# Step 1: Install the necessary libraries
# (Only needed locally; Hugging Face Spaces handles dependencies via 'requirements.txt')
# !pip install streamlit spacy numpy

import streamlit as st
import spacy
import numpy as np
import json
from numpy.linalg import norm

# Step 2: Load the spaCy model
nlp = spacy.load("en_core_web_md")

# Step 3: Load the FAQ data (ensure faqs.json is in the same directory)
with open('faqs.json', 'r') as f:
    faqs = json.load(f)

# Step 4: Flatten the FAQ structure and precompute vectors
faq_docs = []
for category, faq_list in faqs.items():
    for faq in faq_list:
        question = faq['question']
        answer = faq['answer']
        faq_vector = nlp(question).vector  # Precompute the vector
        faq_docs.append((question, answer, faq_vector))  # Store question, answer, and vector

# Step 5: Define the function to find the most relevant FAQs
def find_most_relevant_faq_optimized(query, faq_docs):
    """Find the top 3 most relevant FAQs based on semantic similarity."""
    query_vector = nlp(query).vector

    # Calculate cosine similarity between query and each FAQ
    similarities = [
        (question, answer, np.dot(query_vector, faq_vector) / (norm(query_vector) * norm(faq_vector)))
        for question, answer, faq_vector in faq_docs
    ]

    # Sort by similarity score (highest first)
    similarities = sorted(similarities, key=lambda x: x[2], reverse=True)

    return similarities[:3]  # Return top 3 FAQs

# Step 6: Create the Streamlit UI
st.title("Smart FAQ Search - SARAS AI Institute")
st.markdown("### Find Answers to Your Questions Instantly")

# Text input for the user query
query = st.text_input("Enter your question here:")

if query:
    # Find the most relevant FAQs
    top_faqs = find_most_relevant_faq_optimized(query, faq_docs)

    # Display the results
    st.markdown("### Top Relevant FAQs:")
    for i, (question, answer, score) in enumerate(top_faqs, 1):
        st.write(f"**{i}. {question}**")
        st.write(f"*Answer:* {answer}")
        st.write(f"**Similarity Score:** {score:.2f}")
else:
    st.write("Please enter a query to search for relevant FAQs.")