Update app.py

app.py

@@ -1,246 +1,78 @@
-import gradio as gr 
-import numpy as np
-import librosa
-import librosa.display
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-import pywt
-import os
-import json
+import gradio as gr
 import random
-import tempfile
-from PIL import Image
-from tensorflow.keras.models import load_model
-from sklearn.preprocessing import StandardScaler
-
-SAMPLE_RATE = 22050
-MAX_DURATION = 5
-TIME_STEPS = 20
-USE_DENOISE = True
-
-model = load_model("Huan_luyen_6_huhong.h5")
-
-def load_scaler_from_json(filepath):
-    with open(filepath, 'r') as f:
-        data = json.load(f)
-    scaler = StandardScaler()
-    scaler.mean_ = np.array(data['mean_'])
-    scaler.scale_ = np.array(data['scale_'])
-    scaler.n_features_in_ = len(scaler.mean_)
-    return scaler
-
-scaler = load_scaler_from_json("scaler.json")
-
-with open("label_map.json", "r") as f:
-    label_map = json.load(f)
-index_to_label = {v: k for k, v in label_map.items()}
-
-def denoise_wavelet(signal, wavelet='db8', level=4):
-    coeffs = pywt.wavedec(signal, wavelet, level=level)
-    sigma = np.median(np.abs(coeffs[-1])) / 0.6745
-    uthresh = sigma * np.sqrt(2 * np.log(len(signal)))
-    coeffs_denoised = [pywt.threshold(c, value=uthresh, mode='soft') for c in coeffs]
-    return pywt.waverec(coeffs_denoised, wavelet)
-
-def create_sequences(mfcc, time_steps=20):
-    return np.array([mfcc[i:i+time_steps] for i in range(len(mfcc) - time_steps)])
-
-def cat_2s_ngau_nhien(y, sr, duration=2):
-    if len(y) < duration * sr:
-        return y
-    start = random.randint(0, len(y) - duration * sr)
-    return y[start:start + duration * sr]
-
-def tao_anh_mel(file_path):
-    y, sr = librosa.load(file_path, sr=None, mono=True)
-    y = cat_2s_ngau_nhien(y, sr)
-    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
-    S_dB = librosa.power_to_db(S, ref=np.max)
-    fig, ax = plt.subplots(figsize=(6, 3))
-    img = librosa.display.specshow(S_dB, sr=sr, x_axis='time', y_axis='mel', ax=ax, cmap='magma')
-    ax.set_title("Phổ tần Mel", fontsize=10)
-    fig.colorbar(img, ax=ax)
-    plt.tight_layout()
-    path = os.path.join(tempfile.gettempdir(), "mel.png")
-    fig.savefig(path, dpi=80)
-    plt.close()
-    return Image.open(path)
-
-def tao_wavelet_transform(file_path):
-    y, sr = librosa.load(file_path, sr=None, mono=True)
-    y = cat_2s_ngau_nhien(y, sr)
-    coef, _ = pywt.cwt(y, scales=np.arange(1, 128), wavelet='morl', sampling_period=1/sr)
-    fig, ax = plt.subplots(figsize=(6, 3))
-    ax.imshow(np.abs(coef), extent=[0, len(y)/sr, 1, 128], cmap='plasma', aspect='auto', origin='lower')
-    ax.set_title("Phổ sóng con (Wavelet)")
-    ax.set_xlabel("Thời gian (s)")
-    ax.set_ylabel("Tần số (scale)")
-    plt.tight_layout()
-    path = os.path.join(tempfile.gettempdir(), "wavelet.png")
-    fig.savefig(path, dpi=80)
-    plt.close()
-    return Image.open(path)
-
-def tao_waveform_image(file_path):
-    y, sr = librosa.load(file_path, sr=None, mono=True)
-    y = cat_2s_ngau_nhien(y, sr)
-    fig, ax = plt.subplots(figsize=(6, 2.5))
-    librosa.display.waveshow(y, sr=sr, ax=ax, color='steelblue')
-    ax.set_title("Biểu đồ Sóng Âm (Waveform)")
-    ax.set_xlabel("Thời gian (s)")
-    ax.set_ylabel("Biên độ")
-    plt.tight_layout()
-    path = os.path.join(tempfile.gettempdir(), "waveform.png")
-    fig.savefig(path, dpi=80)
-    plt.close()
-    return Image.open(path)
-
-def bao_san_sang(file_path):
-    if not file_path:
-        return ""
-    return "<b style='color:green;'>✅ Âm thanh đã sẵn sàng. Nhấn kiểm tra ngay!</b>"
-
-def sinh_anh(file_path):
-    if not file_path:
-        return None, None, None
