app.py

import transformers
import torch 
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

# Load tokenizer and model
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

# Define a function to preprocess user input
def preprocess_input(text):
  encoded_input = tokenizer(text, return_tensors='pt')
  return encoded_input

# Define a function to generate response based on user input
def generate_response(user_input):
  encoded_input = preprocess_input(user_input)
  outputs = model(**encoded_input)
  # Extract relevant information from model outputs (e.g., predicted class)
  # Based on the extracted information, formulate a response using predefined responses or logic
  response = "I'm still under development, but I understand you said: {}".format(user_input)
  return response

# Start the chat loop
while True:
  # Get user input
  line = input("Enter your input: ")

  if line.lower() == "quit": 
    break

  # Generate response based on user input
  bot_response = generate_response(line)  # Assuming generate_response is defined elsewhere
  print("Bot:", bot_response)