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Yoga-1M / code /backend /app.py

Arko007

real backend code for production-path tests

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import os
import numpy as np
import gradio as gr

import torch

from app.main import app as api
from app.config import settings
from app.services.hf_loader import get_mlp_model
from app.utils.geometry import FEATURE_NAMES, extract_angles_from_landmarks
from app.utils.rules_classifier import hybrid_classify

# ---------------------------------------------------------------------------
# Companion Gradio UI. HF Spaces on the Docker SDK cannot use ZeroGPU (free
# dynamically-allocated GPU quota); the Gradio SDK can. Mounting Gradio onto
# the existing FastAPI `app` (imported above, unchanged) keeps every /api/*
# route the production Next.js frontend already depends on working exactly
# as before -- this file only adds a themed demo page for anyone who opens
# the raw Space URL directly, it does not replace the API.
# ---------------------------------------------------------------------------

THEME_CSS = """
@import url('https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700;1,9..40,400&family=Lora:wght@600&display=swap');
:root {
    --color-primary: #4f67f4;
    --color-primary-2: #06b6d4;
    --color-success: #059669;
    --color-warning: #d97706;
    --color-error: #dc2626;
    --bg-base: #f7f6f3;
    --bg-surface: #ffffff;
    --color-text: #1a1614;
    --color-text-muted: #3d3530;
}
.gradio-container {
    background: var(--bg-base) !important;
    font-family: 'DM Sans', 'Helvetica Neue', system-ui, sans-serif !important;
    color: var(--color-text) !important;
}
#asana-header {
    background: linear-gradient(135deg, #4f67f4, #06b6d4);
    border-radius: 18px;
    padding: 28px 32px;
    color: #ffffff !important;
    margin-bottom: 20px;
}
#asana-header h1 {
    font-family: 'Lora', Georgia, serif !important;
    font-weight: 600 !important;
    color: #ffffff !important;
    margin: 0 0 6px 0 !important;
}
#asana-header p { color: rgba(255,255,255,0.92) !important; margin: 0 !important; }
.asana-card {
    background: var(--bg-surface) !important;
    border: 1px solid rgba(0,0,0,0.08) !important;
    border-radius: 14px !important;
    box-shadow: 0 4px 16px rgba(0,0,0,0.05) !important;
}
#cta-row a.cta-button {
    display: inline-block;
    background: linear-gradient(135deg, #4f67f4, #06b6d4);
    color: #ffffff !important;
    padding: 10px 20px;
    border-radius: 9999px;
    text-decoration: none;
    font-weight: 600;
    font-size: 0.9rem;
}
"""

VERCEL_URL = "https://yoga-posture-correction-system.vercel.app"


def analyse_demo_image(image: np.ndarray):
    """
    Runs MediaPipe Pose server-side on a single snapshot (webcam or upload),
    computes the same 15 joint-angle features the production client computes
    in-browser, and classifies it with the exact same MLP + rule-engine
    sanity-check pipeline the live /api/analyse_frame route serves -- so this
    demo reflects the real behavior, not a mock.
    """
    if image is None:
        return "Capture or upload a photo of a yoga pose to see a live analysis.", {}

    try:
        import mediapipe as mp
    except Exception as e:
        return f"MediaPipe unavailable in this environment ({e}).", {}

    # mediapipe is unpinned in requirements.txt, so a rebuild can silently pull
    # a newer release -- and 0.10.35 REMOVED mp.solutions.pose in favour of the
    # Tasks API. Guarding only the import would leave that as an uncaught
    # AttributeError here, turning a dependency bump into a 500 on the demo
    # page. Degrade with an explanation instead; /api/analyse_frame is
    # unaffected either way, since the production client runs MediaPipe in the
    # browser and only ever posts the 15 angles.
    mp_pose = getattr(getattr(mp, "solutions", None), "pose", None)
    if mp_pose is None:
        return (f"This demo needs the legacy mediapipe solutions API, which "
                f"was removed in mediapipe 0.10.35 (installed: "
                f"{getattr(mp, '__version__', 'unknown')}). The live app is "
                f"unaffected -- it runs MediaPipe in the browser.", {})

    with mp_pose.Pose(static_image_mode=True, model_complexity=1) as pose:
        results = pose.process(image)

    if not results.pose_landmarks:
        return "No person detected in the frame. Step back so your full body is visible.", {}

    points = np.array([[lm.x, lm.y, lm.z] for lm in results.pose_landmarks.landmark])
    angles = extract_angles_from_landmarks(points)
    angles_dict = {FEATURE_NAMES[i]: angles[i] for i in range(15)}

