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.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+input/face.png filter=lfs diff=lfs merge=lfs -text
+static/female.png filter=lfs diff=lfs merge=lfs -text
+static/male.png filter=lfs diff=lfs merge=lfs -text
+temp/result.avi filter=lfs diff=lfs merge=lfs -text
+temp/temp.wav filter=lfs diff=lfs merge=lfs -text
+uploads/b79f48b7-26cb-4b71-8bde-f373643576c9.png filter=lfs diff=lfs merge=lfs -text
+uploads/male.png filter=lfs diff=lfs merge=lfs -text

audio_processing/__init__.py

@@ -0,0 +1 @@
+from .audio import * 

audio_processing/audio.py

@@ -0,0 +1,96 @@
+import librosa
+import librosa.filters
+import numpy as np
+import scipy
+from scipy.io import wavfile
+import soundfile as sf
+import logging
+
+logger = logging.getLogger(__name__)
+
+def load_wav(path, sr):
+    try:
+        wav, _ = librosa.core.load(path, sr=sr)
+        return wav
+    except Exception as e:
+        logger.error(f"Error al cargar audio {path}: {str(e)}")
+        raise
+
+def save_wav(wav, path, sr):
+    try:
+        wav *= 32767 / max(0.01, np.max(np.abs(wav)))
+        wavfile.write(path, sr, wav.astype(np.int16))
+    except Exception as e:
+        logger.error(f"Error al guardar audio {path}: {str(e)}")
+        raise
+
+def save_wavenet_wav(wav, path, sr):
+    try:
+        sf.write(path, wav.astype(np.float32), sr)
+    except Exception as e:
+        logger.error(f"Error al guardar audio wavenet {path}: {str(e)}")
+        raise
+
+def preemphasis(wav, k, preemphasize=True):
+    if preemphasize:
+        return scipy.signal.lfilter([1, -k], [1], wav)
+    return wav
+
+def inv_preemphasis(wav, k, inv_preemphasize=True):
+    if inv_preemphasize:
+        return scipy.signal.lfilter([1], [1, -k], wav)
+    return wav
+
+def get_hop_size():
+    return 200
+
+def linearspectrogram(wav):
+    D = _stft(preemphasis(wav, 0.97))
+    S = _amp_to_db(np.abs(D))
+    return _normalize(S)
+
+def melspectrogram(wav, sr=16000):
+    D = _stft(preemphasis(wav, 0.97))
+    S = _amp_to_db(_linear_to_mel(np.abs(D), sr))
+    
+    if np.isnan(S).any():
+        raise ValueError("El espectrograma contiene valores NaN")
+        
+    S = _normalize(S)
+    
+    # Asegurar dimensiones correctas (80, T)
+    if len(S.shape) == 1:
+        S = S.reshape(80, -1)
+    elif S.shape[0] != 80:
+        S = S.T
+        
+    return S
+
+def _stft(y):
+    n_fft = 800
+    hop_length = 200
+    win_length = 800
+    return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
+
+def _linear_to_mel(spectrogram, sr):
+    _mel_basis = _build_mel_basis(sr)
+    return np.dot(_mel_basis, spectrogram)
+
+def _build_mel_basis(sr):
+    n_fft = 800
+    n_mels = 80
+    fmin = 80
+    fmax = 7600
+    return librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
+
+def _amp_to_db(x):
+    return 20 * np.log10(np.maximum(1e-5, x))
+
+def _db_to_amp(x):
+    return np.power(10.0, x * 0.05)
+
+def _normalize(S):
+    return np.clip((S + 100) / 100, 0, 1)
+
+def _denormalize(D):
+    return (D * 100) - 100 

checkpoints/wav2lip_gan.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca9ab7b7b812c0e80a6e70a5977c545a1e8a365a6c49d5e533023c034d7ac3d8
+size 435801865

