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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()
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