    # This demo runs MediaPipe server-side, so pose_world_landmarks (metric-
    # scale 3D, separately calibrated from the default normalized landmarks)
    # is directly available here -- no client round-trip needed, unlike the
    # production /api/analyse_frame path. zero_z=False keeps its genuine depth.
    world_angles_dict = None
    if results.pose_world_landmarks:
        world_points = np.array([[lm.x, lm.y, lm.z] for lm in results.pose_world_landmarks.landmark])
        world_angles = extract_angles_from_landmarks(world_points, zero_z=False)
        world_angles_dict = {FEATURE_NAMES[i]: world_angles[i] for i in range(15)}

    model, classes = get_mlp_model()
    x_tensor = torch.tensor([angles], dtype=torch.float32).to(settings.DEVICE)
    with torch.no_grad():
        pose_logits, correctness_logit, deviations_pred = model(x_tensor)
        mlp_pose = classes[pose_logits.argmax(1).item()]
        mlp_correctness = torch.sigmoid(correctness_logit).item()
        devs_deg = (deviations_pred[0].cpu().numpy() * 180.0)
        mlp_devs = {FEATURE_NAMES[i]: float(devs_deg[i]) for i in range(15)}

    # Same MLP + rule-engine (+ world-landmarks 3-way vote) pattern as
    # /api/analyse_frame's hybrid_classify.
    predicted_pose, correctness, devs = hybrid_classify(
        mlp_pose, mlp_correctness, mlp_devs, angles_dict, world_angles_dict
    )

    devs_dict = {name: round(min(180.0, max(0.0, float(val))), 1) for name, val in devs.items()}
    summary = (
        f"**Detected pose:** {predicted_pose.replace('_', ' ').title()}\n\n"
        f"**Correctness score:** {correctness * 100:.1f}%"
    )
    return summary, devs_dict


# Deliberately not named `demo`: HF's Gradio SDK runtime auto-launches any
# top-level variable called `demo` on its own, which raced with our explicit
# uvicorn.run() below and crashed the Space with "address already in use".
with gr.Blocks(title=settings.TITLE) as blocks_ui:
    gr.HTML(
        """
        <div id="asana-header">
            <h1>&#129497; AsanaAI &mdash; Smart Yoga Coach</h1>
            <p>Real-time yoga posture correction &mdash; 3-head ResMLP pose classifier + ST-GCN flow model,
            served from this Space. This page is a lightweight demo of the same backend the full app uses.</p>
        </div>
        """
    )

    with gr.Row(elem_classes=["asana-card"]):
        with gr.Column():
            cam_input = gr.Image(
                sources=["webcam", "upload"],
                type="numpy",
                label="Snapshot for pose analysis",
            )
            analyse_btn = gr.Button("Analyse Pose", variant="primary")
        with gr.Column():
            result_md = gr.Markdown("Capture or upload a photo of a yoga pose to see a live analysis.")
            deviation_json = gr.JSON(label="Per-joint deviation (degrees from target)")

    analyse_btn.click(fn=analyse_demo_image, inputs=cam_input, outputs=[result_md, deviation_json])

    gr.HTML(
        f"""
        <div id="cta-row" style="text-align:center; margin-top: 24px;">
            <a class="cta-button" href="{VERCEL_URL}" target="_blank">
                Open the full real-time app &rarr;
            </a>
        </div>
        """
    )

# ssr_mode explicitly off: HF Spaces sets GRADIO_SSR_MODE=True by default,
# which spawns a Node SSR subprocess that raced our own uvicorn bind on the
# same port and crashed the Space with "address already in use". Not needed
# for this internal demo page anyway.
app = gr.mount_gradio_app(api, blocks_ui, path="/", css=THEME_CSS, ssr_mode=False)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))

	import os
	import numpy as np
	import gradio as gr

	import torch

	from app.main import app as api
	from app.config import settings
	from app.services.hf_loader import get_mlp_model
	from app.utils.geometry import FEATURE_NAMES, extract_angles_from_landmarks
	from app.utils.rules_classifier import hybrid_classify

