import os
from dotenv import load_dotenv
import gradio as gr
import openai
from prompts import SYSTEM_PROMPT

client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Ensure all outputs are returned as string
def transcribe_audio(audio_file: str) -> str:
    with open(audio_file, "rb") as f:
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=f
        )
    return str(transcript.text)

def call_openai(event_details: str) -> str:
    prompt = SYSTEM_PROMPT.format(event_details)
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5,
        max_tokens=1024,
    )
    return str(response.choices[0].message.content)

def case_study_generator(event_details: str) -> str:
    return call_openai(event_details)

def case_study_from_audio(audio: str) -> str:
    text = transcribe_audio(audio)
    return case_study_generator(text)

with gr.Blocks() as demo:
    gr.Markdown("## 📄 iBoothMe Case Study Generator\nWrite a description or record audio.")

    with gr.Tab("Text Input"):
        text_input = gr.Textbox(label="Event Description", lines=10)
        text_button = gr.Button("Generate from Text")
        text_output = gr.Textbox(label="Generated Case Study", lines=15)
        text_button.click(fn=case_study_generator, inputs=text_input, outputs=text_output)

    with gr.Tab("Audio Input"):
        audio_input = gr.Audio(type="filepath", label="Upload or Record audio")
        audio_button = gr.Button("Generate from Audio")
        audio_output = gr.Textbox(label="Generated Case Study", lines=15)
        audio_button.click(fn=case_study_from_audio, inputs=audio_input, outputs=audio_output)

demo.launch()