app.py

import streamlit as st
from PIL import Image
from rasa.core.agent import Agent

agent = Agent.load(model_path='models')

# # For Rasa 2 (I tried it with 2.8.8)
# # def generate_response(text):
# #     response = agent.parse_message_using_nlu_interpreter(
# #                 message_data='Hello there')
# #     return response


# # For Rasa 3
def generate_response(text):
    response = agent.parse_message(
        message_data='Hello there')
    return response


st.title("Automatic Review Bot")

image = Image.open("image.jpeg")
st.image(image, use_column_width=True)
user_ratings = st.slider("Select ratings (out of 5):", 1, 5)
user_text = st.text_area("Enter your text:", "")

if st.button("Generate Review"):
    if user_text:
        response = generate_response(user_text)
        st.success(response)
    else:
        st.warning("Please enter some text.")


st.info("Enter some text to generate a review.")

# import asyncio
# from rasa.core.agent import Agent
# from rasa.shared.utils.io import json_to_string
# from rasa.nlu.model import Interpreter


# class Model:

#     def __init__(self, model_path: str) -> None:
#         self.agent = Agent.load(model_path)
#         print("NLU model loaded")

#     def message(self, message: str) -> str:
#         message = message.strip()
#         result = asyncio.run(self.agent.parse_message(message))
#         return json_to_string(result)


# mdl = Model("./models/model.tar.gz")
# sentence = "The service was slow, but the food was great."
# print(mdl.message(sentence))