Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from transformers import pipeline
+from datetime import datetime, timedelta
+
+# Sentiment Analyzer
+sentiment_model = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+
+st.title("AI Market Analysis")
+
+ticker = st.text_input("Enter Stock/Crypto Ticker", value="AAPL")
+
+if st.button("Analyze"):
+    try:
+        # Get market data
+        data = yf.download(ticker, period="6mo")
+        data = data[['Close']].dropna()
+        data['Days'] = range(len(data))
+
+        # Model Prediksi Sederhana
+        model = LinearRegression()
+        model.fit(data[['Days']], data['Close'])
+        data['Predicted'] = model.predict(data[['Days']])
+
+        # Plot Harga
+        fig, ax = plt.subplots()
+        data['Close'].plot(ax=ax, label="Actual")
+        data['Predicted'].plot(ax=ax, label="Predicted")
+        ax.set_title(f"{ticker} Price Analysis")
+        ax.legend()
+        st.pyplot(fig)
+
+        # Dummy news (karena gak scrapping realtime news dulu)
+        st.subheader("News Sentiment Analysis (Sample Headlines)")
+        headlines = [
+            f"{ticker} stock rises after positive earnings report",
+            f"Market analysts are uncertain about {ticker} future",
+            f"{ticker} faces regulatory challenges in new markets"
+        ]
+
+        for h in headlines:
+            result = sentiment_model(h)[0]
+            st.write(f"**{h}** → `{result['label']}` ({round(result['score'], 2)})")
+
+    except Exception as e:
+        st.error(f"Error: {e}")