app.py

import os
import subprocess
import glob
import numpy as np
from PIL import Image

# Function to install missing dependencies
def install_dependencies():
    packages = [
        "groq", 
        "transformers", 
        "diffusers", 
        "gradio"
    ]
    for package in packages:
        try:
            __import__(package)
        except ImportError:
            subprocess.check_call(["pip", "install", package])

# Install dependencies
install_dependencies()

# Import dependencies
from groq import Groq
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import gradio as gr

# Validate GROQ_API_KEY environment variable
def validate_groq_api_key():
    if not os.environ.get("GROQ_API_KEY"):
        # Set default API key if not present
        os.environ["GROQ_API_KEY"] = "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"

# Initialize Groq Client
validate_groq_api_key()
client = Groq(
    api_key=os.environ.get("GROQ_API_KEY"),
)

# Example Groq Usage
def fetch_groq_completion(prompt):
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="llama3-8b-8192",
        stream=False,
    )
    return chat_completion.choices[0].message.content

# Text Understanding with Hugging Face Transformers
def extract_key_entities(text):
    ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
    entities = ner_pipeline(text)
    return [entity["word"] for entity in entities]

def summarize_text(text):
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    summary = summarizer(text, max_length=50, min_length=10, do_sample=False)
    return summary[0]['summary_text']

# Frame Generation using Stable Diffusion
def generate_frames(prompts, output_dir="frames"):
    os.makedirs(output_dir, exist_ok=True)
    model_id = "CompVis/stable-diffusion-v1-4"
    sd_pipeline = StableDiffusionPipeline.from_pretrained(model_id)
    
    frames = []
    for i, prompt in enumerate(prompts):
        image = sd_pipeline(prompt).images[0]
        frame_path = os.path.join(output_dir, f"frame_{i:04d}.png")
        image.save(frame_path)
        frames.append(frame_path)
    
    return frames

# Video Stitching with FFmpeg
def create_video_from_frames(frames_dir, output_video="output.mp4", fps=24):
    frame_pattern = os.path.join(frames_dir, "frame_%04d.png")
    command = [
        "ffmpeg", "-y", "-framerate", str(fps), "-i", frame_pattern,
        "-c:v", "libx264", "-pix_fmt", "yuv420p", output_video
    ]
    subprocess.run(command, check=True)
    return output_video

# Gradio Interface for Final Output
def generate_video_interface(prompt):
    # Step 1: Fetch understanding from Groq
    groq_response = fetch_groq_completion(prompt)

    # Step 2: Extract entities and summarize
    key_entities = extract_key_entities(groq_response)
    summary = summarize_text(groq_response)

    # Step 3: Generate frames
    prompts = [f"{entity}, cinematic, ultra-realistic" for entity in key_entities]
    frame_dir = "frames"
    generate_frames(prompts, output_dir=frame_dir)

    # Step 4: Create video
    video_path = create_video_from_frames(frame_dir)
    
    return video_path

# Launch Gradio App
def gradio_ui():
    interface = gr.Interface(
        fn=generate_video_interface,
        inputs="text",
        outputs="video",
        title="Text-to-Video Generator",
        description="Generate videos from text descriptions using open-source AI tools."
    )
    interface.launch()

if __name__ == "__main__":
    gradio_ui()