reverting gradio

gradio_app.py

@@ -1,174 +1,3 @@
-import os
-import subprocess
-from datetime import datetime
-from pathlib import Path
-import gradio as gr
-
-# -----------------------------
-# Setup paths and env
-# -----------------------------
-HF_HOME = "/app/hf_cache"
-os.environ["HF_HOME"] = HF_HOME
-os.environ["TRANSFORMERS_CACHE"] = HF_HOME
-os.makedirs(HF_HOME, exist_ok=True)
-
-PRETRAINED_DIR = "/app/pretrained"
-os.makedirs(PRETRAINED_DIR, exist_ok=True)
-
-
-# -----------------------------
-# Step 1: Optional Model Download
-# -----------------------------
-def download_models():
-    expected_model = os.path.join(PRETRAINED_DIR, "RAFT/raft-things.pth")
-    if not Path(expected_model).exists():
-        print("⚙️ Downloading pretrained models...")
-        try:
-            subprocess.check_call(["bash", "download/download_models.sh"])
-            print("✅ Models downloaded.")
-        except subprocess.CalledProcessError as e:
-            print(f"❌ Model download failed: {e}")
-    else:
-        print("✅ Pretrained models already exist.")
-
-
-download_models()
-
-
-# -----------------------------
-# Step 2: Inference Logic
-# -----------------------------
-
-def run_epic_inference(video_path, caption, motion_type):
-    temp_input_path = "/app/temp_input.mp4"
-    output_dir = f"/app/output_anchor"
-    video_output_path = f"{output_dir}/masked_videos/output.mp4"
-    traj_name = motion_type
-    traj_txt = f"/app/inference/v2v_data/test/trajs/{traj_name}.txt"
-    # Save uploaded video
-    if video_path:
-        os.system(f"cp '{video_path}' {temp_input_path}")
-
-    command = [
-    "python", "/app/inference/v2v_data/inference.py",
-    "--video_path", temp_input_path,
-    "--stride", "1",
-    "--out_dir", output_dir,
-    "--radius_scale", "1",
-    "--camera", "target",
-    "--mask", 
-    "--target_pose", "0", "30", "-0.6", "0", "0",
-    "--traj_txt", traj_txt,
-    "--save_name", "output",
-    "--mode", "gradual",
-    ]
-
-    # Run inference command
-    try:
-        result = subprocess.run(command, capture_output=True, text=True, check=True)
-        print("Getting Anchor Videos run successfully.")
-        logs = result.stdout
-    except subprocess.CalledProcessError as e:
-        logs = f"❌ Inference failed:\n{e.stderr}"
-        return logs, None
-
-    # Locate the output video
-    if video_output_path:
-        return logs, str(video_output_path)
-    else:
-        return f"Inference succeeded but no output video found in {output_dir}", None
-def print_output_directory(out_dir): 
-    result = ""
-    for root, dirs, files in os.walk(out_dir):
-        level = root.replace(out_dir, '').count(os.sep)
-        indent = ' ' * 4 * level
-        result += f"{indent}{os.path.basename(root)}/"
-        sub_indent = ' ' * 4 * (level + 1)
-        for f in files:
-            result += f"{sub_indent}{f}\n"
-    return result
-
-def inference(video_path, caption, motion_type):
-    logs, video_masked = run_epic_inference(video_path, caption, motion_type)
-
-    MODEL_PATH="/app/pretrained/CogVideoX-5b-I2V"
-
-    ckpt_steps=500
-    ckpt_dir="/app/out/EPiC_pretrained"
-    ckpt_file=f"checkpoint-{ckpt_steps}.pt"
-    ckpt_path=f"{ckpt_dir}/{ckpt_file}"
-
-    video_root_dir= f"/app/output_anchor"
-    out_dir=f"/app/output"
-
-    command = [
-        "python", "/app/inference/cli_demo_camera_i2v_pcd.py",
-        "--video_root_dir", video_root_dir,
-        "--base_model_path", MODEL_PATH,
-        "--controlnet_model_path", ckpt_path,
-        "--output_path", out_dir,
-        "--start_camera_idx", "0",
-        "--end_camera_idx", "8",
-        "--controlnet_weights", "1.0",
-        "--controlnet_guidance_start", "0.0",
-        "--controlnet_guidance_end", "0.4",
-        "--controlnet_input_channels", "3",
-        "--controlnet_transformer_num_attn_heads", "4",
-        "--controlnet_transformer_attention_head_dim", "64",
-        "--controlnet_transformer_out_proj_dim_factor", "64",
-        "--controlnet_transformer_out_proj_dim_zero_init",
-        "--vae_channels", "16",
-        "--num_frames", "49",
-        "--controlnet_transformer_num_layers", "8",
-        "--infer_with_mask",
-        "--pool_style", "max",
-        "--seed", "43"
-    ]
-
-    # Run the command
-    result = subprocess.run(command, capture_output=True, text=True)
-    if result.returncode == 0:
-        print("Inference completed successfully.")
-    else:
-        print(f"Error occurred during inference: {result.stderr}")
-
-    # Print output directory contents
-    logs = result.stdout
-    result = print_output_directory(out_dir)
-
-    return logs+result, str(f"{out_dir}/00000_43_out.mp4")
-
-# output 43
-# output/    00000_43_out.mp4
-#     00000_43_reference.mp4
-#     00000_43_out_reference.mp4
-
-# -----------------------------
-# Step 3: Create Gradio UI
-# -----------------------------
-demo = gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.Video(label="Upload Video (MP4)"),
-        gr.Textbox(label="Caption", placeholder="e.g., Amalfi coast with boats"),
-        gr.Dropdown(
-            choices=["zoom_in", "rotate", "orbit", "pan", "loop1"],
-            label="Camera Motion Type",
-            value="zoom_in",
-        ),
-    ],
-    outputs=[gr.Textbox(label="Inference Logs"), gr.Video(label="Generated Video")],
-    title="🎬 EPiC: Efficient Video Camera Control",
-    description="Upload a video, describe the scene, and apply cinematic camera motion using pretrained EPiC models.",
-)
-
-# -----------------------------
-# Step 4: Launch App
-# -----------------------------
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
-
 # import os
 # import subprocess
 # from datetime import datetime
@@ -186,6 +15,7 @@ if __name__ == "__main__":
 # PRETRAINED_DIR = "/app/pretrained"
 # os.makedirs(PRETRAINED_DIR, exist_ok=True)
 
