app.py

from flask import Flask, render_template, request, jsonify
from faq_data import faqs

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity


app = Flask(__name__)


# FAQ questions ko alag karna
questions = [faq["question"] for faq in faqs]


# TF-IDF vectorizer banana
vectorizer = TfidfVectorizer(stop_words="english")

faq_vectors = vectorizer.fit_transform(questions)


# User ke question ka answer find karna
def get_answer(user_question):

    # User question ko vector mein convert karna
    user_vector = vectorizer.transform([user_question])

    # Similarity calculate karna
    similarities = cosine_similarity(
        user_vector,
        faq_vectors
    )

    # Sabse similar question ka index
    best_match_index = similarities.argmax()

    # Similarity score
    best_score = similarities[0][best_match_index]

    # Agar koi suitable answer nahi mila
    if best_score < 0.35:
        return "Sorry, I don't understand your question."

    # Best matching FAQ ka answer
    return faqs[best_match_index]["answer"]


# Home page
@app.route("/")
def home():
    return render_template("index.html")


# Chatbot API
@app.route("/ask", methods=["POST"])
def ask():

    data = request.get_json()

    user_question = data.get("question", "")

    answer = get_answer(user_question)

    return jsonify({
        "answer": answer
    })


# Application start
if __name__ == "__main__":
    app.run(debug=True)