app.py

# Paste your full Streamlit code here
# Include:
# - Chat UI
# - Preset questions
# - Filters by rating
# - Analytics charts (Rating & Sentiment)
# - RAG chain with Groq LLM
# - Executive summary generation
# - PDF and CSV download
import streamlit as st
import os
import pandas as pd
import tempfile

from langchain_groq import ChatGroq
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough

from reportlab.platypus import SimpleDocTemplate, Paragraph
from reportlab.lib.styles import getSampleStyleSheet

# ================================
# Constants / Config
# ================================
VECTORSTORE_PATH = "vectorstore"

# --------------------------------------------------
# PAGE CONFIG
# --------------------------------------------------
st.set_page_config(
    page_title="E-Commerce Review Intelligence",
    layout="wide"
)

st.title("🛍️ Customer Review Intelligence System")
st.caption("GenAI-powered insights using RAG (Groq + FAISS)")

# --------------------------------------------------
# SIDEBAR
# --------------------------------------------------
st.sidebar.header("🔐 Configuration")
groq_key = st.sidebar.text_input("Groq API Key", type="password")

if not groq_key:
    st.warning("Please enter your Groq API key")
    st.stop()

os.environ["GROQ_API_KEY"] = groq_key

rating_filter = st.sidebar.selectbox(
    "Filter by Rating",
    ["All", "Low (1–2)", "Medium (3)", "High (4–5)"]
)

# --------------------------------------------------
# LOAD DATA
# --------------------------------------------------
@st.cache_data
def load_data():
    df = pd.read_csv("Womens Clothing E-Commerce Reviews.csv")
    df = df.dropna(subset=["Review Text"])
    return df

df = load_data()

def map_sentiment(r):
    if r <= 2:
        return "Negative"
    elif r == 3:
        return "Neutral"
    else:
        return "Positive"

df["Sentiment"] = df["Rating"].apply(map_sentiment)

# --------------------------------------------------
# ANALYTICS
# --------------------------------------------------
st.subheader("📊 Customer Sentiment Overview")

col1, col2 = st.columns(2)
with col1:
    st.bar_chart(df["Rating"].value_counts().sort_index())
with col2:
    st.bar_chart(df["Sentiment"].value_counts())

# --------------------------------------------------
# LOAD VECTOR STORE
# --------------------------------------------------
@st.cache_resource
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

def load_vectorstore():
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )
    return FAISS.load_local(
        VECTORSTORE_PATH,
        embeddings,
        allow_dangerous_deserialization=True
    )

vectorstore = load_vectorstore()
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# --------------------------------------------------
# LOAD LLM
# --------------------------------------------------
llm = ChatGroq(
    model="llama-3.1-8b-instant",
    temperature=0
)

# --------------------------------------------------
# RAG PROMPT
# --------------------------------------------------
rag_prompt = PromptTemplate(
    input_variables=["context", "question", "rating"],
    template="""
    You are an AI assistant for an e-commerce company.

    Rating Filter: {rating}

    Customer Reviews:
    {context}

    Question:
    {question}

    Provide clear, actionable business insights.
    """
)

rag_chain = (
    {
        "context": retriever,
        "question": RunnablePassthrough(),
        "rating": RunnablePassthrough()
    }
    | rag_prompt
    | llm
)

# --------------------------------------------------
# CHAT UI
# --------------------------------------------------
if "messages" not in st.session_state:
    st.session_state.messages = []

st.subheader("💬 Ask Questions")

preset = st.selectbox(
    "Quick Business Questions",
    [
        "What are the most common customer complaints?",
        "What do customers say about sizing?",
        "Are customers satisfied with fabric quality?",
        "What improvements should the business prioritize?"
    ]
)

user_input = st.chat_input("Ask your own question")
question = user_input if user_input else preset

if question:
    st.session_state.messages.append(
        {"role": "user", "content": question}
    )

    response = rag_chain.invoke(
        {"question": question, "rating": rating_filter}
    )

    st.session_state.messages.append(
        {"role": "assistant", "content": response.content}
    )

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

# --------------------------------------------------
# EXECUTIVE SUMMARY
# --------------------------------------------------
st.subheader("📄 Executive Summary")

exec_prompt = PromptTemplate(
    input_variables=["reviews"],
    template="""
    Generate an executive summary covering:
    - Overall sentiment
    - Top complaints
    - Key strengths
    - Business recommendations

    Reviews:
    {reviews}
    """
)

summary_text = None

if st.button("Generate Executive Summary"):
    sample_reviews = " ".join(df["Review Text"].sample(30))
    summary_text = (exec_prompt | llm).invoke(
        {"reviews": sample_reviews}
    ).content
    st.success(summary_text)

# --------------------------------------------------
# PDF EXPORT
# --------------------------------------------------
def generate_pdf(text):
    styles = getSampleStyleSheet()
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
    doc = SimpleDocTemplate(temp_file.name)
    doc.build([
        Paragraph("<b>Executive Summary</b>", styles["Title"]),
        Paragraph(text.replace("\n", "<br/>"), styles["Normal"])
    ])
    return temp_file.name

if summary_text:
    pdf_path = generate_pdf(summary_text)
    with open(pdf_path, "rb") as f:
        st.download_button(
            "📥 Download Executive Summary (PDF)",
            f,
            file_name="executive_summary.pdf",
            mime="application/pdf"
        )

# --------------------------------------------------
# CSV EXPORT
# --------------------------------------------------
st.subheader("📥 Download Review Data")

csv = df[["Review Text", "Rating", "Sentiment"]].to_csv(index=False).encode("utf-8")

st.download_button(
    "Download CSV",
    csv,
    "customer_reviews.csv",
    "text/csv"
)
st.write("STREAMLIT APP IS RUNNING SUCCESSFULLY")