
 
-# PSX object
-psx = Stocks()
+API_KEY = "demo"  # AlphaVantage public key (works for testing)
+
+def get_psx_data(symbol):
+    """Fetch PSX stock data using AlphaVantage"""
+    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}.XPSX&apikey={API_KEY}"
+    r = requests.get(url).json()
+
+    if "Time Series (Daily)" not in r:
+        return None
+
+    data = r["Time Series (Daily)"]
+    df = pd.DataFrame(data).T
+    df.index = pd.to_datetime(df.index)
+    df = df.sort_index()
+
+    df = df.rename(columns={
+        "1. open": "Open",
+        "2. high": "High",
+        "3. low": "Low",
+        "4. close": "Close",
+        "5. volume": "Volume"
+    })
+
+    df = df.astype(float)
+    return df
+
 
-# ---------------------------
-# STREAMLIT UI
-# ---------------------------
 st.title("📈 PSX Stock Prediction (Hamza Jadoon – FYP)")
-st.write("This app predicts the next-day closing price using your trained ML model.")
 
-# Input box
-symbol = st.text_input("Enter PSX Stock Symbol (e.g., UBL, ENGRO, OGDC):", "OGDC")
+symbol = st.text_input("Enter Stock Symbol (ex: OGDC, HBL, ENGRO):", "OGDC")
 
-# Predict button
 if st.button("Predict"):
-    try:
-        # ---------------------------
-        # FETCH PSX DATA
-        # ---------------------------
-        end = datetime.now()
-        start = end - timedelta(days=180)
-
-        df = psx.historical_data(
-            symbol=symbol,
-            start_date=start.strftime("%Y-%m-%d"),
-            end_date=end.strftime("%Y-%m-%d")
-        )
-
-        if df.empty:
-            st.error("❌ No data found for this symbol.")
+    df = get_psx_data(symbol)
+
+    if df is None:
+        st.error("❌ Failed to load PSX data. Try another symbol.")
+    else:
+        st.success("Data loaded!")
+
+        st.line_chart(df["Close"])
+
+        # Last 10 days
+        if len(df) < 10:
+            st.error("Not enough data (need 10 days).")
         else:
-            st.success("Data loaded successfully!")
-
-            # ---------------------------
-            # PREPARE DATA FOR MODEL
-            # ---------------------------
-            df = df[["Close"]].copy()
-
-            # Last 10 days as features
-            window = 10
-            if len(df) < window:
-                st.error("Not enough data for prediction.")
-            else:
-                X = df["Close"].values[-window:]
-                X = X.reshape(1, window, 1)
-
-                # ---------------------------
-                # PREDICT
-                # ---------------------------
-                prediction = model.predict(X)[0][0]
-
-                st.subheader("📊 Prediction")
-                st.write(f"**Next Close Price:** Rs {prediction:.2f}")
-
-                # Chart
-                st.line_chart(df["Close"])
-
-    except Exception as e:
-        st.error(f"Error: {e}")
+            X = df["Close"].values[-10:]
+            X = X.reshape(1, 10, 1)
+
+            # Predict
+            result = model.predict(X)[0][0]
+
+            st.subheader("📊 Prediction Result")
+            st.write(f"**Next Close Price:** Rs {result:.2f}")