Update app.py

app.py

@@ -4,7 +4,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import os
 
-# Optional imports for ML, but safely handled
+# Optional ML imports
 try:
     import tensorflow as tf
     from tensorflow.keras.models import load_model
@@ -16,10 +16,15 @@ except ImportError:
     MinMaxScaler = None
     joblib = None
 
-st.set_page_config(page_title="PSX Stock Predictor", layout="wide")
+# Optional sentiment analysis
+try:
+    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+    analyzer = SentimentIntensityAnalyzer()
+except ImportError:
+    analyzer = None
 
-st.title("📈 PSX Stock Predictor – HF Safe Version")
-st.write("This app works even if model or API is missing.")
+st.set_page_config(page_title="PSX Stock Predictor", layout="wide")
+st.title("📈 PSX Stock Predictor – HF Safe + Live Version")
 
 # ------------------------------
 # Load Model & Scaler
@@ -37,16 +42,53 @@ else:
     st.warning("Model not found. Using dummy predictions.")
 
 # ------------------------------
-# Dummy / Fallback Data
+# Fetch PSX Data
 # ------------------------------
-def get_dummy_data():
+API_KEY = os.getenv("ALPHAVANTAGE_API_KEY", None)
+
+def get_psx_data(symbol="HBL"):
+    if API_KEY:
+        try:
+            import requests
+            url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}.PSX&apikey={API_KEY}"
+            r = requests.get(url).json()
+            data = r.get("Time Series (Daily)", None)
+            if data:
+                df = pd.DataFrame(data).T
+                df.index = pd.to_datetime(df.index)
+                df = df.sort_index()
+                df = df[["4. close"]].rename(columns={"4. close": "Close"})
+                return df
+        except:
+            pass
+    # Fallback dummy data
     dates = pd.date_range(end=pd.Timestamp.today(), periods=200)
     prices = np.linspace(100, 150, 200) + np.random.normal(0, 2, 200)
     df = pd.DataFrame({"Close": prices}, index=dates)
     return df
 
 # ------------------------------
-# Prediction function
+# News Sentiment
+# ------------------------------
+NEWS_KEY = os.getenv("NEWSAPI_KEY", None)
+
+def get_sentiment(stock="HBL"):
+    if not analyzer or not NEWS_KEY:
+        return 0
+    try:
+        import requests
+        url = f"https://newsapi.org/v2/everything?q={stock}+Pakistan&apiKey={NEWS_KEY}"
+        r = requests.get(url).json()
+        articles = r.get("articles", [])[:5]
+        if not articles:
+            return 0
+        scores = [analyzer.polarity_scores(a["title"])['compound'] for a in articles]
+        return np.mean(scores) if scores else 0
+    except:
+        return 0
+
+# ------------------------------
+# Prediction
 # ------------------------------
 def predict_next(df):
     if MODEL_LOADED:
@@ -56,7 +98,7 @@ def predict_next(df):
         pred_real = scaler.inverse_transform([[pred]])[0][0]
         return pred_real
     else:
-        # Dummy prediction: next value + small random change
+        # Dummy prediction: last value + small random change
         return df["Close"].iloc[-1] * (1 + np.random.uniform(-0.01, 0.01))
 
 # ------------------------------
@@ -66,17 +108,21 @@ symbol = st.selectbox("Choose PSX Stock:", ["HBL", "UBL", "ENGRO", "PSO", "OGDC"
 
 if st.button("Fetch & Predict"):
     with st.spinner("Fetching data and predicting..."):
-        df = get_dummy_data()
+        df = get_psx_data(symbol)
+        sentiment = get_sentiment(symbol)
         prediction = predict_next(df)
+        # Adjust prediction with sentiment (2% weight)
+        sentiment_adj = prediction + (prediction * sentiment * 0.02)
 
         # Plot historical + predicted
         fig, ax = plt.subplots()
         ax.plot(df.index, df["Close"], label="Historical Price")
-        ax.axhline(prediction, linestyle="--", color="red", label="Predicted Price")
+        ax.axhline(sentiment_adj, linestyle="--", color="red", label="Predicted Price")
         ax.set_title(f"{symbol} Stock Price Prediction")
         ax.legend()
         st.pyplot(fig)
 
-        # Display result
+        # Display results
         st.subheader("Prediction Result")
-        st.write(f"**Predicted Price:** Rs {prediction:.2f}")
+        st.write(f"**Predicted Price:** Rs {sentiment_adj:.2f}")
+        st.write(f"**Sentiment Impact:** {sentiment:.3f}")