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Budi Mulyo

add app.py

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"""
SIXR Prompt‑to‑Prototype Studio – Gradio starter app
====================================================
A lightweight, one‑file scaffold you can push to a **Hugging Face Gradio Space**.
It collects a creative prompt (plus optional reference image & visual style),
then returns placeholder outputs for:
  • Loglines (3 variants)
  • Mood board (1 placeholder image + 9 themed text cells)
  • Shot list (6 items)
  • Animatic description (≈4 s)
  • Pitch summary
  • JSON download bundle
Swap the stub generators with your real AI calls (OpenAI, Replicate, etc.)
when you're ready. Works locally via `python app.py` or `gradio app.py`.
"""

from __future__ import annotations

import datetime as _dt
import json
import uuid
from pathlib import Path
import os

import gradio as gr
from PIL import Image, ImageDraw, ImageFont

# -----------------------------------------------------------------------------
# Config
# -----------------------------------------------------------------------------
STYLES = [
    "Default",
    "Cyberpunk",
    "Film Noir",
    "Watercolor",
    "Neo‑Deco",
]
PLACEHOLDER_COLOR = (24, 24, 32)  # dark bluish‑gray bg
TEXT_COLOR = (220, 220, 230)
TMP_DIR = Path("/tmp")

# It's good practice to load secrets from environment variables.
# On Hugging Face Spaces, you can set these in the repository settings.
ACCESS_TOKEN = os.environ.get("HF_TOKEN") # Or whatever you named your secret

# -----------------------------------------------------------------------------
# Helper functions
# -----------------------------------------------------------------------------

def _make_placeholder_image(text: str = "Moodboard", size: tuple[int, int] = (512, 512)) -> Image.Image:  # noqa: E501
    """Generate a simple placeholder PIL image with centered text."""
    img = Image.new("RGB", size, color=PLACEHOLDER_COLOR)
    draw = ImageDraw.Draw(img)
    try:
        font = ImageFont.truetype("arial.ttf", 32)
    except Exception:  # font might be missing in container – fall back
        font = ImageFont.load_default()

    w, h = draw.textsize(text, font=font)
    draw.text(((size[0] - w) / 2, (size[1] - h) / 2), text, fill=TEXT_COLOR, font=font)
    return img


def _timestamp() -> str:
    """Return an ISO‑8601 UTC timestamp (second precision)."""
    return _dt.datetime.utcnow().isoformat(timespec="seconds") + "Z"


def read_readme():
    with open("README.md", "r") as f:
        return f.read()


# -----------------------------------------------------------------------------
# Core generator (stub – replace with your own AI calls)
# -----------------------------------------------------------------------------

def generate_prototype(prompt: str, reference_image: Image.Image | None, style: str):
    """Return all UI outputs plus a path to the JSON bundle file."""

    prompt = (prompt or "").strip()
    if not prompt:
        raise gr.Error("Please enter a creative prompt first! ☝️")

    # --- Loglines ------------------------------------------------------------
    loglines = [
        f"{prompt.capitalize()} – through the lens of {style.lower()} (take {i})."
        for i in range(1, 4)
    ]

    # --- Moodboard -----------------------------------------------------------
    moodboard_img = _make_placeholder_image(f"{style} moodboard")
    moodboard_words = [f"{style} motif #{i}" for i in range(1, 10)]

    # --- Shot list -----------------------------------------------------------
    framings = [
        "Close‑up",
        "Medium shot",
        "Wide shot",
        "Low‑angle",
        "Over‑the‑shoulder",
        "Drone reveal",
    ]
    movements = [
        "push‑in",
        "pull‑out",
        "pan‑left",
        "tilt‑up",
        "handheld",
        "steadycam glide",
    ]
    shot_list = [f"{framings[i]} with a {movements[i]}" for i in range(len(framings))]

    # --- Animatic ------------------------------------------------------------
    animatic = (
        "4‑second sequence: stars dissolve into silhouette; camera drifts forward, "
        "title flares in neon before cutting to black."
    )

    # --- Pitch summary -------------------------------------------------------
    pitch = (
        f"An immersive concept blending {style.lower()} aesthetics with audience agency "
        f"around the theme: ‘{prompt}'."
    )

    # --- Bundle everything into a JSON dict ---------------------------------
    bundle = {
        "prompt": prompt,
        "style": style,
        "generated_at": _timestamp(),
        "loglines": loglines,
        "moodboard": {
            "words": moodboard_words,
            "image_placeholder": "(placeholder – no image bytes)",
        },
        "shot_list": shot_list,
        "animatic": animatic,
        "pitch_summary": pitch,
    }

    json_path = TMP_DIR / f"prototype_{uuid.uuid4().hex}.json"
    json_path.write_text(json.dumps(bundle, indent=2), encoding="utf-8")

    # Return tuple aligned with UI outputs order
    return (
        loglines,
        moodboard_img,
        "\n".join(moodboard_words),
        "\n".join(shot_list),
        animatic,
        pitch,
        str(json_path),
    )


# -----------------------------------------------------------------------------
# Gradio interface definition
# -----------------------------------------------------------------------------

def build_ui() -> gr.Blocks:
    with gr.Blocks(title="SIXR Prompt‑to‑Prototype Studio") as demo:
        gr.Markdown(read_readme())
        if ACCESS_TOKEN:
            gr.Markdown("Your access token is configured!")
        else:
            gr.Markdown("Access token not found. Set it in your Space secrets.")

