app.py

import os
import random
import gradio as gr
from groq import Groq
from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip
import numpy as np

# Initialize client with API key
client = Groq(
    api_key=os.getenv("Groq_Api_Key")
)

if client.api_key is None:
    raise EnvironmentError("Groq_Api_Key environment variable is not set.")

# Helper to create messages from history
def create_history_messages(history):
    history_messages = [{"role": "user", "content": m[0]} for m in history]
    history_messages.extend([{"role": "assistant", "content": m[1]} for m in history])
    return history_messages

# Generate response function
def generate_response(prompt, history, model, temperature, max_tokens, top_p, seed):
    messages = create_history_messages(history)
    messages.append({"role": "user", "content": prompt})

    if seed == 0:
        seed = random.randint(1, 100000)

    stream = client.chat.completions.create(
        messages=messages,
        model=model,
        temperature=temperature,
        max_tokens=max_tokens,
        top_p=top_p,
        seed=seed,
        stop=None,
        stream=True,
    )

    response = ""
    for chunk in stream:
        delta_content = chunk.choices[0].delta.content
        if delta_content is not None:
            response += delta_content
            yield response

    return response

# Process video function
from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip
from PIL import Image

# Adjusting MoviePy's resize function to use Image.LANCZOS directly
def process_video(text):
    video_folder = "videos"
    video_files = [os.path.join(video_folder, f) for f in os.listdir(video_folder) if f.endswith(('mp4', 'mov', 'avi', 'mkv'))]
    if not video_files:
        raise FileNotFoundError("No video files found in the specified directory.")

    selected_video = random.choice(video_files)
    video = VideoFileClip(selected_video)
    start_time = random.uniform(0, max(0, video.duration - 60))
    video = video.subclip(start_time, min(start_time + 60, video.duration))
    
    # Manually resize using PIL to avoid the issue
    def resize_image(image, new_size):
        pil_image = Image.fromarray(image)
        resized_pil = pil_image.resize(new_size[::-1], Image.LANCZOS)
        return np.array(resized_pil)
    
    new_size = (1080, int(video.h * (1080 / video.w)))
    video = video.fl_image(lambda image: resize_image(image, new_size))
    video = video.crop(x1=video.w // 2 - 540, x2=video.w // 2 + 540)

    text_lines = text.split()
    text = "\n".join([" ".join(text_lines[i:i+8]) for i in range(0, len(text_lines), 8)])
    
    text_clip = TextClip(text, fontsize=70, color='white', size=video.size, method='caption')
    text_clip = text_clip.set_position('center').set_duration(video.duration)

    final = CompositeVideoClip([video, text_clip])

    output_path = "output.mp4"
    final.write_videofile(output_path, codec="libx264")

    return output_path

# Additional inputs for the chat interface
additional_inputs = [
    gr.Dropdown(choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it"], value="llama3-70b-8192", label="Model"),
    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
    gr.Slider(minimum=1, maximum=32192, step=1, value=4096, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b, llama 7b & 70b, 32k for mixtral 8x7b."),
    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
    gr.Number(precision=0, value=42, label="Seed", info="A starting point to initiate generation, use 0 for random")
]

# Gradio interface with blocks and tabs
# Chat Interface
def create_chat_interface():
    return gr.ChatInterface(
        fn=generate_response,
        chatbot=gr.Chatbot(
            show_label=False,
            show_share_button=False,
            show_copy_button=True,
            likeable=True,
            layout="panel"
        ),
        additional_inputs=additional_inputs,
        title="YTSHorts Maker",
        description="Powered by GROQ, MoviePy, and other tools."
    )

# Main app definition
with gr.Blocks(theme=gr.themes.Soft(primary_hue="red", secondary_hue="pink")) as demo:
    with gr.Tabs():
        # Chat Ta
        with gr.TabItem("Video Processing"):
            text_input = gr.Textbox(lines=5, label="Text (8 words max per line)")
            process_button = gr.Button("Process Video")
            video_output = gr.Video(label="Processed Video")

            process_button.click(
                fn=process_video,
                inputs=text_input,
                outputs=video_output,
            )

# Launch the Gradio interface
if __name__ == "__main__":
    demo.launch()