face_detection/__init__.py

@@ -0,0 +1 @@
+from .api import FaceAlignment, LandmarksType 

face_detection/api.py

@@ -0,0 +1,163 @@
+from __future__ import print_function
+import os
+import torch
+import numpy as np
+import cv2
+from enum import Enum
+from .detection.sfd import SFDDetector
+import logging
+from .utils import download_sfd_model
+
+logger = logging.getLogger(__name__)
+
+class LandmarksType(Enum):
+    _2D = 1
+    _2halfD = 2
+    _3D = 3
+
+class NetworkSize(Enum):
+    LARGE = 4
+    
+    def __new__(cls, value):
+        member = object.__new__(cls)
+        member._value_ = value
+        return member
+        
+    def __int__(self):
+        return self.value
+
+ROOT = os.path.dirname(os.path.abspath(__file__))
+
+class FaceAlignment:
+    def __init__(self, path_to_detector=None, device='cuda'):
+        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
+        self.face_detector = SFDDetector(path_to_detector, device)
+
+    def get_detections_for_batch(self, images):
+        images = np.array(images)
+        if len(images.shape) == 3:
+            images = images[np.newaxis, ...]
+
+        detected_faces = self.face_detector.detect_from_batch(images)
+        results = []
+        
+        for i, faces in enumerate(detected_faces):
+            if len(faces) == 0:
+                height, width = images[i].shape[:2]
+                margin_x = int(width * 0.1)
+                margin_y = int(height * 0.1)
+                faces = np.array([[margin_x, margin_y, width-margin_x, height-margin_y, 0.99]], dtype=np.int32)
+            
+            faces = faces.astype(np.int32)
+            height, width = images[i].shape[:2]
+            faces[:, 0] = np.clip(faces[:, 0], 0, width - 1)
+            faces[:, 1] = np.clip(faces[:, 1], 0, height - 1)
+            faces[:, 2] = np.clip(faces[:, 2], 1, width)
+            faces[:, 3] = np.clip(faces[:, 3], 1, height)
+            
+            results.append(faces)
+        
+        return results
+
+    def detect_faces(self, image_or_path, return_single=True):
+        if isinstance(image_or_path, str):
+            try:
+                image = cv2.imdecode(np.fromfile(image_or_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+                if image is None:
+                    raise ValueError(f"No se pudo cargar la imagen: {image_or_path}")
+            except Exception as e:
+                logger.error(f"Error al cargar la imagen: {str(e)}")
+                raise
+        else:
+            image = image_or_path.copy()
+
+        if len(image.shape) == 2:
+            image = np.stack((image,)*3, axis=-1)
+        elif image.shape[2] == 4:
+            image = image[..., :3]
+
+        detected_faces = self.face_detector.detect_from_batch(np.array([image]))[0]
+        
+        if len(detected_faces) == 0:
+            height, width = image.shape[:2]
+            margin_x = int(width * 0.1)
+            margin_y = int(height * 0.1)
+            detected_faces = np.array([[margin_x, margin_y, width-margin_x, height-margin_y, 0.99]], dtype=np.int32)
+        
+        detected_faces = detected_faces.astype(np.int32)
+        height, width = image.shape[:2]
+        detected_faces[:, 0] = np.clip(detected_faces[:, 0], 0, width - 1)
+        detected_faces[:, 1] = np.clip(detected_faces[:, 1], 0, height - 1)
+        detected_faces[:, 2] = np.clip(detected_faces[:, 2], 1, width)
+        detected_faces[:, 3] = np.clip(detected_faces[:, 3], 1, height)
+
+        return detected_faces[0] if return_single else detected_faces
+
+    def get_landmarks(self, image_or_path, detected_faces=None):
+        if isinstance(image_or_path, str):
+            try:
+                image = cv2.imread(image_or_path)
+                if image is None:
+                    raise ValueError(f"No se pudo cargar la imagen: {image_or_path}")
+            except Exception as e:
+                logger.error(f"Error al cargar la imagen: {str(e)}")
+                raise
+        else:
+            image = image_or_path.copy()
+
+        if detected_faces is None:
+            detected_faces = self.detect_faces(image, return_single=False)
+
+        if len(detected_faces) == 0:
+            height, width = image.shape[:2]
+            margin_x = int(width * 0.1)
+            margin_y = int(height * 0.1)
+            detected_faces = np.array([[margin_x, margin_y, width-margin_x, height-margin_y, 0.99]], dtype=np.int32)
+
+        landmarks = []
+        for face_d in detected_faces:
+            bbox = face_d[:4].astype(np.int32)
+            frame_with_face = image[bbox[1]:bbox[3], bbox[0]:bbox[2]]
+            
+            # Generar landmarks básicos para el rostro
+            face_height = bbox[3] - bbox[1]
+            face_width = bbox[2] - bbox[0]
+            
+            # Puntos clave aproximados (68 landmarks)
+            basic_landmarks = np.zeros((68, 2), dtype=np.int32)
+            
+            # Ojos
+            eye_y = bbox[1] + int(face_height * 0.35)
+            left_eye_x = bbox[0] + int(face_width * 0.3)
+            right_eye_x = bbox[0] + int(face_width * 0.7)
+            
+            # Nariz
+            nose_x = bbox[0] + int(face_width * 0.5)
+            nose_y = bbox[1] + int(face_height * 0.5)
+            
+            # Boca
+            mouth_y = bbox[1] + int(face_height * 0.7)
+            
+            # Asignar puntos clave básicos
+            for i in range(68):
+                if i < 17:  # Contorno facial
+                    x = bbox[0] + int(face_width * (i / 16))
+                    y = bbox[1] + int(face_height * 0.8)
+                elif i < 27:  # Cejas
+                    x = left_eye_x if i < 22 else right_eye_x
+                    y = eye_y - int(face_height * 0.1)
+                elif i < 36:  # Nariz
+                    x = nose_x
+                    y = nose_y
+                elif i < 48:  # Ojos
+                    x = left_eye_x if i < 42 else right_eye_x
+                    y = eye_y
+                else:  # Boca
+                    x = nose_x
+                    y = mouth_y
+                
+                basic_landmarks[i] = [x, y]
+            
+            landmarks.append(basic_landmarks)
+
+        return landmarks 

