app/app.py

import httpx
import os
import requests
import gradio as gr
import openai

from fastapi import Depends, FastAPI, Request
from app.db import User, create_db_and_tables
from app.schemas import UserCreate, UserRead, UserUpdate
from app.users import auth_backend, current_active_user, fastapi_users
from dotenv import load_dotenv
import examples as chatbot_examples

# Get the current environment from the environment variable
current_environment = os.getenv("APP_ENV", "dev")

# Load the appropriate .env file based on the current environment
if current_environment == "dev":
    load_dotenv(".env.dev")
elif current_environment == "test":
    load_dotenv(".env.test")
elif current_environment == "prod":
    load_dotenv(".env.prod")
else:
    raise ValueError("Invalid environment specified")
    
    
def api_login(email, password):
    port = os.getenv("APP_PORT")
    scheme = os.getenv("APP_SCHEME")
    host = os.getenv("APP_HOST")

    url = f"{scheme}://{host}:{port}/auth/jwt/login"
    payload = {
        'username': email,
        'password': password
    }
    headers = {
      'Content-Type': 'application/x-www-form-urlencoded'
    }

    response = requests.post(
        url,
        data=payload,
        headers=headers
    )
    
    if(response.status_code==200):
        response_json = response.json()
        api_key = response_json['access_token']
        return True, api_key
    else:
        response_json = response.json()
        detail = response_json['detail']
        return False, detail
    

def get_api_key(email, password):
    successful, message = api_login(email, password)
    
    if(successful):
        return os.getenv("APP_API_BASE"), message
    else:
        raise gr.Error(message)
        return "", ""
    
# Define a function to get the AI's reply using the OpenAI API
def get_ai_reply(message, model="gpt-3.5-turbo", system_message=None, temperature=0, message_history=[]):
    # Initialize the messages list
    messages = []
    
    # Add the system message to the messages list
    if system_message is not None:
        messages += [{"role": "system", "content": system_message}]

    # Add the message history to the messages list
    if message_history is not None:
        messages += message_history
    
    # Add the user's message to the messages list
    messages += [{"role": "user", "content": message}]
    
    # Make an API call to the OpenAI ChatCompletion endpoint with the model and messages
    completion = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature
    )
    
    # Extract and return the AI's response from the API response
    return completion.choices[0].message.content.strip()

# Define a function to handle the chat interaction with the AI model
def chat(model, system_message, message, chatbot_messages, history_state):
    # Initialize chatbot_messages and history_state if they are not provided
    chatbot_messages = chatbot_messages or []
    history_state = history_state or []
    
    # Try to get the AI's reply using the get_ai_reply function
    try:
        ai_reply = get_ai_reply(message, model=model, system_message=system_message, message_history=history_state)
    except Exception as e:
        # If an error occurs, raise a Gradio error
        raise gr.Error(e)
    
    # Append the user's message and the AI's reply to the chatbot_messages list
    chatbot_messages.append((message, ai_reply))
    
    # Append the user's message and the AI's reply to the history_state list
    history_state.append({"role": "user", "content": message})
    history_state.append({"role": "assistant", "content": ai_reply})
    
    # Return None (empty out the user's message textbox), the updated chatbot_messages, and the updated history_state
    return None, chatbot_messages, history_state

# Define a function to launch the chatbot interface using Gradio
def get_chatbot_app(additional_examples=[]):
    # Load chatbot examples and merge with any additional examples provided
    examples = chatbot_examples.load_examples(additional=additional_examples)
    
    # Define a function to get the names of the examples
    def get_examples():
        return [example["name"] for example in examples]

    # Define a function to choose an example based on the index
    def choose_example(index):
        if(index!=None):
            system_message = examples[index]["system_message"].strip()
            user_message = examples[index]["message"].strip()
            return system_message, user_message, [], []
        else:
            return "", "", [], []

    # Create the Gradio interface using the Blocks layout
    with gr.Blocks() as app:
        with gr.Tab("Conversation"):
            with gr.Row():
                with gr.Column():
                    # Create a dropdown to select examples
                    example_dropdown = gr.Dropdown(get_examples(), label="Examples", type="index")
                    # Create a button to load the selected example
                    example_load_btn = gr.Button(value="Load")
                    # Create a textbox for the system message (prompt)
                    system_message = gr.TextArea(label="System Message (Prompt)", value="You are a helpful assistant.", lines=20, max_lines=400)
                with gr.Column():
                    # Create a dropdown to select the AI model
                    model_selector = gr.Dropdown(
                        ["gpt-3.5-turbo"],
                        label="Model",
                        value="gpt-3.5-turbo"
                    )
                    # Create a chatbot interface for the conversation
                    chatbot = gr.Chatbot(label="Conversation")
                    # Create a textbox for the user's message
                    message = gr.Textbox(label="Message")
                    # Create a state object to store the conversation history
                    history_state = gr.State()
                    # Create a button to send the user's message
                    btn = gr.Button(value="Send")

                # Connect the example load button to the choose_example function
                example_load_btn.click(choose_example, inputs=[example_dropdown], outputs=[system_message, message, chatbot, history_state])
                # Connect the send button to the chat function
                btn.click(chat, inputs=[model_selector, system_message, message, chatbot, history_state], outputs=[message, chatbot, history_state])
        with gr.Tab("Get API Key"):
            email_box = gr.Textbox(label="Email Address", placeholder="Student Email")
            password_box = gr.Textbox(label="Password", type="password", placeholder="Student ID")
            btn = gr.Button(value ="Generate")
            api_host_box = gr.Textbox(label="OpenAI API Base", interactive=False)
            api_key_box = gr.Textbox(label="OpenAI API Key", interactive=False)
            btn.click(get_api_key, inputs = [email_box, password_box], outputs = [api_host_box, api_key_box])
        # Return the app
        return app

app = FastAPI()

app.include_router(
    fastapi_users.get_auth_router(auth_backend), prefix="/auth/jwt", tags=["auth"]
)
app.include_router(
    fastapi_users.get_register_router(UserRead, UserCreate),
    prefix="/auth",
    tags=["auth"],
)
app.include_router(
    fastapi_users.get_users_router(UserRead, UserUpdate),
    prefix="/users",
    tags=["users"],
)

@app.get("/authenticated-route")
async def authenticated_route(user: User = Depends(current_active_user)):
    return {"message": f"Hello {user.email}!"}

@app.post("/v1/chat/completions")
async def openai_api_chat_completions_passthrough(
    request: Request,
    user: User = Depends(fastapi_users.current_user()),
):
    if not user:
        raise HTTPException(status_code=401, detail="Unauthorized")

    # Get the request data and headers
    request_data = await request.json()
    request_headers = request.headers
    openai_api_key = os.getenv("OPENAI_API_KEY")
    
    if(request_data['model']=='gpt-4' or request_data['model'] == 'gpt-4-32k'):
        print("User requested gpt-4, falling back to gpt-3.5-turbo")
        request_data['model'] = 'gpt-3.5-turbo'

    # Forward the request to the OpenAI API
    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        json=request_data,
        headers={
            "Content-Type": request_headers.get("Content-Type"),
            "Authorization": f"Bearer {openai_api_key}",
        },
    )
    print(response)

    # Return the OpenAI API response
    return response.json()

@app.on_event("startup")
async def on_startup():
    # Not needed if you setup a migration system like Alembic
    await create_db_and_tables()
    
gradio_gui = get_chatbot_app()
gradio_gui.auth = api_login
gradio_gui.auth_message = "Hello"
app = gr.mount_gradio_app(app, gradio_gui, path="/gradio")