Modules/Generate_Video.py

from __future__ import annotations

import os
import uuid
import random
import tempfile
from typing import Annotated

import gradio as gr
from huggingface_hub import InferenceClient
from .File_System import ROOT_DIR

from app import _log_call_end, _log_call_start, _truncate_for_log
from ._docstrings import autodoc

HF_VIDEO_TOKEN = os.getenv("HF_READ_TOKEN") or os.getenv("HF_TOKEN")

# Single source of truth for the LLM-facing tool description
TOOL_SUMMARY = (
    "Generate a short MP4 video from a text prompt via Hugging Face serverless inference; "
    "control model, steps, guidance, seed, size, fps, and duration; returns a temporary MP4 file path. "
    "Return the generated media to the user in this format `![Alt text](URL)`."
)


def _write_video_tmp(data_iter_or_bytes: object, suffix: str = ".mp4") -> str:
    filename = f"video_{uuid.uuid4().hex[:8]}{suffix}"
    path = os.path.join(ROOT_DIR, filename)
    try:
        with open(path, "wb") as file:
            if isinstance(data_iter_or_bytes, (bytes, bytearray)):
                file.write(data_iter_or_bytes)
            elif hasattr(data_iter_or_bytes, "read"):
                file.write(data_iter_or_bytes.read())
            elif hasattr(data_iter_or_bytes, "content"):
                file.write(data_iter_or_bytes.content)  # type: ignore[attr-defined]
            elif hasattr(data_iter_or_bytes, "__iter__") and not isinstance(data_iter_or_bytes, (str, dict)):
                for chunk in data_iter_or_bytes:  # type: ignore[assignment]
                    if chunk:
                        file.write(chunk)
            else:
                raise gr.Error("Unsupported video data type returned by provider.")
    except Exception:
        try:
            os.remove(path)
        except Exception:
            pass
        raise
    return path


@autodoc(
    summary=TOOL_SUMMARY,
)
def Generate_Video(
    prompt: Annotated[str, "Text description of the video to generate (e.g., 'a red fox running through a snowy forest at sunrise')."],
    model_id: Annotated[str, "Hugging Face model id in the form 'creator/model-name'. Defaults to akhaliq/sora-2."] = "akhaliq/sora-2",
    negative_prompt: Annotated[str, "What should NOT appear in the video."] = "",
    steps: Annotated[int, "Number of denoising steps (1–100). Higher can improve quality but is slower."] = 25,
    cfg_scale: Annotated[float, "Guidance scale (1–20). Higher = follow the prompt more closely, lower = more creative."] = 3.5,
    seed: Annotated[int, "Random seed for reproducibility. Use -1 for a random seed per call."] = -1,
    width: Annotated[int, "Output width in pixels (multiples of 8 recommended)."] = 768,
    height: Annotated[int, "Output height in pixels (multiples of 8 recommended)."] = 768,
    fps: Annotated[int, "Frames per second of the output video (e.g., 24)."] = 24,
    duration: Annotated[float, "Target duration in seconds. For Sora-2, must be 4, 8, or 12."] = 4.0,
) -> str:
    _log_call_start(
        "Generate_Video",
        prompt=_truncate_for_log(prompt, 160),
        model_id=model_id,
        steps=steps,
        cfg_scale=cfg_scale,
        fps=fps,
        duration=duration,
        size=f"{width}x{height}",
    )
    if not prompt or not prompt.strip():
        _log_call_end("Generate_Video", "error=empty prompt")
        raise gr.Error("Please provide a non-empty prompt.")
    providers = ["auto", "replicate", "fal-ai"]
    last_error: Exception | None = None
    parameters = {
        "negative_prompt": negative_prompt or None,
        "num_inference_steps": steps,
        "guidance_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1_000_000_000),
        "width": width,
        "height": height,
        "fps": fps,
        "duration": duration,
    }
    for provider in providers:
        try:
            client = InferenceClient(api_key=HF_VIDEO_TOKEN, provider=provider)
            if hasattr(client, "text_to_video"):
                num_frames = int(duration * fps) if duration and fps else None
                extra_body = {}
                if width:
                    extra_body["width"] = width
                if height:
                    extra_body["height"] = height
                if fps:
                    extra_body["fps"] = fps
                if duration:
                    extra_body["duration"] = duration
                result = client.text_to_video(
                    prompt=prompt,
                    model=model_id,
                    guidance_scale=cfg_scale,
                    negative_prompt=[negative_prompt] if negative_prompt else None,
                    num_frames=num_frames,
                    num_inference_steps=steps,
                    seed=parameters["seed"],
                    extra_body=extra_body if extra_body else None,
                )
            else:
                # Fallback for older clients or specific providers if needed, though InferenceClient usually has text_to_video
                # Note: client.post is not available in some versions of InferenceClient
                continue 
            
