app.py

import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from datetime import datetime
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# --- App Configuration ---
st.set_page_config(
    page_title="Prospira | Data-Driven Business Insights",
    page_icon="📈",
    layout="wide"
)

# --- Sidebar Navigation ---
st.sidebar.image("https://via.placeholder.com/150", use_column_width=True)  # Placeholder for Prospira logo
st.sidebar.title("Prospira Navigation")
page = st.sidebar.radio("Go to", ["🏠 Home", "📊 Business Dashboard", "🔍 Data Analytics", "💡 Brainstorm Hub", "🤖 AI Assistant"])

# --- Homepage ---
if page == "🏠 Home":
    st.title("🚀 Welcome to Prospira")
    st.subheader("📊 Data-Driven Solutions for Businesses and Creators")

    st.markdown("""
    **Prospira** empowers businesses and creators to enhance their content, products, and marketing strategies using AI-driven insights.
    
    ### **✨ Key Features**
    - **📈 Performance Analytics:** Real-time insights into business metrics.
    - **🔎 Competitive Analysis:** Benchmark your business against competitors.
    - **💡 Smart Product Ideas:** AI-generated recommendations for future products and content.
    - **🧠 AI Business Mentor:** Personalized AI guidance for strategy and growth.

    Explore how **Prospira** can help optimize your decision-making and drive success! 💡🚀
    """)

    st.image("https://via.placeholder.com/800x400", use_column_width=True)  # Placeholder for banner image

# --- Business Dashboard ---
elif page == "📊 Business Dashboard":
    st.title("📊 Prospira Business Dashboard")
    st.subheader("Real-time Performance Insights")

    # Generate Sample Business Data
    dates = pd.date_range(start='2024-01-01', end='2024-01-31')
    data = pd.DataFrame({
        'Date': dates,
        'Revenue': np.random.randint(800, 5000, len(dates)),
        'Users': np.random.randint(100, 500, len(dates)),
        'Engagement': np.random.uniform(0.3, 0.9, len(dates)),
        'Profit': np.random.uniform(200, 1500, len(dates)),
        'Category': np.random.choice(['Social Media', 'E-commerce', 'SaaS', 'Services'], len(dates))
    })

    # Top-Level KPIs
    col1, col2, col3, col4 = st.columns(4)
    col1.metric("Total Revenue", f"${data['Revenue'].sum():,.0f}")
    col2.metric("Total Users", f"{data['Users'].sum():,}")
    col3.metric("Avg Engagement", f"{data['Engagement'].mean():.2%}")
    col4.metric("Profit", f"${data['Profit'].sum():,.0f}")

    # Revenue & Profit Trends
    st.subheader("📈 Revenue & Profit Trends")
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Revenue'], mode='lines', name='Revenue', line=dict(color='blue')))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Profit'], mode='lines', name='Profit', line=dict(color='green')))
    st.plotly_chart(fig, use_container_width=True)

    # Category Performance Breakdown
    st.subheader("📊 Category Performance")
    category_summary = data.groupby('Category')[['Revenue', 'Users', 'Engagement']].mean().reset_index()
    fig_bar = px.bar(category_summary, x='Category', y='Revenue', color='Engagement', title="Revenue by Category")
    st.plotly_chart(fig_bar, use_container_width=True)

# --- Data Analytics Page ---
elif page == "🔍 Data Analytics":
    st.title("🔍 Upload and Analyze Your Data")

    uploaded_file = st.file_uploader("Upload your CSV file", type=['csv'])
    if uploaded_file:
        df = pd.read_csv(uploaded_file)
        st.write("### Preview of Your Data:")
        st.dataframe(df.head())

        st.write("### 📊 Basic Statistics")
        st.write(df.describe())

        st.write("### 📈 Data Visualization")
        col1, col2 = st.columns(2)
        x_col = col1.selectbox("X-Axis", df.columns)
        y_col = col2.selectbox("Y-Axis", df.columns)

        fig = px.scatter(df, x=x_col, y=y_col, title=f"Scatter Plot of {x_col} vs {y_col}")
        st.plotly_chart(fig, use_container_width=True)

# --- Brainstorming Hub ---
elif page == "💡 Brainstorm Hub":
    st.title("💡 AI-Powered Product Brainstorming")

    product_name = st.text_input("Product Name")
    category = st.selectbox("Category", ["Digital", "Physical", "Service"])
    audience = st.multiselect("Target Audience", ["Students", "Professionals", "Businesses"])
    description = st.text_area("Product Description")
    price_range = st.slider("Price Range ($)", 0, 1000, (50, 200))

    if st.button("Generate Insights"):
        st.success("AI-generated insights will appear here.")
        st.write(f"💡 **Suggested Price**: ${np.mean(price_range):.2f}")
        st.write(f"🚀 **Market Opportunity**: High")

# --- AI Assistant Page ---
elif page == "🤖 AI Assistant":
    st.title("🤖 Prospira AI Business Mentor")

    class LLaMAAssistant:
        def __init__(self, model_name="meta-llama/Llama-2-7b-chat-hf"):
            try:
                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
                self.model = AutoModelForCausalLM.from_pretrained(
                    model_name, torch_dtype=torch.float16, device_map="auto"
                )
            except Exception as e:
                st.error(f"Model loading error: {e}")
                self.model = None
                self.tokenizer = None

        def generate_response(self, prompt: str) -> str:
            if not self.model or not self.tokenizer:
                return "LLM not initialized."
            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.model.device)
            output = self.model.generate(input_ids, max_length=300)
            return self.tokenizer.decode(output[0], skip_special_tokens=True)

    assistant = LLaMAAssistant()
    user_input = st.text_area("Ask Prospira AI for Business Advice:")
    if st.button("Get AI Insights"):
        response = assistant.generate_response(user_input)
        st.write("**🧠 AI Response:**")
        st.info(response)