+
 # # -----------------------------
 # # Step 1: Optional Model Download
 # # -----------------------------
@@ -201,69 +31,75 @@ if __name__ == "__main__":
 #     else:
 #         print("✅ Pretrained models already exist.")
 
+
 # download_models()
 
+
 # # -----------------------------
 # # Step 2: Inference Logic
 # # -----------------------------
-# def run_epic_inference(video_path, target_pose, mode):
+
+# def run_epic_inference(video_path, caption, motion_type):
 #     temp_input_path = "/app/temp_input.mp4"
-#     output_dir = "/app/output_anchor"
+#     output_dir = f"/app/output_anchor"
 #     video_output_path = f"{output_dir}/masked_videos/output.mp4"
-
+#     traj_name = motion_type
+#     traj_txt = f"/app/inference/v2v_data/test/trajs/{traj_name}.txt"
 #     # Save uploaded video
 #     if video_path:
 #         os.system(f"cp '{video_path}' {temp_input_path}")
 
-#     try:
-#         theta, phi, r, x, y = target_pose.strip().split()
-#     except ValueError:
-#         return f"❌ Invalid target pose format. Use: θ φ r x y", None
-
 #     command = [
-#         "python", "/app/inference/v2v_data/inference.py",
-#         "--video_path", temp_input_path,
-#         "--stride", "1",
-#         "--out_dir", output_dir,
-#         "--radius_scale", "1",
-#         "--camera", "target",
-#         "--mask",
-#         "--target_pose", theta, phi, r, x, y,
-#         "--save_name", "output",
-#         "--mode", mode,
+#     "python", "/app/inference/v2v_data/inference.py",
+#     "--video_path", temp_input_path,
+#     "--stride", "1",
+#     "--out_dir", output_dir,
+#     "--radius_scale", "1",
+#     "--camera", "target",
+#     "--mask", 
+#     "--target_pose", "0", "30", "-0.6", "0", "0",
+#     "--traj_txt", traj_txt,
+#     "--save_name", "output",
+#     "--mode", "gradual",
 #     ]
 