            path = _write_video_tmp(result, suffix=".mp4")
            try:
                size = os.path.getsize(path)
            except Exception:
                size = -1
            _log_call_end("Generate_Video", f"provider={provider} path={os.path.basename(path)} bytes={size}")
            return path
        except KeyError as exc:
            # Handle specific provider errors that manifest as KeyError (e.g. fal-ai missing 'video' key on error)
            if "video" in str(exc):
                last_error = ValueError(f"Provider {provider} returned an invalid response. This often happens with invalid parameters (e.g. duration must be 4, 8, or 12 for Sora-2).")
            else:
                last_error = exc
            continue
        except Exception as exc:  # pylint: disable=broad-except
            last_error = exc
            continue
    msg = str(last_error) if last_error else "Unknown error"
    lowered = msg.lower()
    if "404" in msg:
        raise gr.Error(f"Model not found or unavailable: {model_id}. Check the id and HF token access.")
    if "503" in msg:
        raise gr.Error("The model is warming up. Please try again shortly.")
    if "401" in msg or "403" in msg:
        raise gr.Error("Please duplicate the space and provide a `HF_READ_TOKEN` to enable Image and Video Generation.")
    if ("api_key" in lowered) or ("hf auth login" in lowered) or ("unauthorized" in lowered) or ("forbidden" in lowered):
        raise gr.Error("Please duplicate the space and provide a `HF_READ_TOKEN` to enable Image and Video Generation.")
    _log_call_end("Generate_Video", f"error={_truncate_for_log(msg, 200)}")
    raise gr.Error(f"Video generation failed: {msg}")


def build_interface() -> gr.Interface:
    return gr.Interface(
        fn=Generate_Video,
        inputs=[
            gr.Textbox(label="Prompt", placeholder="Enter a prompt for the video", lines=2),
            gr.Textbox(
                label="Model",
                value="akhaliq/sora-2",
                placeholder="creator/model-name",
                max_lines=1,
                info="<a href=\"https://huggingface.co/models?pipeline_tag=text-to-video&inference_provider=nebius,cerebras,novita,fireworks-ai,together,fal-ai,groq,featherless-ai,nscale,hyperbolic,sambanova,cohere,replicate,scaleway,publicai,hf-inference&sort=trending\" target=\"_blank\" rel=\"noopener noreferrer\">Browse models</a>",
            ),
            gr.Textbox(label="Negative Prompt", value="", lines=2),
            gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Steps"),
            gr.Slider(minimum=1.0, maximum=20.0, value=3.5, step=0.1, label="CFG Scale"),
            gr.Slider(minimum=-1, maximum=1_000_000_000, value=-1, step=1, label="Seed (-1 = random)"),
            gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Width"),
            gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Height"),
            gr.Slider(minimum=4, maximum=60, value=24, step=1, label="FPS"),
            gr.Slider(minimum=1.0, maximum=10.0, value=4.0, step=0.5, label="Duration (s)"),
        ],
        outputs=gr.Video(label="Generated Video", buttons=["download"], format="mp4"),
        title="Generate Video",
        description=(
            "<div style=\"text-align:center\">Generate short videos via Hugging Face serverless inference. "
            "Default model is Sora-2.</div>"
        ),
        api_description=TOOL_SUMMARY,
        flagging_mode="never",
    )


__all__ = ["Generate_Video", "build_interface"]