src/streamlit_app.py

import os
import json
import streamlit as st
from transformers import pipeline

# Set environment variables for Hugging Face cache
os.environ['HF_HOME'] = os.path.expanduser('~/.cache/huggingface')
os.environ['TRANSFORMERS_CACHE'] = os.environ['HF_HOME']

# Streamlit configuration
st.set_page_config(page_title="SmartProcureAI", layout="wide")

# Load data
with open("sap_data.json") as f:
    sap_data = json.load(f)

# Initialize the text generation pipeline
summarizer = pipeline("text-generation", model="gpt2", max_new_tokens=150)

# App title
st.title("📦 SmartProcureAI: GenAI-Powered SAP Procurement Assistant")

# Sidebar inputs
st.sidebar.header("📄 RFQ Generator")
product_type = st.sidebar.selectbox("Select Product", [p["name"] for p in sap_data["products"]])
budget = st.sidebar.number_input("Enter Budget (INR)", value=50000)

# Retrieve selected product and vendor
product = next(p for p in sap_data["products"] if p["name"] == product_type)
vendor = next(v for v in sap_data["vendors"] if v["id"] == product["vendor_id"])

# Generate prompt
prompt = f"""
Write a professional RFQ email for the following procurement need:
- Product: {product['name']}
- Vendor: {vendor['name']}
- Price: ₹{product['price']}
- Delivery Time: {vendor['delivery_time']} days
- Max Budget: ₹{budget}
Keep it concise and formal.
"""

# Generate RFQ
if st.sidebar.button("📝 Generate RFQ"):
    with st.spinner("Generating RFQ..."):
        output = summarizer(prompt)[0]["generated_text"]
        st.subheader("📄 Generated RFQ:")
        st.code(output)

# Display vendor information
st.sidebar.markdown("---")
st.sidebar.subheader("📊 Vendor Summary")
if st.sidebar.button("🔍 Show Vendor Info"):
    st.write("### 🧾 Vendor Details")
    st.json(vendor)