	# ---------------------------------------------------------------------------
	# Companion Gradio UI. HF Spaces on the Docker SDK cannot use ZeroGPU (free
	# dynamically-allocated GPU quota); the Gradio SDK can. Mounting Gradio onto
	# the existing FastAPI `app` (imported above, unchanged) keeps every /api/*
	# route the production Next.js frontend already depends on working exactly
	# as before -- this file only adds a themed demo page for anyone who opens
	# the raw Space URL directly, it does not replace the API.
	# ---------------------------------------------------------------------------

	THEME_CSS = """
	@import url('https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700;1,9..40,400&family=Lora:wght@600&display=swap');

	:root {
	--color-primary: #4f67f4;
	--color-primary-2: #06b6d4;
	--color-success: #059669;
	--color-warning: #d97706;
	--color-error: #dc2626;
	--bg-base: #f7f6f3;
	--bg-surface: #ffffff;
	--color-text: #1a1614;
	--color-text-muted: #3d3530;
	}

	.gradio-container {
	background: var(--bg-base) !important;
	font-family: 'DM Sans', 'Helvetica Neue', system-ui, sans-serif !important;
	color: var(--color-text) !important;
	}

	#asana-header {
	background: linear-gradient(135deg, #4f67f4, #06b6d4);
	border-radius: 18px;
	padding: 28px 32px;
	color: #ffffff !important;
	margin-bottom: 20px;
	}
	#asana-header h1 {
	font-family: 'Lora', Georgia, serif !important;
	font-weight: 600 !important;
	color: #ffffff !important;
	margin: 0 0 6px 0 !important;
	}
	#asana-header p { color: rgba(255,255,255,0.92) !important; margin: 0 !important; }

	.asana-card {
	background: var(--bg-surface) !important;
	border: 1px solid rgba(0,0,0,0.08) !important;
	border-radius: 14px !important;
	box-shadow: 0 4px 16px rgba(0,0,0,0.05) !important;
	}

	#cta-row a.cta-button {
	display: inline-block;
	background: linear-gradient(135deg, #4f67f4, #06b6d4);
	color: #ffffff !important;
	padding: 10px 20px;
	border-radius: 9999px;
	text-decoration: none;
	font-weight: 600;
	font-size: 0.9rem;
	}
	"""

	VERCEL_URL = "https://yoga-posture-correction-system.vercel.app"


	def analyse_demo_image(image: np.ndarray):
	"""
	Runs MediaPipe Pose server-side on a single snapshot (webcam or upload),
	computes the same 15 joint-angle features the production client computes
	in-browser, and classifies it with the exact same MLP + rule-engine
	sanity-check pipeline the live /api/analyse_frame route serves -- so this
	demo reflects the real behavior, not a mock.
	"""
	if image is None:
	return "Capture or upload a photo of a yoga pose to see a live analysis.", {}

	try:
	import mediapipe as mp
	except Exception as e:
	return f"MediaPipe unavailable in this environment ({e}).", {}

	# mediapipe is unpinned in requirements.txt, so a rebuild can silently pull
	# a newer release -- and 0.10.35 REMOVED mp.solutions.pose in favour of the
	# Tasks API. Guarding only the import would leave that as an uncaught
	# AttributeError here, turning a dependency bump into a 500 on the demo
	# page. Degrade with an explanation instead; /api/analyse_frame is
	# unaffected either way, since the production client runs MediaPipe in the
	# browser and only ever posts the 15 angles.
	mp_pose = getattr(getattr(mp, "solutions", None), "pose", None)
	if mp_pose is None:
	return (f"This demo needs the legacy mediapipe solutions API, which "
	f"was removed in mediapipe 0.10.35 (installed: "
	f"{getattr(mp, '__version__', 'unknown')}). The live app is "
	f"unaffected -- it runs MediaPipe in the browser.", {})

	with mp_pose.Pose(static_image_mode=True, model_complexity=1) as pose:
	results = pose.process(image)

	if not results.pose_landmarks:
	return "No person detected in the frame. Step back so your full body is visible.", {}

	points = np.array([[lm.x, lm.y, lm.z] for lm in results.pose_landmarks.landmark])
	angles = extract_angles_from_landmarks(points)
	angles_dict = {FEATURE_NAMES[i]: angles[i] for i in range(15)}