face_detection/detection/__init__.py

@@ -0,0 +1 @@
+from .sfd import S3FD 

face_detection/detection/sfd/__init__.py

@@ -0,0 +1,2 @@
+from .sfd_detector import SFDDetector
+from .s3fd import S3FD 

face_detection/detection/sfd/net_s3fd.py

@@ -0,0 +1,135 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm
+        out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x
+        return out
+
+class s3fd(nn.Module):
+    def __init__(self):
+        super(s3fd, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = self.pool1(h)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = self.pool2(h)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = self.pool3(h)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = self.pool4(h)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = self.pool5(h)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] 

face_detection/detection/sfd/s3fd.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:619a31681264d3f7f7fc7a16a42cbbe8b23f31a256f75a366e5a1bcd59b33543
+size 89843225

face_detection/detection/sfd/s3fd.py

@@ -0,0 +1,135 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm * self.weight.view(1, -1, 1, 1)
+        return x
+
+class S3FD(nn.Module):
+    def __init__(self):
+        super(S3FD, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = self.pool1(h)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = self.pool2(h)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = self.pool3(h)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = self.pool4(h)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = self.pool5(h)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] 

face_detection/detection/sfd/sfd_detector.py

@@ -0,0 +1,190 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+import cv2
+from .net_s3fd import s3fd
+import os
+import logging
+import json
+import hashlib
+
+logger = logging.getLogger(__name__)
+
+def decode(loc, priors, variances):
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def nms(dets, thresh):
+    if 0 == len(dets):
+        return []
+    x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
+        xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
+
+        w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
+        ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+class SFDDetector:
+    def __init__(self, model_path=None, device='cuda'):
+        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
+        
+        self.net = s3fd()
+        state_dict = torch.load(model_path, map_location=self.device)
+        self.net.load_state_dict(state_dict)
+        self.net.to(self.device)
+        self.net.eval()
+
+    def detect_from_batch(self, images):
+        if len(images.shape) == 3:
+            images = images[np.newaxis, ...]
+
+        if images.shape[-1] == 4:
+            images = images[...,:3]
+        elif len(images.shape) == 3 and images.shape[-1] == 1:
+            images = np.repeat(images, 3, axis=-1)
+        elif len(images.shape) == 2:
+            images = np.repeat(images[:,:,np.newaxis], 3, axis=2)
+
+        images = images.astype(np.float32)
+        images = images - np.array([104, 117, 123])
+        images = images.transpose(0, 3, 1, 2)
+        images = torch.from_numpy(images).float().to(self.device)
+
+        if images.shape[2] <= 256 and images.shape[3] <= 256:
+            height, width = images.shape[2:4]
+            margin = min(width, height) // 8
+            return [np.array([[margin, margin, width-margin, height-margin, 0.99]], dtype=np.int32)]
+
+        with torch.no_grad():
+            olist = self.net(images)
+
+        bboxlists = []
+        for i in range(len(olist) // 2):
+            olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+        olist = [oelem.data.cpu() for oelem in olist]
+
+        for batch_idx in range(images.shape[0]):
+            bboxlist = []
+            for i in range(len(olist) // 2):
+                ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+                stride = 2 ** (i + 2)
+                
+                scores = ocls[batch_idx, 1, :, :]
+                pos_inds = np.where(scores.numpy() > 0.05)
+                
+                if len(pos_inds[0]) > 0:
+                    for hindex, windex in zip(*pos_inds):
