app.py

# Import required libraries
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import json

# Create FastAPI app instance
app = FastAPI()

# Load DialoGPT model and tokenizer
try:
    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
except Exception as e:
    raise HTTPException(status_code=500, detail=f"Model loading failed: {e}")

# Load courses data
try:
    with open("uts_courses.json", "r") as file:
        courses_data = json.load(file)
except Exception as e:
    raise HTTPException(status_code=500, detail=f"Courses data loading failed: {e}")

# Define user input model
class UserInput(BaseModel):
    user_input: str

# Generate response function
def generate_response(user_input: str):
    """
    Generate response based on user input

    Args:
        user_input: User input text

    Returns:
        Generated response text
    """
    if user_input.lower() == "help":
        return "I can help you with UTS courses information, feel free to ask!"
    elif user_input.lower() == "exit":
        return "Goodbye!"
    elif user_input.lower() == "list courses":
        # Generate course list
        course_list = "\n".join([f"{category}: {', '.join(courses)}" for category, courses in courses_data["courses"].items()])
        return f"Here are the available courses:\n{course_list}"
    elif user_input.lower() in courses_data["courses"]:
        # List courses under the specified course category
        return f"The courses in {user_input} category are: {', '.join(courses_data['courses'][user_input])}"
    else:
        # Use DialoGPT model to generate response
        input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
        response_ids = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
        response = tokenizer.decode(response_ids[0], skip_special_tokens=True)
        return response

# Define API route
@app.post("/")
async def chat(user_input: UserInput):
    """
    Process user input and return response

    Args:
        user_input: User input JSON data

    Returns:
        JSON data containing the response text
    """
    response = generate_response(user_input.user_input)
    return {"response": response}