+#     # Run inference command
 #     try:
 #         result = subprocess.run(command, capture_output=True, text=True, check=True)
+#         print("Getting Anchor Videos run successfully.")
 #         logs = result.stdout
 #     except subprocess.CalledProcessError as e:
 #         logs = f"❌ Inference failed:\n{e.stderr}"
 #         return logs, None
 
-#     return logs, str(video_output_path) if os.path.exists(video_output_path) else (logs, None)
-
+#     # Locate the output video
+#     if video_output_path:
+#         return logs, str(video_output_path)
+#     else:
+#         return f"Inference succeeded but no output video found in {output_dir}", None
 # def print_output_directory(out_dir): 
 #     result = ""
 #     for root, dirs, files in os.walk(out_dir):
 #         level = root.replace(out_dir, '').count(os.sep)
 #         indent = ' ' * 4 * level
-#         result += f"{indent}{os.path.basename(root)}/\n"
+#         result += f"{indent}{os.path.basename(root)}/"
 #         sub_indent = ' ' * 4 * (level + 1)
 #         for f in files:
 #             result += f"{sub_indent}{f}\n"
 #     return result
 
-# def inference(video_path, num_frames, fps, target_pose, mode):
-#     logs, video_masked = run_epic_inference(video_path, target_pose, mode)
+# def inference(video_path, caption, motion_type):
+#     logs, video_masked = run_epic_inference(video_path, caption, motion_type)
 
-#     MODEL_PATH = "/app/pretrained/CogVideoX-5b-I2V"
-#     ckpt_steps = 500
-#     ckpt_dir = "/app/out/EPiC_pretrained"
-#     ckpt_file = f"checkpoint-{ckpt_steps}.pt"
-#     ckpt_path = f"{ckpt_dir}/{ckpt_file}"
+#     MODEL_PATH="/app/pretrained/CogVideoX-5b-I2V"
 
-#     video_root_dir = "/app/output_anchor"
-#     out_dir = "/app/output"
+#     ckpt_steps=500
+#     ckpt_dir="/app/out/EPiC_pretrained"
+#     ckpt_file=f"checkpoint-{ckpt_steps}.pt"
+#     ckpt_path=f"{ckpt_dir}/{ckpt_file}"
+
+#     video_root_dir= f"/app/output_anchor"
+#     out_dir=f"/app/output"
 
 #     command = [
 #         "python", "/app/inference/cli_demo_camera_i2v_pcd.py",
@@ -282,19 +118,30 @@ if __name__ == "__main__":
 #         "--controlnet_transformer_out_proj_dim_factor", "64",
 #         "--controlnet_transformer_out_proj_dim_zero_init",
 #         "--vae_channels", "16",
-#         "--num_frames", str(num_frames),
+#         "--num_frames", "49",
 #         "--controlnet_transformer_num_layers", "8",
 #         "--infer_with_mask",
 #         "--pool_style", "max",
-#         "--seed", "43",
-#         "--fps", str(fps)
+#         "--seed", "43"
 #     ]
 
+#     # Run the command
 #     result = subprocess.run(command, capture_output=True, text=True)
-#     logs += "\n" + result.stdout
-#     result_dir = print_output_directory(out_dir)
+#     if result.returncode == 0:
+#         print("Inference completed successfully.")
+#     else:
+#         print(f"Error occurred during inference: {result.stderr}")
+
+#     # Print output directory contents
+#     logs = result.stdout
+#     result = print_output_directory(out_dir)
+
+#     return logs+result, str(f"{out_dir}/00000_43_out.mp4")
 