	# This demo runs MediaPipe server-side, so pose_world_landmarks (metric-
	# scale 3D, separately calibrated from the default normalized landmarks)
	# is directly available here -- no client round-trip needed, unlike the
	# production /api/analyse_frame path. zero_z=False keeps its genuine depth.
	world_angles_dict = None
	if results.pose_world_landmarks:
	world_points = np.array([[lm.x, lm.y, lm.z] for lm in results.pose_world_landmarks.landmark])
	world_angles = extract_angles_from_landmarks(world_points, zero_z=False)
	world_angles_dict = {FEATURE_NAMES[i]: world_angles[i] for i in range(15)}

	model, classes = get_mlp_model()
	x_tensor = torch.tensor([angles], dtype=torch.float32).to(settings.DEVICE)
	with torch.no_grad():
	pose_logits, correctness_logit, deviations_pred = model(x_tensor)
	mlp_pose = classes[pose_logits.argmax(1).item()]
	mlp_correctness = torch.sigmoid(correctness_logit).item()
	devs_deg = (deviations_pred[0].cpu().numpy() * 180.0)
	mlp_devs = {FEATURE_NAMES[i]: float(devs_deg[i]) for i in range(15)}

	# Same MLP + rule-engine (+ world-landmarks 3-way vote) pattern as
	# /api/analyse_frame's hybrid_classify.
	predicted_pose, correctness, devs = hybrid_classify(
	mlp_pose, mlp_correctness, mlp_devs, angles_dict, world_angles_dict
	)

	devs_dict = {name: round(min(180.0, max(0.0, float(val))), 1) for name, val in devs.items()}
	summary = (
	f"Detected pose: {predicted_pose.replace('_', ' ').title()}\n\n"
	f"Correctness score: {correctness * 100:.1f}%"
	)
	return summary, devs_dict


	# Deliberately not named `demo`: HF's Gradio SDK runtime auto-launches any
	# top-level variable called `demo` on its own, which raced with our explicit
	# uvicorn.run() below and crashed the Space with "address already in use".
	with gr.Blocks(title=settings.TITLE) as blocks_ui:
	gr.HTML(
	"""
	<div id="asana-header">
	<h1>🧙 AsanaAI — Smart Yoga Coach</h1>
	<p>Real-time yoga posture correction — 3-head ResMLP pose classifier + ST-GCN flow model,
	served from this Space. This page is a lightweight demo of the same backend the full app uses.</p>
	</div>
	"""
	)

	with gr.Row(elem_classes=["asana-card"]):
	with gr.Column():
	cam_input = gr.Image(
	sources=["webcam", "upload"],
	type="numpy",
	label="Snapshot for pose analysis",
	)
	analyse_btn = gr.Button("Analyse Pose", variant="primary")
	with gr.Column():
	result_md = gr.Markdown("Capture or upload a photo of a yoga pose to see a live analysis.")
	deviation_json = gr.JSON(label="Per-joint deviation (degrees from target)")

	analyse_btn.click(fn=analyse_demo_image, inputs=cam_input, outputs=[result_md, deviation_json])

	gr.HTML(
	f"""
	<div id="cta-row" style="text-align:center; margin-top: 24px;">
	<a class="cta-button" href="{VERCEL_URL}" target="_blank">
	Open the full real-time app →
	</a>
	</div>
	"""
	)

	# ssr_mode explicitly off: HF Spaces sets GRADIO_SSR_MODE=True by default,
	# which spawns a Node SSR subprocess that raced our own uvicorn bind on the
	# same port and crashed the Space with "address already in use". Not needed
	# for this internal demo page anyway.
	app = gr.mount_gradio_app(api, blocks_ui, path="/", css=THEME_CSS, ssr_mode=False)

	if __name__ == "__main__":
	import uvicorn
	uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))