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import streamlit as st
import plotly.graph_objects as go
from groq import Groq
import numpy as np
import pandas as pd
import yfinance as yf
# prediction module
from predict_live import predict_next_price, get_live_data
# Streamlit Setup
st.set_page_config(page_title="📊 PSX Investment Advisor", layout="wide", page_icon="📈")
st.title("📊 PSX Investment Dashboard")
st.write("GenAI-powered stock insights with LIVE LSTM predictions")
# Sidebar Inputs
st.sidebar.header("Investment Options")
investment_type = st.sidebar.radio("Investment Type:", ["Short Term", "Long Term"])
sector = st.sidebar.selectbox("Sector:", ["Banking", "Energy"])
stock = st.sidebar.text_input("Stock Symbol:", "HBL") # user enters HBL
#PSX ticker format
ticker = stock.upper() + ".KA"
# LIVE Prediction
try:
close_prices = get_live_data(ticker)
predicted_price = predict_next_price(ticker)
except Exception as e:
st.error(f"Error fetching live data: {e}")
st.stop()
# Dummy sentiment
sentiment_score = 0.10
sentiment_adjusted_pred = predicted_price + (predicted_price * sentiment_score * 0.02)
# Dashboard
col1, col2, col3 = st.columns(3)
col1.metric("Selected Stock", stock)
col2.metric("Live Last Price", f"Rs {close_prices[-1]:.2f}")
col3.metric("LSTM Prediction", f"Rs {sentiment_adjusted_pred:.2f}")
#Plot
def plot_stock(close_prices, predicted_price):
fig = go.Figure()
# Plot live historical
fig.add_trace(go.Scatter(
y=close_prices,
x=list(range(len(close_prices))),
mode='lines',
name='Live Prices'
))
# Plot prediction
fig.add_trace(go.Scatter(
x=[len(close_prices)],
y=[predicted_price],
mode='markers',
name='Predicted Next Price',
marker=dict(size=12)
))
fig.update_layout(
title=f"{stock} Live Price + Prediction",
xaxis_title="Time",
yaxis_title="Price (Rs)",
template="plotly_dark"
)
return fig
st.subheader("Live Price Chart")
st.plotly_chart(plot_stock(close_prices, sentiment_adjusted_pred), use_container_width=True)
st.subheader("Prediction Result")
st.write(f"**LSTM Next Price Prediction:** Rs {sentiment_adjusted_pred:.2f}")
st.write(f"**Sentiment Impact:** {sentiment_score:.2f}")
# AI Chatbox via Groq
st.subheader("💬 Ask Your Investment Advisor")
user_input = st.text_input(
"Your question to AI:",
f"Should I invest in {stock} for {investment_type.lower()}?"
)
if st.button("Ask AI"):
if user_input:
with st.spinner("Thinking..."):
client = Groq(api_key=st.secrets["GROQ_API_KEY"])
advisor_prompt = f"""
You are a financial advisor AI.
Use the following data:
Current Price: {close_prices[-1]}
Predicted Next Price: {sentiment_adjusted_pred}
User Question: {user_input}
Respond MUST:
- Give a clear BUY / SELL / HOLD
- Explain in 2–3 simple lines
- Mention risk in simple terms
- 1 friendly tip
- No complex financial jargon
"""
response = client.chat.completions.create(
model="openai/gpt-oss-120b",
messages=[
{"role": "system", "content": "You are a professional stock advisor."},
{"role": "user", "content": advisor_prompt}
]
)
answer = response.choices[0].message.content
st.markdown(f"**AI:** {answer}")
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