Delete app.py

app.py

@@ -1,148 +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:
-        original_name = os.path.basename(video)
-        allowed_extensions = ('.mp4', '.avi', '.mov', '.mkv')
-        if not original_name.lower().endswith(allowed_extensions):
-            raise gr.Error("Solo se permiten archivos de video (mp4, avi, mov, mkv)")
-
-        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)
-
-        cap = cv2.VideoCapture(video)
-        frame_count = 0
-        frame_paths = []
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            img = Image.fromarray(frame_rgb)
-            img_path = os.path.join(temp_dir, f"frame_{frame_count:04d}.jpg")
-            img.save(img_path)
-            frame_paths.append(img_path)
-            frame_count += 1
-        cap.release()
-
-        if frame_count == 0:
-            raise gr.Error("No se pudieron extraer fotogramas del video")
-
-        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]
-
-        images = []
-        for img_path in selected_frames:
-            img = Image.open(img_path)
-            bordered_img = ImageOps.expand(img, border=2, fill='white')
-            images.append(bordered_img)
-
-        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))
-        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)
-        ]
-
-        for i, img in enumerate(images):
-            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)
-            collage.paste(img, positions[i])
-
-        collage_path = os.path.join(temp_dir, "collage_forense.jpg")
-        collage.save(collage_path, quality=95, dpi=(300, 300))
-
-        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:
-            for img_path in frame_paths:
-                zipf.write(img_path, os.path.basename(img_path))
-
-            with open(video, "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
-
-    except Exception as e:
-        raise gr.Error(f"Error en procesamiento: {str(e)}")
-
-with gr.Blocks(title="Extractor 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).
-""")
-    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.File(
-                label="CARGAR VIDEO",
-                file_types=[".mp4", ".avi", ".mov", ".mkv"],
-                type="filepath",
-                interactive=True
-            )
-            procesar_btn = gr.Button("🔍 INICIAR ANÁLISIS", interactive=False)
-        with gr.Column():
-            gallery_output = gr.Image(label="COLLAGE DE REFERENCIA", height=400)
-            download_file = gr.File(label="DESCARGAR EVIDENCIAS", visible=True)
-
-    import os
-    def habilitar_procesado(video):
-        if video and os.path.exists(video) and video.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
-            return gr.update(interactive=True)
-        return gr.update(interactive=False)
-
-    video_input.change(
-        fn=habilitar_procesado,
-        inputs=video_input,
-        outputs=procesar_btn,
-        queue=False
-    )
-
-    def procesar_y_mostrar(video):
-        collage, zip_path = procesar_video(video)
-        return collage, zip_path
-
-    procesar_btn.click(
-        fn=procesar_y_mostrar,
-        inputs=video_input,
-        outputs=[gallery_output, download_file]
-    )
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)