-    mel_img = tao_anh_mel(file_path)
-    wavelet_img = tao_wavelet_transform(file_path)
-    waveform_img = tao_waveform_image(file_path)
-    return mel_img, wavelet_img, waveform_img
-
-def du_doan(file_path):
-    if not file_path:
-        return "<b style='color:red;'>❌ Chưa có âm thanh.</b>"
-
-    signal, sr = librosa.load(file_path, sr=SAMPLE_RATE, mono=True)
-    signal, _ = librosa.effects.trim(signal)
-    signal = librosa.util.fix_length(signal, size=SAMPLE_RATE * MAX_DURATION)
-
-    if USE_DENOISE:
-        signal = denoise_wavelet(signal)
-
-    mfcc = librosa.feature.mfcc(y=signal, sr=sr, n_mfcc=13).T
-    mfcc = scaler.transform(mfcc)
-    X_input = create_sequences(mfcc, time_steps=TIME_STEPS)
-
-    if len(X_input) == 0:
-        return "<b style='color:red;'>⚠️ Âm thanh quá ngắn để phân tích.</b>"
-
-    y_preds = model.predict(X_input, verbose=0)
-    avg_probs = np.mean(y_preds, axis=0)
-    pred_index = np.argmax(avg_probs)
-    confidence = avg_probs[pred_index] * 100
-    pred_label = "HƯ HỎNG KHÁC" if confidence < 60 else index_to_label[pred_index]
-
-    html = f"""<div style='background:#f0faff;color:#000;padding:10px;border-radius:10px'>
-<b style='color:#000'>📋 Kết Quả:</b><br>
-✅ <b style='color:#000'>Tình trạng:</b> <span style='color:#007acc;font-size:18px'>{pred_label.upper()}</span><br>
-📊 <b style='color:#000'>Độ tin cậy:</b> <span style='color:#000'>{confidence:.2f}%</span><br>
-<hr style='margin:6px 0'>
-<b style='color:#000'>Xác suất từng lớp:</b><br>"""
-    for i, prob in enumerate(avg_probs):
-        html += f"<span style='color:#000'>- {index_to_label[i]}: {prob*100:.1f}%</span><br>"
-    html += "</div>"
-    return html
-
-def reset_output():
-    return "", None, None, None, ""
-
-def chon_file(f1, f2):
-    return f1 if f1 else f2
-
-with gr.Blocks(css="""
-#check-btn {
-    background: #007acc;
-    color: white;
-    height: 48px;
-    font-size: 16px;
-    font-weight: bold;
-    border-radius: 10px;
-}
-""") as demo:
-
-    gr.HTML("""
-    <div style="
-        display: flex;
-        align-items: center;
-        background-image: url('https://cdn-uploads.huggingface.co/production/uploads/6881f05ad0fc87fca019ee65/t7NwSiUHpjoFXh1S10MT4.png');
-        background-repeat: no-repeat;
-        background-size: 100px 40px;
-        background-position: 0px 0px;   
-        padding-left: 60px;
-        height: 50px;
-        margin: 0;                     
-    ">
-    </div>
-    """)
-
-    gr.Markdown("""
-    <div style='
-        display: flex;
-        justify-content: center;
-        align-items: center;
-        margin-top: -10px;
-        margin-bottom: 10px;
-        height: 40px;
-    '>
-        <h4 style='color:#007acc; font-size:20px; font-weight:bold; margin: 0;'>
-                  CHẨN ĐOÁN HƯ HỎNG TỪ ÂM THANH ĐỘNG CƠ 
-        </h4>
-    </div>
-    """)
+
+# Trạng thái game
+def new_game():
+    return 0, 0, 1, "Game bắt đầu! ⚽"
+
+def shoot(direction, score, round_num):
+    directions = ["trái", "giữa", "phải"]
+    keeper = random.choice(directions)
+
+    if direction == keeper:
+        result = f"❌ Bị cản! (Thủ môn: {keeper})"
+    else:
+        score += 1
+        result = f"⚽ GOAL!!! (Thủ môn: {keeper})"
+
+    round_num += 1
+
+    if round_num > 5:
+        result += f"\n\n🏁 Kết thúc! Bạn ghi {score}/5 bàn"
+    
+    return score, round_num, result
+
+# UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # ⚽ Penalty Game
+        ### Sút 5 quả - ghi càng nhiều bàn càng tốt!
+        """
+    )
+
+    score = gr.State(0)
+    round_num = gr.State(1)
+
+    score_text = gr.Markdown("**Điểm: 0 | Lượt: 1/5**")
+    result = gr.Markdown("Chọn hướng sút 👇")
 