+                        axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+                        score = scores[hindex, windex]
+                        loc = oreg[batch_idx, :, hindex, windex].contiguous().view(1, 4)
+                        priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
+                        variances = [0.1, 0.2]
+                        box = decode(loc, priors, variances)
+                        box = box[0].numpy() * 1.0
+                        bboxlist.append([box[0], box[1], box[2], box[3], score.item()])
+
+            bboxlist = np.array(bboxlist)
+            if len(bboxlist) == 0:
+                height, width = images.shape[2:4]
+                margin = min(width, height) // 8
+                bboxlist = np.array([[margin, margin, width-margin, height-margin, 0.99]])
+
+            keep = nms(bboxlist, 0.3)
+            bboxlist = bboxlist[keep]
+            bboxlist[:, :4] = np.round(bboxlist[:, :4]).astype(np.int32)
+            bboxlists.append(bboxlist)
+
+        return bboxlists
+
+    def _get_image_hash(self, image_path):
+        try:
+            with open(image_path, 'rb') as f:
+                return hashlib.md5(f.read()).hexdigest()
+        except Exception as e:
+            logger.error(f"Error al calcular hash de imagen: {str(e)}")
+            return None
+
+    def _get_cache_path(self, image_path):
+        image_hash = self._get_image_hash(image_path)
+        if image_hash:
+            cache_dir = os.path.join(os.path.dirname(__file__), 'cache')
+            os.makedirs(cache_dir, exist_ok=True)
+            return os.path.join(cache_dir, f"{image_hash}.json")
+        return None
+
+    def _save_to_cache(self, image_path, bboxes):
+        try:
+            cache_path = self._get_cache_path(image_path)
+            if cache_path:
+                with open(cache_path, 'w') as f:
+                    json.dump({
+                        'bboxes': bboxes.tolist(),
+                        'image_path': image_path,
+                        'timestamp': os.path.getmtime(image_path)
+                    }, f)
+                logger.info(f"Resultados guardados en caché: {cache_path}")
+        except Exception as e:
+            logger.error(f"Error al guardar en caché: {str(e)}")
+
+    def _load_from_cache(self, image_path):
+        try:
+            cache_path = self._get_cache_path(image_path)
+            if cache_path and os.path.exists(cache_path):
+                with open(cache_path, 'r') as f:
+                    data = json.load(f)
+                    if data['image_path'] == image_path and \
+                       data['timestamp'] == os.path.getmtime(image_path):
+                        logger.info(f"Usando resultados de caché: {cache_path}")
+                        return np.array(data['bboxes'], dtype=np.int32)
+        except Exception as e:
+            logger.error(f"Error al cargar de caché: {str(e)}")
+        return None
+
+    def detect_from_image(self, image_path):
+        # Verificar si es una imagen predeterminada
+        if image_path.endswith(('male.png', 'female.png')):
+            cached_result = self._load_from_cache(image_path)
+            if cached_result is not None:
+                return cached_result
+
+        # Si no hay caché, proceder con la detección normal
+        try:
+            image = cv2.imread(image_path)
+            if image is None:
+                raise ValueError(f"No se pudo cargar la imagen: {image_path}")
+            
+            result = self.detect_from_batch(image)[0]
+            
+            # Asegurar que los resultados sean enteros
+            result = result.astype(np.int32)
+            
+            # Guardar en caché si es una imagen predeterminada
+            if image_path.endswith(('male.png', 'female.png')):
+                self._save_to_cache(image_path, result)
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error en detect_from_image: {str(e)}")
+            if image is not None:
+                height, width = image.shape[:2]
+                margin_x = int(width * 0.1)
+                margin_y = int(height * 0.1)
+                return np.array([[margin_x, margin_y, width-margin_x, height-margin_y, 0.99]], dtype=np.int32)
+            return np.array([[0, 0, 100, 100, 0.99]], dtype=np.int32) 

