Delete app.py

app.py

@@ -1,163 +0,0 @@
-import gradio as gr
-import cv2
-import os
-import zipfile
-from PIL import Image, ImageOps
-from datetime import datetime
-import hashlib
-
-def procesar_video(video):
-    try:
-        video_path = video
-        original_name = os.path.basename(video_path)
-
-        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-        temp_dir = f"temp_{datetime.now().strftime('%Y%m%d%H%M%S')}"
-        os.makedirs(temp_dir, exist_ok=True)
-
-        # Extracción de todos los fotogramas
-        from moviepy.editor import VideoFileClip
-        clip = VideoFileClip(video_path)
-        frame_count = int(clip.fps * clip.duration)
-        frame_paths = []
-        for i, frame in enumerate(clip.iter_frames()):
-            img = Image.fromarray(frame)
-            img_path = os.path.join(temp_dir, f"frame_{i:04d}.jpg")
-            img.save(img_path)
-            frame_paths.append(img_path)
-        clip.close()
-
-        if frame_count == 0:
-            raise gr.Error("No se pudieron extraer fotogramas del video")
-
-        # Selección estratégica de 4 fotogramas equidistantes
-        n_seleccion = 4
-        step = max(1, frame_count // (n_seleccion + 1))
-        selected_indices = [step * (i+1) for i in range(n_seleccion)]
-        selected_frames = [frame_paths[min(i, len(frame_paths)-1)] for i in selected_indices]
-
-        # Creación de collage profesional
-        images = []
-        for img_path in selected_frames:
-            img = Image.open(img_path)
-            bordered_img = ImageOps.expand(img, border=2, fill='white')  # Borde blanco
-            images.append(bordered_img)
-
-        # Configuración del diseño
-        img_w, img_h = images[0].size
-        margin = 30
-        border_size = 20
-        shadow_offset = 5
-        
-        collage_width = (img_w * 2) + margin + (border_size * 2)
-        collage_height = (img_h * 2) + margin + (border_size * 2)
-        
-        collage = Image.new('RGB', 
-            (collage_width, collage_height), 
-            (230, 230, 230))  # Fondo gris claro
-        
-        # Posiciones con efecto de profundidad
-        positions = [
-            (border_size, border_size),
-            (border_size + img_w + margin, border_size),
-            (border_size, border_size + img_h + margin),
-            (border_size + img_w + margin, border_size + img_h + margin)
-        ]
-        
-        # Pegar imágenes con sombra
-        for i, img in enumerate(images):
-            # Sombra
-            shadow = Image.new('RGBA', (img_w + shadow_offset, img_h + shadow_offset), (0,0,0,50))
-            collage.paste(shadow, (positions[i][0]+shadow_offset, positions[i][1]+shadow_offset), shadow)
-            
-            # Imagen principal
-            collage.paste(img, positions[i])
-
-        collage_path = os.path.join(temp_dir, "collage_forense.jpg")
-        collage.save(collage_path, quality=95, dpi=(300, 300))
-
-        # Generación del ZIP con cadena de custodia
-        base_name = os.path.splitext(original_name)[0]
-        zip_filename = f"{base_name}_Fotogramas.zip"
-        final_zip_path = os.path.join(temp_dir, zip_filename)
-        
-        with zipfile.ZipFile(final_zip_path, mode="w") as zipf:
-            # Añadir todos los frames
-            for img_path in frame_paths:
-                zipf.write(img_path, os.path.basename(img_path))
-            
-            # Archivo TXT con formato profesional
-            with open(video_path, "rb") as f:
-                video_hash = hashlib.md5(f.read()).hexdigest()
-            
-            chain_content = (
-                "=== CADENA DE CUSTODIA DIGITAL ===\r\n\r\n"
-                f"• Archivo original: {original_name}\r\n"
-                f"• Fecha de procesamiento: {timestamp}\r\n"
-                f"• Fotogramas totales: {frame_count}\r\n"
-                f"• Hash MD5 video: {video_hash}\r\n"
-                f"• Fotogramas muestra: {', '.join([f'#{i+1}' for i in selected_indices])}\r\n\r\n"
-                "Este documento certifica la integridad del proceso de extracción.\n"
-                "Sistema Certificado por Peritos Forenses Digitales de Guatemala. \n"
-                "www.forensedigital.gt"
-            )
-            zipf.writestr("00_CADENA_CUSTODIA.txt", chain_content)
-
-        return collage_path, final_zip_path, temp_dir
-
-    except Exception as e:
-        raise gr.Error(f"Error en procesamiento: {str(e)}")
-
-def limpiar_cache(temp_dir):
-    if temp_dir and os.path.exists(temp_dir):
-        for file in os.listdir(temp_dir):
-            os.remove(os.path.join(temp_dir, file))
-        os.rmdir(temp_dir)
-
-with gr.Blocks(title="Extractior Forense de Fotogramas") as demo:
-    gr.Markdown("# 📷 Extractor Forense de Fotogramas de Videos")
-    gr.Markdown("**Herramienta certificada para extracción forense de fotogramas de videos** (No se guarda ninguna información). Soporta todos los formatos de video compatibles con OpenCV/FFmpeg.")
-    gr.Markdown("Desarrollado por José R. Leonett para el Grupo de Peritos Forenses Digitales de Guatemala - [www.forensedigital.gt](https://www.forensedigital.gt)")
-    
-    with gr.Row():
-        with gr.Column():
-            video_input = gr.Video(
-                label="CARGAR VIDEO",
-                sources=["upload"],
-                format="",
-                interactive=True
-            )
-            procesar_btn = gr.Button("🔍 INICIAR ANÁLISIS", interactive=False)
-        with gr.Column():
-            # gr.Markdown("## Resultados:")
-            gallery_output = gr.Image(label="COLLAGE DE REFERENCIA", height=400)
-            download_file = gr.File(label="DESCARGAR EVIDENCIAS", visible=True)
-    
-    temp_dir_state = gr.State()
-    zip_path_state = gr.State()
-    
-    def habilitar_procesado(video):
-        return gr.update(interactive=True) if video else gr.update(interactive=False)
-    
-    video_input.change(
-        fn=habilitar_procesado,
-        inputs=video_input,
-        outputs=procesar_btn,
-        queue=False
-    )
-    
-    def procesar_y_mostrar(video):
-        if temp_dir_state.value:
-            limpiar_cache(temp_dir_state.value)
-        collage, zip_path, temp_dir = procesar_video(video)
-        return collage, zip_path, temp_dir, zip_path
-    
-    procesar_btn.click(
-        fn=procesar_y_mostrar,
-        inputs=video_input,
-        outputs=[gallery_output, download_file, temp_dir_state, zip_path_state]
-    )
-
-if __name__ == "__main__":
-    demo.queue()
-    demo.launch(server_name="0.0.0.0", server_port=7860)