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# app/main.py
import streamlit as st
from ocr_extractor import extract_text_from_pdf
from classifier import classify_bill, extract_fields
from recommender import recommend_plans
from visualisation import show_comparison_chart
from agent_chain import agentic_reasoning
st.set_page_config(page_title="Telco Bill Recommender", layout="wide")
st.title("📄 Telco Bill Scanner & Plan Recommender")
# Upload Bill
uploaded_file = st.file_uploader("Upload your Telco Bill (PDF or JSON)", type=["pdf", "json"])
if uploaded_file:
st.success("Bill uploaded successfully!")
# Step 0: OCR Extraction for PDFs
if uploaded_file.name.endswith(".pdf"):
st.subheader("Step 0: OCR Extraction")
extracted_text = extract_text_from_pdf(uploaded_file)
st.text_area("Extracted Text (Preview)", extracted_text[:1000])
# Extract structured fields from OCR text
fields = extract_fields(extracted_text)
else:
extracted_text = None
fields = None
# Step 1: Customer Type Classification
st.subheader("Step 1: Customer Type Identification")
if fields:
customer_type, details = classify_bill(None, fields)
else:
customer_type, details = classify_bill(uploaded_file)
st.write(f"**Detected Type:** {customer_type}")
st.json(details)
# Fallback to Agentic AI if classification uncertain
if customer_type == "Unknown":
st.warning("Classification uncertain. Using Agentic AI fallback reasoning...")
# Use extracted text if available; else read file as text
if extracted_text:
agent_input = extracted_text
else:
try:
agent_input = uploaded_file.read().decode("utf-8", errors="ignore")
except:
agent_input = "Could not read file."
customer_type = agentic_reasoning(agent_input)
st.write(f"**Agentic AI suggests:** {customer_type}")
# Step 2: Plan Recommendation
st.subheader("Step 2: Plan Recommendations")
recommendations = recommend_plans(uploaded_file, customer_type)
st.table(recommendations)
# Step 3: Visualisation of Savings
st.subheader("Step 3: Usage & Cost Comparison")
show_comparison_chart(uploaded_file, recommendations)
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