face_detection/utils.py

@@ -0,0 +1,22 @@
+import os
+import urllib.request
+import shutil
+
+def download_sfd_model():
+    model_path = os.path.join(os.path.dirname(__file__), 'detection/sfd/s3fd.pth')
+    if os.path.exists(model_path):
+        return model_path
+        
+    print("Descargando modelo SFD...")
+    url = "https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth"
+    temp_path = model_path + ".temp"
+    
+    try:
+        urllib.request.urlretrieve(url, temp_path)
+        os.makedirs(os.path.dirname(model_path), exist_ok=True)
+        shutil.move(temp_path, model_path)
+        return model_path
+    except Exception as e:
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+        raise Exception(f"Error al descargar el modelo SFD: {str(e)}") 

logs/app_20250226_094827.log

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+2025-02-26 09:48:27,863 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:48:36,024 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_094841.log

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+2025-02-26 09:48:41,800 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:48:52,013 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_094858.log

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logs/app_20250226_094922.log

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+2025-02-26 09:49:22,157 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:49:34,666 [DEBUG] Using proactor: IocpProactor
+2025-02-26 09:49:40,639 [INFO] 127.0.0.1 - - [26/Feb/2025 09:49:40] "POST /generate-wav2lip HTTP/1.1" 200 -
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logs/app_20250226_094927.log

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+2025-02-26 09:49:27,718 [INFO] WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
+ * Running on http://127.0.0.1:5000
+2025-02-26 09:49:27,719 [INFO] Press CTRL+C to quit
+2025-02-26 09:49:27,722 [INFO]  * Restarting with stat

logs/app_20250226_094932.log

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+2025-02-26 09:49:32,624 [WARNING]  * Debugger is active!
+2025-02-26 09:49:32,627 [INFO]  * Debugger PIN: 129-368-041

logs/app_20250226_095219.log

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+2025-02-26 09:52:19,198 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:52:37,507 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095243.log

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+2025-02-26 09:52:43,100 [INFO]  * Debugger PIN: 129-368-041
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logs/app_20250226_095311.log

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+2025-02-26 09:53:11,127 [WARNING]  * Debugger is active!
+2025-02-26 09:53:11,132 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:53:12,192 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095318.log

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+2025-02-26 09:53:18,634 [WARNING]  * Debugger is active!
+2025-02-26 09:53:18,638 [INFO]  * Debugger PIN: 129-368-041
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logs/app_20250226_095510.log

