app.py

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

# Load DialoGPT model and tokenizer
model_name = "microsoft/DialoGPT-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load a sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

# Initialize conversation history
chat_history_ids = None

# Chat loop
print("Chatbot: Hi! I'm DialoGPT. Let's chat about anything, including movies! (type 'quit' to exit)")

while True:
    try:
        # Get user input
        user_input = input("You: ")
    except EOFError:
        # Handle EOFError by setting a default input and continuing
        print("\nChatbot: I noticed an issue with input, but let's continue!")
        user_input = "quit"  # Default to quit if EOFError occurs

    # Exit the loop if user types 'quit'
    if user_input.lower() == "quit":
        print("Chatbot: Goodbye! Have a great day!")
        break

    # Check if the input is a movie review query
    if "movie" in user_input.lower() or "film" in user_input.lower():
        # Analyze the sentiment of the user input
        sentiment = sentiment_analyzer(user_input)[0]
        sentiment_label = sentiment["label"]
        sentiment_score = sentiment["score"]

        if sentiment_label == "POSITIVE":
            response = f"Sounds like you really enjoyed the movie! I'm glad to hear that. 😊 (Confidence: {sentiment_score:.2f})"
        else:
            response = f"I'm sorry to hear you didn't enjoy the movie. 😞 (Confidence: {sentiment_score:.2f})"

        print(f"Chatbot: {response}")
        continue

    # Encode user input and add conversation history
    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
    chat_history_ids = (
        torch.cat([chat_history_ids, input_ids], dim=-1) if chat_history_ids is not None else input_ids
    )

    # Generate a response
    response_ids = model.generate(
        chat_history_ids,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
        top_k=50,
        top_p=0.9,
        temperature=0.7,
    )

    # Decode and print the response
    response = tokenizer.decode(response_ids[:, chat_history_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"Chatbot: {response}")