        # --------------------------- Inputs ----------------------------------
        with gr.Row():
            prompt_in = gr.Textbox(
                label="Creative prompt",
                placeholder="Describe your immersive idea…",
                lines=3,
            )
            image_in = gr.Image(
                label="Reference image (optional)",
                type="pil",
                image_mode="RGB",
            )
            style_in = gr.Dropdown(STYLES, value=STYLES[0], label="Visual style")

        generate_btn = gr.Button("Generate prototype", variant="primary")

        # --------------------------- Outputs ---------------------------------
        with gr.Tabs():
            with gr.Tab("Loglines"):
                loglines_out = gr.JSON(label="Three logline variants")
            with gr.Tab("Moodboard"):
                mood_img_out = gr.Image(label="Moodboard image")
                mood_txt_out = gr.Textbox(label="Moodboard keywords", lines=10)
            with gr.Tab("Shot list"):
                shots_out = gr.Textbox(label="Shot list", lines=8)
            with gr.Tab("Animatic"):
                anim_out = gr.Textbox(label="Animatic description")
            with gr.Tab("Pitch summary"):
                pitch_out = gr.Textbox(label="Pitch summary")

        json_file_out = gr.File(label="Download JSON bundle", interactive=False)

        # Callback wiring
        generate_btn.click(
            fn=generate_prototype,
            inputs=[prompt_in, image_in, style_in],
            outputs=[
                loglines_out,
                mood_img_out,
                mood_txt_out,
                shots_out,
                anim_out,
                pitch_out,
                json_file_out,
            ],
        )

    return demo


# -----------------------------------------------------------------------------
# Main entrypoint
# -----------------------------------------------------------------------------
if __name__ == "__main__":
    ui = build_ui()
    # In Spaces, host/port are auto‑set; defaults work fine.
    ui.launch()

	"""
	SIXR Prompt‑to‑Prototype Studio – Gradio starter app
	====================================================
	A lightweight, one‑file scaffold you can push to a Hugging Face Gradio Space.
	It collects a creative prompt (plus optional reference image & visual style),
	then returns placeholder outputs for:
	• Loglines (3 variants)
	• Mood board (1 placeholder image + 9 themed text cells)
	• Shot list (6 items)
	• Animatic description (≈4 s)
	• Pitch summary
	• JSON download bundle

	Swap the stub generators with your real AI calls (OpenAI, Replicate, etc.)
	when you're ready. Works locally via `python app.py` or `gradio app.py`.
	"""

	from __future__ import annotations

	import datetime as _dt
	import json
	import uuid
	from pathlib import Path
	import os

	import gradio as gr
	from PIL import Image, ImageDraw, ImageFont

	# -----------------------------------------------------------------------------
	# Config
	# -----------------------------------------------------------------------------
	STYLES = [
	"Default",
	"Cyberpunk",
	"Film Noir",
	"Watercolor",
	"Neo‑Deco",
	]
	PLACEHOLDER_COLOR = (24, 24, 32) # dark bluish‑gray bg
	TEXT_COLOR = (220, 220, 230)
	TMP_DIR = Path("/tmp")

	# It's good practice to load secrets from environment variables.
	# On Hugging Face Spaces, you can set these in the repository settings.
	ACCESS_TOKEN = os.environ.get("HF_TOKEN") # Or whatever you named your secret

	# -----------------------------------------------------------------------------
	# Helper functions
	# -----------------------------------------------------------------------------

	def _make_placeholder_image(text: str = "Moodboard", size: tuple[int, int] = (512, 512)) -> Image.Image: # noqa: E501
	"""Generate a simple placeholder PIL image with centered text."""
	img = Image.new("RGB", size, color=PLACEHOLDER_COLOR)
	draw = ImageDraw.Draw(img)
	try:
	font = ImageFont.truetype("arial.ttf", 32)
	except Exception: # font might be missing in container – fall back
	font = ImageFont.load_default()

	w, h = draw.textsize(text, font=font)
	draw.text(((size[0] - w) / 2, (size[1] - h) / 2), text, fill=TEXT_COLOR, font=font)
	return img


	def _timestamp() -> str:
	"""Return an ISO‑8601 UTC timestamp (second precision)."""
	return _dt.datetime.utcnow().isoformat(timespec="seconds") + "Z"


	def read_readme():
	with open("README.md", "r") as f:
	return f.read()


