app.py

import gradio as gr
import os
from huggingface_hub import InferenceClient
import tempfile
from pathlib import Path
from PIL import Image as PILImage
import io

# Initialize the Hugging Face Inference client
client = InferenceClient(
    provider="fal-ai",
    api_key=os.environ.get("HF_TOKEN"),  # Your HF token must be set in the environment
    bill_to="huggingface",
)

# --- Core Functions ---
def text_to_video(prompt, duration=5, aspect_ratio="16:9", resolution="720p"):
    """Generate video from text prompt"""
    try:
        if not prompt or prompt.strip() == "":
            return None, "⚠️ Please enter a text prompt."

        # Generate video from text
        video = client.text_to_video(
            prompt,
            model="akhaliq/veo3.1-fast",
        )

        # Save the video to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
            tmp_file.write(video)
            video_path = tmp_file.name

        return video_path, f"✅ Video generated successfully from prompt: '{prompt[:50]}...'"
    except Exception as e:
        return None, f"❌ Error generating video: {str(e)}"


def image_to_video(image, prompt, duration=5, aspect_ratio="16:9", resolution="720p"):
    """Generate video from image and prompt"""
    try:
        if image is None:
            return None, "⚠️ Please upload an image."
        if not prompt or prompt.strip() == "":
            return None, "⚠️ Please enter a motion description."

        # Convert image to bytes
        if isinstance(image, PILImage.Image):
            buffer = io.BytesIO()
            image.save(buffer, format="PNG")
            input_image = buffer.getvalue()
        else:
            pil_image = PILImage.fromarray(image)
            buffer = io.BytesIO()
            pil_image.save(buffer, format="PNG")
            input_image = buffer.getvalue()

        # Generate video from image
        video = client.image_to_video(
            input_image,
            prompt=prompt,
            model="akhaliq/veo3.1-fast-image-to-video",
        )

        # Save the video to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
            tmp_file.write(video)
            video_path = tmp_file.name

        return video_path, f"✅ Video generated successfully with motion: '{prompt[:50]}...'"
    except Exception as e:
        return None, f"❌ Error generating video: {str(e)}"


def clear_text_tab():
    """Clear text-to-video tab"""
    return "", None, ""


def clear_image_tab():
    """Clear image-to-video tab"""
    return None, "", None, ""


# --- Custom CSS ---
custom_css = """
.container {
    max-width: 1200px;
    margin: auto;
}
.status-box {
    padding: 10px;
    border-radius: 5px;
    margin-top: 10px;
}
"""

# --- Gradio App ---
with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="AI Video Generator") as demo:
    gr.Markdown(
        """
        # 🎬 AI Video Generator  
        ### Generate stunning videos from text or animate your images with AI  
        #### Powered by VEO 3.1 Fast Model
        """
    )

    with gr.Tabs() as tabs:
        # Text-to-Video Tab
        with gr.Tab("📝 Text to Video"):
            gr.Markdown("### Transform your text descriptions into dynamic videos")

            with gr.Row():
                with gr.Column(scale=1):
                    text_prompt = gr.Textbox(
                        label="Text Prompt",
                        placeholder="Describe the video you want to create...",
                        lines=4,
                        max_lines=6
                    )

                    with gr.Accordion("Advanced Settings", open=False):
                        text_duration = gr.Slider(1, 10, value=5, step=1, label="Duration (seconds)")
                        text_aspect_ratio = gr.Dropdown(
                            ["16:9", "9:16", "1:1", "4:3", "21:9"], value="16:9", label="Aspect Ratio"
                        )
                        text_resolution = gr.Dropdown(
                            ["480p", "720p", "1080p"], value="720p", label="Resolution"
                        )

                    with gr.Row():
                        text_generate_btn = gr.Button("🎬 Generate Video", variant="primary")
                        text_clear_btn = gr.ClearButton(value="🗑️ Clear")

                    text_status = gr.Textbox(
                        label="Status", interactive=False, visible=True, elem_classes=["status-box"]
                    )

                with gr.Column(scale=1):
                    text_video_output = gr.Video(
                        label="Generated Video", autoplay=True, show_download_button=True, height=400
                    )

            gr.Examples(
                examples=[
                    ["A serene beach at sunset with gentle waves"],
                    ["A bustling city street with neon lights at night"],
                    ["A majestic eagle soaring through mountain peaks"],
                    ["An astronaut floating in space near the ISS"],
                    ["Cherry blossoms falling in a Japanese garden"],
                ],
                inputs=text_prompt,
                label="Example Prompts",
            )

        # Image-to-Video Tab
        with gr.Tab("🖼️ Image to Video"):
            gr.Markdown("### Bring your static images to life with motion")

            with gr.Row():
                with gr.Column(scale=1):
                    image_input = gr.Image(label="Upload Image", type="pil", height=300)
                    image_prompt = gr.Textbox(
                        label="Motion Prompt",
                        placeholder="Describe how the image should move...",
                        lines=3,
                        max_lines=5
                    )

                    with gr.Accordion("Advanced Settings", open=False):
                        image_duration = gr.Slider(1, 10, value=5, step=1, label="Duration (seconds)")
                        image_aspect_ratio = gr.Dropdown(
                            ["16:9", "9:16", "1:1", "4:3", "21:9"], value="16:9", label="Aspect Ratio"
                        )
                        image_resolution = gr.Dropdown(
                            ["480p", "720p", "1080p"], value="720p", label="Resolution"
                        )

                    with gr.Row():
                        image_generate_btn = gr.Button("🎬 Animate Image", variant="primary")
                        image_clear_btn = gr.ClearButton(value="🗑️ Clear")

                    image_status = gr.Textbox(
                        label="Status", interactive=False, visible=True, elem_classes=["status-box"]
                    )

                with gr.Column(scale=1):
                    image_video_output = gr.Video(
                        label="Generated Video", autoplay=True, show_download_button=True, height=400
                    )

            gr.Examples(
                examples=[
                    [None, "The person starts walking forward"],
                    [None, "The animal begins to run"],
                    [None, "Camera slowly zooms in while the subject smiles"],
                    [None, "The flowers sway gently in the breeze"],
                    [None, "The clouds move across the sky in time-lapse"],
                ],
                inputs=[image_input, image_prompt],
                label="Example Motion Prompts",
            )

    # --- Event handlers ---
    text_generate_btn.click(
        fn=text_to_video,
        inputs=[text_prompt, text_duration, text_aspect_ratio, text_resolution],
        outputs=[text_video_output, text_status],
    )

    text_clear_btn.click(
        fn=clear_text_tab,
        inputs=[],
        outputs=[text_prompt, text_video_output, text_status],
    )

    image_generate_btn.click(
        fn=image_to_video,
        inputs=[image_input, image_prompt, image_duration, image_aspect_ratio, image_resolution],
        outputs=[image_video_output, image_status],
    )

    image_clear_btn.click(
        fn=clear_image_tab,
        inputs=[],
        outputs=[image_input, image_prompt, image_video_output, image_status],
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(show_api=False, share=False, quiet=True)