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from flask import Flask, request, jsonify, send_from_directory
from flask_cors import CORS
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from datasets import load_dataset
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
app = Flask(__name__)
CORS(app)
# Load the DialoGPT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
# Load the Bitext Travel Dataset
try:
dataset = load_dataset("bitext/Bitext-travel-llm-chatbot-training-dataset")
print("Bitext dataset loaded successfully.")
except Exception as e:
print(f"Error loading Bitext dataset: {str(e)}")
dataset = None
# Load a pre-trained sentence transformer model for semantic similarity
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
def find_closest_response(user_input):
if dataset is None:
return "I'm sorry, but I couldn't load the travel dataset. Please try again later."
try:
# Precompute embeddings for all instructions in the dataset
instructions = [example['instruction'] for example in dataset['train']]
instruction_embeddings = sentence_model.encode(instructions)
# Encode the user input
user_embedding = sentence_model.encode([user_input])
# Compute cosine similarity between the user input and all instructions
similarities = cosine_similarity(
user_embedding, instruction_embeddings)
closest_index = similarities.argmax()
# Return the closest response
return dataset['train'][closest_index]['response']
except Exception as e:
print(f"Error finding closest response: {str(e)}")
return "I'm sorry, but I couldn't find a suitable response. Please try again."
def chat_with_bot(user_input, chat_history_ids=None):
try:
# Find the closest response from the Bitext dataset
closest_response = find_closest_response(user_input)
print(f"Closest response: {closest_response}") # Debugging statement
# Generate a response using DialoGPT
new_user_input_ids = tokenizer.encode(
user_input + tokenizer.eos_token, return_tensors='pt')
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids],
dim=-1) if chat_history_ids is not None else new_user_input_ids
chat_history_ids = model.generate(
bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
bot_reply = tokenizer.decode(
chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT response: {bot_reply}") # Debugging statement
# Combine the Bitext response and DialoGPT response
combined_response = f"{closest_response}\n\n{bot_reply}"
return combined_response, chat_history_ids
except Exception as e:
print(f"Exception: {str(e)}") # Print the full exception
return "Sorry, an unexpected error occurred. Please try again.", None
# Serve the HTML file
@app.route("/")
def serve_html():
return send_from_directory(".", "index.html")
# Chat route
@app.route("/chat", methods=["POST"])
def chat():
user_input = request.json.get("message")
if not user_input:
return jsonify({"error": "No message provided"}), 400
# Get the chat history from the session (if any)
chat_history_ids = request.json.get("chat_history_ids")
if chat_history_ids:
chat_history_ids = torch.tensor(
chat_history_ids) # Convert back to a tensor
# Get the bot's response
bot_response, chat_history_ids = chat_with_bot(
user_input, chat_history_ids)
# Convert chat_history_ids to a list for JSON serialization
chat_history_ids_list = chat_history_ids.tolist(
) if chat_history_ids is not None else None
return jsonify({
"response": bot_response,
"chat_history_ids": chat_history_ids_list
})
if __name__ == "__main__":
app.run(debug=True)
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