	# -----------------------------------------------------------------------------
	# Core generator (stub – replace with your own AI calls)
	# -----------------------------------------------------------------------------

	def generate_prototype(prompt: str, reference_image: Image.Image \| None, style: str):
	"""Return all UI outputs plus a path to the JSON bundle file."""

	prompt = (prompt or "").strip()
	if not prompt:
	raise gr.Error("Please enter a creative prompt first! ☝️")

	# --- Loglines ------------------------------------------------------------
	loglines = [
	f"{prompt.capitalize()} – through the lens of {style.lower()} (take {i})."
	for i in range(1, 4)
	]

	# --- Moodboard -----------------------------------------------------------
	moodboard_img = _make_placeholder_image(f"{style} moodboard")
	moodboard_words = [f"{style} motif #{i}" for i in range(1, 10)]

	# --- Shot list -----------------------------------------------------------
	framings = [
	"Close‑up",
	"Medium shot",
	"Wide shot",
	"Low‑angle",
	"Over‑the‑shoulder",
	"Drone reveal",
	]
	movements = [
	"push‑in",
	"pull‑out",
	"pan‑left",
	"tilt‑up",
	"handheld",
	"steadycam glide",
	]
	shot_list = [f"{framings[i]} with a {movements[i]}" for i in range(len(framings))]

	# --- Animatic ------------------------------------------------------------
	animatic = (
	"4‑second sequence: stars dissolve into silhouette; camera drifts forward, "
	"title flares in neon before cutting to black."
	)

	# --- Pitch summary -------------------------------------------------------
	pitch = (
	f"An immersive concept blending {style.lower()} aesthetics with audience agency "
	f"around the theme: ‘{prompt}'."
	)

	# --- Bundle everything into a JSON dict ---------------------------------
	bundle = {
	"prompt": prompt,
	"style": style,
	"generated_at": _timestamp(),
	"loglines": loglines,
	"moodboard": {
	"words": moodboard_words,
	"image_placeholder": "(placeholder – no image bytes)",
	},
	"shot_list": shot_list,
	"animatic": animatic,
	"pitch_summary": pitch,
	}

	json_path = TMP_DIR / f"prototype_{uuid.uuid4().hex}.json"
	json_path.write_text(json.dumps(bundle, indent=2), encoding="utf-8")

	# Return tuple aligned with UI outputs order
	return (
	loglines,
	moodboard_img,
	"\n".join(moodboard_words),
	"\n".join(shot_list),
	animatic,
	pitch,
	str(json_path),
	)


	# -----------------------------------------------------------------------------
	# Gradio interface definition
	# -----------------------------------------------------------------------------

	def build_ui() -> gr.Blocks:
	with gr.Blocks(title="SIXR Prompt‑to‑Prototype Studio") as demo:
	gr.Markdown(read_readme())
	if ACCESS_TOKEN:
	gr.Markdown("Your access token is configured!")
	else:
	gr.Markdown("Access token not found. Set it in your Space secrets.")

	# --------------------------- Inputs ----------------------------------
	with gr.Row():
	prompt_in = gr.Textbox(
	label="Creative prompt",
	placeholder="Describe your immersive idea…",
	lines=3,
	)
	image_in = gr.Image(
	label="Reference image (optional)",
	type="pil",
	image_mode="RGB",
	)
	style_in = gr.Dropdown(STYLES, value=STYLES[0], label="Visual style")

	generate_btn = gr.Button("Generate prototype", variant="primary")

	# --------------------------- Outputs ---------------------------------
	with gr.Tabs():
	with gr.Tab("Loglines"):
	loglines_out = gr.JSON(label="Three logline variants")
	with gr.Tab("Moodboard"):
	mood_img_out = gr.Image(label="Moodboard image")
	mood_txt_out = gr.Textbox(label="Moodboard keywords", lines=10)
	with gr.Tab("Shot list"):
	shots_out = gr.Textbox(label="Shot list", lines=8)
	with gr.Tab("Animatic"):
	anim_out = gr.Textbox(label="Animatic description")
	with gr.Tab("Pitch summary"):
	pitch_out = gr.Textbox(label="Pitch summary")

	json_file_out = gr.File(label="Download JSON bundle", interactive=False)

	# Callback wiring
	generate_btn.click(
	fn=generate_prototype,
	inputs=[prompt_in, image_in, style_in],
	outputs=[
	loglines_out,
	mood_img_out,
	mood_txt_out,
	shots_out,
	anim_out,
	pitch_out,
	json_file_out,
	],
	)

	return demo


	# -----------------------------------------------------------------------------
	# Main entrypoint
	# -----------------------------------------------------------------------------
	if __name__ == "__main__":
	ui = build_ui()
	# In Spaces, host/port are auto‑set; defaults work fine.
	ui.launch()