app.py

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

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

# 🔁 Transcribe audio input using OpenAI Whisper API
def transcribe_audio(audio_path):
    if isinstance(audio_path, str) and os.path.exists(audio_path):
        with open(audio_path, "rb") as f:
            transcript = client.audio.transcriptions.create(
                model="whisper-1",
                file=f
            )
        return transcript.text
    return "❌ Invalid audio input."


def call_openai(event_details):
    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 response.choices[0].message.content

def case_study_generator(event_details):
    return call_openai(event_details)
    
def case_study_from_audio(audio):
    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, placeholder="Paste informal event description here...")
        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 your event description")
        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()