-#     return logs + result_dir, str(f"{out_dir}/00000_43_out.mp4")
+# # output 43
+# # output/    00000_43_out.mp4
+# #     00000_43_reference.mp4
+# #     00000_43_out_reference.mp4
 
 # # -----------------------------
 # # Step 3: Create Gradio UI
@@ -303,15 +150,14 @@ if __name__ == "__main__":
 #     fn=inference,
 #     inputs=[
 #         gr.Video(label="Upload Video (MP4)"),
-#         gr.Slider(minimum=1, maximum=120, value=50, step=1, label="Number of Frames"),
-#         gr.Slider(minimum=1, maximum=90, value=10, step=1, label="FPS"),
-#         gr.Textbox(label="Target Pose (θ φ r x y)", placeholder="e.g., 0 30 -0.6 0 0"),
-#         gr.Dropdown(choices=["gradual", "direct", "bullet"], value="gradual", label="Camera Mode"),
-#     ],
-#     outputs=[
-#         gr.Textbox(label="Inference Logs"),
-#         gr.Video(label="Generated Video")
+#         gr.Textbox(label="Caption", placeholder="e.g., Amalfi coast with boats"),
+#         gr.Dropdown(
+#             choices=["zoom_in", "rotate", "orbit", "pan", "loop1"],
+#             label="Camera Motion Type",
+#             value="zoom_in",
+#         ),
 #     ],
+#     outputs=[gr.Textbox(label="Inference Logs"), gr.Video(label="Generated Video")],
 #     title="🎬 EPiC: Efficient Video Camera Control",
 #     description="Upload a video, describe the scene, and apply cinematic camera motion using pretrained EPiC models.",
 # )
@@ -321,3 +167,158 @@ if __name__ == "__main__":
 # # -----------------------------
 # if __name__ == "__main__":
 #     demo.launch(server_name="0.0.0.0", server_port=7860)
+
+
+import os
+import subprocess
+from datetime import datetime
+from pathlib import Path
+import gradio as gr
+
+# -----------------------------
+# Setup paths and env
+# -----------------------------
+HF_HOME = "/app/hf_cache"
+os.environ["HF_HOME"] = HF_HOME
+os.environ["TRANSFORMERS_CACHE"] = HF_HOME
+os.makedirs(HF_HOME, exist_ok=True)
+
+PRETRAINED_DIR = "/app/pretrained"
+os.makedirs(PRETRAINED_DIR, exist_ok=True)
+
+# -----------------------------
+# Step 1: Optional Model Download
+# -----------------------------
+def download_models():
+    expected_model = os.path.join(PRETRAINED_DIR, "RAFT/raft-things.pth")
+    if not Path(expected_model).exists():
+        print("⚙️ Downloading pretrained models...")
+        try:
+            subprocess.check_call(["bash", "download/download_models.sh"])
+            print("✅ Models downloaded.")
+        except subprocess.CalledProcessError as e:
+            print(f"❌ Model download failed: {e}")
+    else:
+        print("✅ Pretrained models already exist.")
+
+download_models()
+
+# -----------------------------
+# Step 2: Inference Logic
+# -----------------------------
+def run_epic_inference(video_path, target_pose, mode):
+    temp_input_path = "/app/temp_input.mp4"
+    output_dir = "/app/output_anchor"
+    video_output_path = f"{output_dir}/masked_videos/output.mp4"
+
+    # Save uploaded video
+    if video_path:
+        os.system(f"cp '{video_path}' {temp_input_path}")
+
+    try:
+        theta, phi, r, x, y = target_pose.strip().split()
+    except ValueError:
+        return f"❌ Invalid target pose format. Use: θ φ r x y", None
+    logs =  f"Running inference with target pose: θ={theta}, φ={phi}, r={r}, x={x}, y={y}\n"
+    command = [
+        "python", "/app/inference/v2v_data/inference.py",
+        "--video_path", temp_input_path,
+        "--stride", "1",
+        "--out_dir", output_dir,
+        "--radius_scale", "1",
+        "--camera", "target",