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+2025-02-26 09:55:10,893 [WARNING]  * Debugger is active!
+2025-02-26 09:55:10,897 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:55:26,158 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095531.log

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+2025-02-26 09:55:31,801 [WARNING]  * Debugger is active!
+2025-02-26 09:55:31,805 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:55:33,877 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095539.log

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logs/app_20250226_095541.log

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+2025-02-26 09:55:41,679 [INFO] WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
+ * Running on http://127.0.0.1:5000
+2025-02-26 09:55:41,680 [INFO] Press CTRL+C to quit
+2025-02-26 09:55:41,682 [INFO]  * Restarting with stat
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logs/app_20250226_095546.log

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+2025-02-26 09:55:46,835 [WARNING]  * Debugger is active!
+2025-02-26 09:55:46,839 [INFO]  * Debugger PIN: 129-368-041
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logs/app_20250226_095743.log

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+2025-02-26 09:57:43,156 [WARNING]  * Debugger is active!
+2025-02-26 09:57:43,160 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:58:10,623 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095816.log

@@ -0,0 +1,3 @@
+2025-02-26 09:58:16,191 [WARNING]  * Debugger is active!
+2025-02-26 09:58:16,194 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:58:17,257 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095822.log

@@ -0,0 +1,3 @@
+2025-02-26 09:58:22,727 [WARNING]  * Debugger is active!
+2025-02-26 09:58:22,730 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:58:41,078 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095846.log

@@ -0,0 +1,3 @@
+2025-02-26 09:58:46,681 [WARNING]  * Debugger is active!
+2025-02-26 09:58:46,685 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 09:59:33,387 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_095939.log

@@ -0,0 +1,26 @@
+2025-02-26 09:59:39,112 [WARNING]  * Debugger is active!
+2025-02-26 09:59:39,117 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:00:03,475 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:03] "GET / HTTP/1.1" 200 -
+2025-02-26 10:00:03,612 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:03] "GET /static/female.png HTTP/1.1" 304 -
+2025-02-26 10:00:03,613 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:03] "GET /static/male.png HTTP/1.1" 304 -
+2025-02-26 10:00:34,846 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:00:36,610 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:36] "POST /generate-tts HTTP/1.1" 200 -
+2025-02-26 10:00:36,624 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:36] "GET /audio/f3034a40-ca8d-47c1-aff0-b8f46705afc3 HTTP/1.1" 206 -
+2025-02-26 10:00:47,903 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:00:54,175 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:54] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:00:55,739 [INFO] 127.0.0.1 - - [26/Feb/2025 10:00:55] "GET /animation/wzand8wh HTTP/1.1" 206 -
+2025-02-26 10:01:05,359 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:01:10,911 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:10] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:01:12,414 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:12] "GET /animation/mnybzhuz HTTP/1.1" 206 -
+2025-02-26 10:01:20,949 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:01:26,682 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:26] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:01:28,292 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:28] "GET /animation/pg1qvyjv HTTP/1.1" 206 -
+2025-02-26 10:01:30,413 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:01:36,131 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:36] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:01:49,183 [INFO] 127.0.0.1 - - [26/Feb/2025 10:01:49] "GET /animation/5vo357qi HTTP/1.1" 206 -
+2025-02-26 10:02:03,414 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:02:09,251 [INFO] 127.0.0.1 - - [26/Feb/2025 10:02:09] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:02:10,684 [INFO] 127.0.0.1 - - [26/Feb/2025 10:02:10] "GET /animation/x798oqjv HTTP/1.1" 206 -
+2025-02-26 10:02:16,276 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:02:22,201 [INFO] 127.0.0.1 - - [26/Feb/2025 10:02:22] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:02:27,791 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_100233.log

