main.py

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
from datasets import load_dataset
import pandas as pd
import re

from numpy.random import randn
from keras.models import load_model
from matplotlib import pyplot
import matplotlib
import tensorflow as tf


from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

import chatbot
import textgen
import imagegen


app = FastAPI()

# Add CORS middleware to allow any origin
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allows all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allows all methods (GET, POST, etc.)
    allow_headers=["*"],  # Allows all headers
)

@app.get("/")
def root():
    return "Hello World"

# Define the Pydantic model to parse JSON input

from pydantic import BaseModel
class HistoryRequest(BaseModel):
    user: list[str]
    ai: list[str]

@app.post("/chatbot")
async def generate_response(history: HistoryRequest):
    print("Chatbot request")
    global model

    try:
        # check if model is already loaded
        if not isinstance(model, chatbot.ChatBot):
            model = chatbot.ChatBot(
                'models/fine-tuned-gpt2',
                GPT2Tokenizer.from_pretrained('models/fine-tuned-gpt2'),
                GPT2LMHeadModel.from_pretrained('models/fine-tuned-gpt2'),
                torch.device("cuda" if torch.cuda.is_available() else "cpu")
            )
    except NameError:
        # if model is not defined, load
        model = chatbot.ChatBot(
            'models/fine-tuned-gpt2',
            GPT2Tokenizer.from_pretrained('models/fine-tuned-gpt2'),
            GPT2LMHeadModel.from_pretrained('models/fine-tuned-gpt2'),
            torch.device("cuda" if torch.cuda.is_available() else "cpu")
        )
    
    response = model.generate_response(history)
    return response


class TextGenInput(BaseModel):
    user: str

@app.post("/text-gen")
async def generate_text(input: TextGenInput):
    print("Generating text request")
    global model
    directory = 'models/fine-tuned-gpt2-textgen'

    try:
        # check if model is already loaded
        if not isinstance(model, textgen.TextGen):
            model = textgen.TextGen(
                GPT2Tokenizer.from_pretrained(directory),
                GPT2LMHeadModel.from_pretrained(directory),
                torch.device("cuda" if torch.cuda.is_available() else "cpu")
            )
    except NameError:
        # if model is not defined, load
        model = textgen.TextGen(
            GPT2Tokenizer.from_pretrained(directory),
            GPT2LMHeadModel.from_pretrained(directory),
            torch.device("cuda" if torch.cuda.is_available() else "cpu")
        )

    response = model.generate_text(input.user)

    return response

@app.get("/face-gen")
async def generate_face():
    print("Generating face image request")

    global model
    
    try:
        # check if model is already loaded
        if not isinstance(model, imagegen.FaceGen):
            model = imagegen.FaceGen(
                model = load_model('models/face-gen-gan/generator_model_100.h5'),
                device =  '/CPU:0'
            )
    except NameError:
        # if model is not defined, load
        model = imagegen.FaceGen(
            model = load_model('models/face-gen-gan/generator_model_100.h5'),
            device = '/GPU:0' if tf.config.list_physical_devices('GPU') else '/CPU:0'
        )

    response = model.generate_image(latent_dim=100,n_samples=128,seed=randn(100*128))

    return response