     with gr.Row():
-        audio_file = gr.Audio(type="filepath", label="📂 Tải File Âm Thanh", interactive=True)
-        audio_mic = gr.Audio(type="filepath", label="🎤 Ghi Âm", sources=["microphone"], interactive=True)
-
-    thong_bao_ready = gr.HTML()
-    btn_check = gr.Button("🔍 KIỂM TRA NGAY", elem_id="check-btn")
-    output_html = gr.HTML()
-
-    with gr.Accordion("📊 Phân tích Âm Thanh", open=False):
-        mel_output = gr.Image(label="")
-        wavelet_output = gr.Image(label="")
-        waveform_output = gr.Image(label="")
-
-    def xu_ly_toan_bo(file_path):
-        tb = bao_san_sang(file_path)
-        mel, wavl, wave = sinh_anh(file_path)
-        kq = du_doan(file_path)
-        return tb, mel, wavl, wave, kq
-
-    audio_file.change(
-        fn=xu_ly_toan_bo,
-        inputs=audio_file,
-        outputs=[thong_bao_ready, mel_output, wavelet_output, waveform_output, output_html]
+        btn_left = gr.Button("⬅️ Trái")
+        btn_center = gr.Button("⬆️ Giữa")
+        btn_right = gr.Button("➡️ Phải")
+
+    btn_restart = gr.Button("🔄 Chơi lại")
+
+    def update_ui(score, round_num, message):
+        return (
+            score,
+            round_num,
+            f"**Điểm: {score} | Lượt: {min(round_num,5)}/5**",
+            message
+        )
+
+    btn_left.click(
+        lambda s, r: update_ui(*shoot("trái", s, r)),
+        inputs=[score, round_num],
+        outputs=[score, round_num, score_text, result]
     )
 
-    audio_mic.change(
-        fn=xu_ly_toan_bo,
-        inputs=audio_mic,
-        outputs=[thong_bao_ready, mel_output, wavelet_output, waveform_output, output_html]
+    btn_center.click(
+        lambda s, r: update_ui(*shoot("giữa", s, r)),
+        inputs=[score, round_num],
+        outputs=[score, round_num, score_text, result]
     )
 
-    btn_check.click(
-        fn=lambda f1, f2: du_doan(chon_file(f1, f2)),
-        inputs=[audio_file, audio_mic],
-        outputs=output_html
+    btn_right.click(
+        lambda s, r: update_ui(*shoot("phải", s, r)),
+        inputs=[score, round_num],
+        outputs=[score, round_num, score_text, result]
     )
 
-    audio_file.clear(fn=reset_output, outputs=[
-        thong_bao_ready,
-        mel_output,
-        wavelet_output,
-        waveform_output,
-        output_html
-    ])
-    audio_mic.clear(fn=reset_output, outputs=[
-        thong_bao_ready,
-        mel_output,
-        wavelet_output,
-        waveform_output,
-        output_html
-    ])
+    btn_restart.click(
+        lambda: (0, 1, "**Điểm: 0 | Lượt: 1/5**", "Game mới! ⚽"),
+        outputs=[score, round_num, score_text, result]
+    )
 
 demo.launch()