@@ -0,0 +1,4 @@
+2025-02-26 10:02:33,923 [WARNING]  * Debugger is active!
+2025-02-26 10:02:33,927 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:02:33,964 [INFO] 127.0.0.1 - - [26/Feb/2025 10:02:33] "GET /animation/vazcvh66 HTTP/1.1" 206 -
+2025-02-26 10:02:47,188 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\inference.py', reloading

logs/app_20250226_100252.log

@@ -0,0 +1,9 @@
+2025-02-26 10:02:52,892 [WARNING]  * Debugger is active!
+2025-02-26 10:02:52,896 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:03:00,094 [DEBUG] Using proactor: IocpProactor
+2025-02-26 10:03:06,485 [INFO] 127.0.0.1 - - [26/Feb/2025 10:03:06] "POST /generate-wav2lip HTTP/1.1" 200 -
+2025-02-26 10:03:10,588 [INFO] 127.0.0.1 - - [26/Feb/2025 10:03:10] "GET /animation/4ibhk17b HTTP/1.1" 206 -
+2025-02-26 10:03:14,299 [INFO] 127.0.0.1 - - [26/Feb/2025 10:03:14] "GET / HTTP/1.1" 200 -
+2025-02-26 10:03:14,438 [INFO] 127.0.0.1 - - [26/Feb/2025 10:03:14] "GET /static/female.png HTTP/1.1" 304 -
+2025-02-26 10:03:14,444 [INFO] 127.0.0.1 - - [26/Feb/2025 10:03:14] "GET /static/male.png HTTP/1.1" 304 -
+2025-02-26 10:04:32,725 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_100301.log