+        "--mask",
+        "--target_pose", theta, phi, r, x, y,
+        "--save_name", "output",
+        "--mode", mode,
+    ]
+
+    try:
+        result = subprocess.run(command, capture_output=True, text=True, check=True)
+        logs += result.stdout
+    except subprocess.CalledProcessError as e:
+        logs += f"❌ Inference failed:\n{e.stderr}"
+        return logs, None
+
+    return logs, str(video_output_path) if os.path.exists(video_output_path) else (logs, None)
+
+def print_output_directory(out_dir): 
+    result = ""
+    for root, dirs, files in os.walk(out_dir):
+        level = root.replace(out_dir, '').count(os.sep)
+        indent = ' ' * 4 * level
+        result += f"{indent}{os.path.basename(root)}/\n"
+        sub_indent = ' ' * 4 * (level + 1)
+        for f in files:
+            result += f"{sub_indent}{f}\n"
+    return result
+
+def inference(video_path, num_frames, fps, target_pose, mode):
+    logs, video_masked = run_epic_inference(video_path, target_pose, mode)
+    return logs, video_masked
+
+    MODEL_PATH = "/app/pretrained/CogVideoX-5b-I2V"
+    ckpt_steps = 500
+    ckpt_dir = "/app/out/EPiC_pretrained"
+    ckpt_file = f"checkpoint-{ckpt_steps}.pt"
+    ckpt_path = f"{ckpt_dir}/{ckpt_file}"
+
+    video_root_dir = "/app/output_anchor"
+    out_dir = "/app/output"
+
+    command = [
+        "python", "/app/inference/cli_demo_camera_i2v_pcd.py",
+        "--video_root_dir", video_root_dir,
+        "--base_model_path", MODEL_PATH,
+        "--controlnet_model_path", ckpt_path,
+        "--output_path", out_dir,
+        "--start_camera_idx", "0",
+        "--end_camera_idx", "8",
+        "--controlnet_weights", "1.0",
+        "--controlnet_guidance_start", "0.0",
+        "--controlnet_guidance_end", "0.4",
+        "--controlnet_input_channels", "3",
+        "--controlnet_transformer_num_attn_heads", "4",
+        "--controlnet_transformer_attention_head_dim", "64",
+        "--controlnet_transformer_out_proj_dim_factor", "64",
+        "--controlnet_transformer_out_proj_dim_zero_init",
+        "--vae_channels", "16",
+        "--num_frames", str(num_frames),
+        "--controlnet_transformer_num_layers", "8",
+        "--infer_with_mask",
+        "--pool_style", "max",
+        "--seed", "43",
+        "--fps", str(fps)
+    ]
+
+    result = subprocess.run(command, capture_output=True, text=True)
+    logs += "\n" + result.stdout
+    result_dir = print_output_directory(out_dir)
+
+    return logs + result_dir + "Hello! it is successful", str(f"{out_dir}/00000_43_out.mp4")
+
+# -----------------------------
+# Step 3: Create Gradio UI
+# -----------------------------
+demo = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.Video(label="Upload Video (MP4)"),
+        gr.Slider(minimum=1, maximum=120, value=50, step=1, label="Number of Frames"),
+        gr.Slider(minimum=1, maximum=90, value=10, step=1, label="FPS"),
+        gr.Textbox(label="Target Pose (θ φ r x y)", placeholder="e.g., 0 30 -0.6 0 0"),
+        gr.Dropdown(choices=["gradual", "direct", "bullet"], value="gradual", label="Camera Mode"),
+    ],
+    outputs=[
+        gr.Textbox(label="Inference Logs"),
+        gr.Video(label="Generated Video")
+    ],
+    title="🎬 EPiC: Efficient Video Camera Control",
+    description="Upload a video, describe the scene, and apply cinematic camera motion using pretrained EPiC models.",
+)
+
+# -----------------------------
+# Step 4: Launch App
+# -----------------------------
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)