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import json
import random
from fuzzywuzzy import process
from flask import Flask, request, render_template
import joblib
app = Flask(__name__)
# Load intents.json
with open("static/intents.json", "r") as file:
intents = json.load(file)
import joblib
nlp = joblib.load("static/chatbot_model1.joblib")
# Extract all possible questions for fuzzy matching
all_questions = []
question_to_intent = {}
for intent in intents["intents"]:
for pattern in intent["patterns"]:
all_questions.append(pattern)
question_to_intent[pattern] = intent["tag"]
# Function to get intent using the trained model
def get_intent(text):
doc = nlp(text)
intent = max(doc.cats, key=doc.cats.get)
return intent, doc.cats[intent]
# Function for fuzzy matching
def fuzzy_match(text, questions, threshold=80):
match, score = process.extractOne(text, questions)
return match if score >= threshold else None
# Function to get chatbot response
def chatbot_response(user_input):
intent, confidence = get_intent(user_input)
if confidence > 0.75: # If spaCy model is confident
for intent_data in intents["intents"]:
if intent_data["tag"] == intent:
return random.choice(intent_data["responses"])
# Fallback to fuzzy matching
best_match = fuzzy_match(user_input, all_questions)
if best_match:
matched_intent = question_to_intent[best_match]
for intent_data in intents["intents"]:
if intent_data["tag"] == matched_intent:
return random.choice(intent_data["responses"])
return "Sorry, I didn't understand that. Can you rephrase?"
# Flask routes
@app.route("/")
def index():
return render_template("chat.html") # Make sure chat.html exists
@app.route("/get", methods=["POST"])
def chat():
msg = request.form["msg"]
response = chatbot_response(msg)
return response
if __name__ == "__main__":
app.run(debug=False)
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