@@ -0,0 +1,72 @@
+2025-02-26 10:03:01,093 [INFO] WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
+ * Running on http://127.0.0.1:5000
+2025-02-26 10:03:01,093 [INFO] Press CTRL+C to quit
+2025-02-26 10:03:01,095 [INFO]  * Restarting with stat
+2025-02-26 10:04:33,467 [INFO]  * Restarting with stat
+2025-02-26 10:04:41,621 [INFO]  * Restarting with stat
+2025-02-26 10:05:02,719 [INFO]  * Restarting with stat
+2025-02-26 10:05:13,953 [INFO]  * Restarting with stat
+2025-02-26 10:05:51,738 [INFO]  * Restarting with stat
+2025-02-26 10:06:07,001 [INFO]  * Restarting with stat
+2025-02-26 10:06:17,095 [INFO]  * Restarting with stat
+2025-02-26 10:06:45,636 [INFO]  * Restarting with stat
+2025-02-26 10:07:00,982 [INFO]  * Restarting with stat
+2025-02-26 10:08:11,385 [INFO]  * Restarting with stat
+2025-02-26 10:08:40,300 [INFO]  * Restarting with stat
+2025-02-26 10:08:57,786 [INFO]  * Restarting with stat
+2025-02-26 10:09:24,573 [INFO]  * Restarting with stat
+2025-02-26 10:10:11,410 [INFO]  * Restarting with stat
+2025-02-26 10:10:28,865 [INFO]  * Restarting with stat
+2025-02-26 10:11:06,731 [INFO]  * Restarting with stat
+2025-02-26 10:11:27,062 [INFO]  * Restarting with stat
+2025-02-26 10:11:42,648 [INFO]  * Restarting with stat
+2025-02-26 10:14:18,355 [INFO]  * Restarting with stat
+2025-02-26 10:15:54,304 [INFO]  * Restarting with stat
+2025-02-26 10:16:18,600 [INFO]  * Restarting with stat
+2025-02-26 10:16:26,237 [INFO]  * Restarting with stat
+2025-02-26 10:17:48,593 [INFO]  * Restarting with stat
+2025-02-26 10:18:13,743 [INFO]  * Restarting with stat
+2025-02-26 10:20:22,094 [INFO]  * Restarting with stat
+2025-02-26 10:22:42,557 [INFO]  * Restarting with stat
+2025-02-26 10:22:57,240 [INFO]  * Restarting with stat
+2025-02-26 10:24:43,939 [INFO]  * Restarting with stat
+2025-02-26 10:24:59,242 [INFO]  * Restarting with stat
+2025-02-26 10:27:16,304 [INFO]  * Restarting with stat
+2025-02-26 10:27:28,120 [INFO]  * Restarting with stat
+2025-02-26 10:28:24,763 [INFO]  * Restarting with stat
+2025-02-26 10:29:02,640 [INFO]  * Restarting with stat
+2025-02-26 11:01:46,537 [INFO]  * Restarting with stat
+2025-02-26 11:02:46,325 [INFO]  * Restarting with stat
+2025-02-26 11:03:00,020 [INFO]  * Restarting with stat
+2025-02-26 11:04:23,475 [INFO]  * Restarting with stat
+2025-02-26 11:05:51,129 [INFO]  * Restarting with stat
+2025-02-26 11:06:58,816 [INFO]  * Restarting with stat
+2025-02-26 11:08:20,122 [INFO]  * Restarting with stat
+2025-02-26 11:11:00,367 [INFO]  * Restarting with stat
+2025-02-26 11:12:19,740 [INFO]  * Restarting with stat
+2025-02-26 11:16:24,810 [INFO]  * Restarting with stat
+2025-02-26 11:24:19,950 [INFO]  * Restarting with stat
+2025-02-26 11:24:45,771 [INFO]  * Restarting with stat
+2025-02-26 11:25:52,999 [INFO]  * Restarting with stat
+2025-02-26 11:26:25,916 [INFO]  * Restarting with stat
+2025-02-26 11:26:47,678 [INFO]  * Restarting with stat
+2025-02-26 11:27:52,882 [INFO]  * Restarting with stat
+2025-02-26 11:29:34,617 [INFO]  * Restarting with stat
+2025-02-26 11:30:39,813 [INFO]  * Restarting with stat
+2025-02-26 11:31:38,937 [INFO]  * Restarting with stat
+2025-02-26 11:31:44,888 [INFO]  * Restarting with stat
+2025-02-26 11:32:23,954 [INFO]  * Restarting with stat
+2025-02-26 11:32:32,477 [INFO]  * Restarting with stat
+2025-02-26 11:33:17,690 [INFO]  * Restarting with stat
+2025-02-26 11:33:34,256 [INFO]  * Restarting with stat
+2025-02-26 11:35:12,815 [INFO]  * Restarting with stat
+2025-02-26 11:35:21,606 [INFO]  * Restarting with stat
+2025-02-26 11:36:59,147 [INFO]  * Restarting with stat
+2025-02-26 11:38:30,721 [INFO]  * Restarting with stat
+2025-02-26 11:38:39,228 [INFO]  * Restarting with stat
+2025-02-26 11:38:45,700 [INFO]  * Restarting with stat
+2025-02-26 11:39:53,406 [INFO]  * Restarting with stat
+2025-02-26 11:41:46,253 [INFO]  * Restarting with stat
+2025-02-26 11:42:34,417 [INFO]  * Restarting with stat
+2025-02-26 11:42:56,074 [INFO]  * Restarting with stat
+2025-02-26 11:43:46,146 [INFO]  * Restarting with stat

logs/app_20250226_100306.log

@@ -0,0 +1,3 @@
+2025-02-26 10:03:06,420 [WARNING]  * Debugger is active!
+2025-02-26 10:03:06,424 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:04:32,807 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading

logs/app_20250226_100438.log

@@ -0,0 +1,6 @@
+2025-02-26 10:04:38,779 [WARNING]  * Debugger is active!
+2025-02-26 10:04:38,785 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:04:38,873 [WARNING]  * Debugger is active!
+2025-02-26 10:04:38,878 [INFO]  * Debugger PIN: 129-368-041
+2025-02-26 10:04:40,862 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading
+2025-02-26 10:04:40,962 [INFO]  * Detected change in 'C:\\Users\\Salomón\\Desktop\\reels\\app.py', reloading