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# gradio_generator.py - CPU & Spaces-compatible Gradio interface with HF Inference API for STT & Monetization
import os
import json
import requests
import gradio as gr
from deployer.simulator_interface import VirtualRobot
from deployer.revenue_tracker import get_revenue_stats, package_artifacts
from core_creator.voice_to_app import VoiceToAppCreator
# Hugging Face Inference API settings
HF_STT_URL = "https://api-inference.huggingface.co/models/openai/whisper-small"
HK_API_KEY = os.getenv("HK_API_KEY")
if not HK_API_KEY:
raise EnvironmentError(
"Please set the HK_API_KEY environment variable for HuggingFace Inference API.")
HEADERS = {"Authorization": f"Bearer {HK_API_KEY}"}
# Core robot logic
def robot_behavior(user_input: str) -> str:
bot = VirtualRobot()
text = user_input.strip().lower()
if any(greet in text for greet in ["hello", "hi", "hey", "welcome"]):
return bot.perform_action("wave") + "\n" + bot.perform_action("say Hello there!")
if text.startswith("say "):
return bot.perform_action(text)
return bot.perform_action("say I'm sorry, I didn't understand that.")
# Transcribe via Hugging Face Inference API
def transcribe_audio(audio_file: str) -> str:
with open(audio_file, "rb") as f:
data = f.read()
response = requests.post(HF_STT_URL, headers=HEADERS, data=data)
if response.status_code != 200:
return f"❗ Transcription error: {response.status_code}"
result = response.json()
# The inference API returns {'text': '...'}
return result.get("text", "")
# Combined flow
def transcribe_and_respond(audio_file: str) -> str:
text = transcribe_audio(audio_file)
return robot_behavior(text)
# Package generation pipeline
def on_generate(i: str):
creator = VoiceToAppCreator(i)
assets = creator.run_pipeline()
zip_path = package_artifacts(assets)
blueprint = assets.get("blueprint", {})
code_files = assets.get("code", {})
code_preview = "\n\n".join([f"# {fname}\n{content}" for fname, content in code_files.items()])
return (
"✅ App generated successfully!",
zip_path,
blueprint,
code_preview
)
# Build full-featured Gradio app
def launch_gradio_app(
title: str = "RoboSage App",
description: str = "Your robot, your voice, monetized."
) -> gr.Blocks:
with gr.Blocks() as demo:
gr.Markdown(f"# 🚀 {title}\n\n{description}")
# 1. Generate & Download Artifacts
with gr.Accordion("🎨 Generate App & Download Artifacts", open=True):
idea = gr.Textbox(label="Robot Idea", placeholder="e.g. A friendly greeting robot.")
gen_btn = gr.Button("Generate & Package", key="gen-app-btn")
status = gr.Textbox(label="Generation Status", interactive=False)
download_zip = gr.File(label="Download Artifacts (.zip)")
blueprint_view = gr.JSON(label="App Blueprint")
code_view = gr.Code(label="Generated Code Preview", language="python")
gen_btn.click(
fn=on_generate,
inputs=[idea],
outputs=[status, download_zip, blueprint_view, code_view]
)
# 2. Robot Simulation (Text & Voice)
with gr.Accordion("🤖 Test Your Robot", open=False):
text_input = gr.Textbox(label="Text Command", placeholder="Type 'hello' or 'say Good job!'" )
text_btn = gr.Button("Send Text", key="send-text-btn")
text_output = gr.Textbox(label="Robot Response", lines=3, interactive=False)
text_btn.click(fn=robot_behavior, inputs=[text_input], outputs=[text_output])
gr.Markdown("---")
audio_input = gr.Audio(source="microphone", type="filepath", label="Record Command")
audio_btn = gr.Button("Send Audio", key="send-audio-btn")
audio_output = gr.Textbox(label="Robot Response (via voice)", lines=3, interactive=False)
audio_btn.click(fn=transcribe_and_respond, inputs=[audio_input], outputs=[audio_output])
# 3. Monetization & Revenue Dashboard
with gr.Accordion("💰 Monetization Dashboard", open=False):
subscribe_btn = gr.Button("Subscribe to Pro Plan", key="subscribe-btn")
subscribe_msg = gr.Textbox(label="Subscription Status", interactive=False)
rev_btn = gr.Button("View Revenue Stats", key="rev-stats-btn")
rev_table = gr.Dataframe(label="Revenue & Usage Metrics")
subscribe_btn.click(fn=lambda: "✅ Subscribed!", inputs=None, outputs=[subscribe_msg])
rev_btn.click(fn=get_revenue_stats, inputs=None, outputs=[rev_table])
return demo
if __name__ == "__main__":
app = launch_gradio_app()
app.launch()
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