utils.py

import requests
import constants
import os
from PIL import Image
from gradio_client import Client
import moviepy.editor as mp
from moviepy.video.VideoClip import ImageClip
from moviepy.editor import AudioFileClip
from structured_output_extractor import StructuredOutputExtractor
from pydantic import BaseModel, Field
from typing import List
import tempfile
import os


def get_summarization(text: str):
    print('\n\nSummarizing text: ', text, type(text))
    # Input payload
    data = {"text_input": text}

    # Headers for authentication
    headers = {"Authorization": f"Bearer {constants.HF_TOKEN}"}

    try:
        # Make a GET request
        response = requests.post(constants.SUMMARIZATION_ENDPOINT, json=data, headers=headers)
        # Process response
        if response.status_code == 200:
            response_data = response.json()
            print("Returning Summarization")
            return response_data.get("output", "No output found.")
        else:
            print("Some Error Occured During Summarization Request")
            print(response)
            print(f"Error: {response.status_code}, {response.text}")
            return {"error_occured" : response.text}
    except Exception as e:
        print(f"An exception occurred: {e}")
        return {"error_occured" : e}
    
 
def segments_to_chunks(segments):
    chunks = []
    for segment in segments:
        chunks.append(segment.get("text"))
    return chunks
    

def get_image_prompts(text_input : List, summary):
    print(f"summary: {summary}")
        # Example Pydantic model (e.g., Movie)
    class ImagePromptResponseSchema(BaseModel):
        image_prompts: List[str] = Field(
            description="List of detailed image prompts, Each Image Prompt Per Chunk"
        )

    extractor = StructuredOutputExtractor(response_schema=ImagePromptResponseSchema)
    chunks_count = len(text_input)
    chunks = "chunk: " + "\nchunk: ".join(text_input)
    prompt = f"""
ROLE: You are a Highly Experienced Image Prompt Synthesizer
SYSTEM PROMPT: Given the Overall Summary and All Chunks of the Text:
1. Read the summary and the combined context of all chunks (the entire script).
2. **Identify the central theme and setting** of the complete text.
3. For each chunk, examine both the chunk and its summary, then create a **focused, context-aware image prompt** based on key visual elements.
4. **Ensure thematic consistency across all chunks:**  
   - The environment, mood, and lighting must remain true to the established theme (e.g., a dark, eerie jungle remains consistently dark and mysterious throughout).
5. **Keep the image style as 3D (this MUST be followed).**
6. **Negatives:** Do not include hyper-realistic elements or real-life human depictions, and avoid any out-of-context settings (e.g., a park in a jungle story).
7. **Use mood-specific lighting and color palettes:**  
   - For example, if the theme is a dark jungle, use deep greens, blacks, misty blues, and dim moonlight.  
   - Ensure that all visual elements (fog, shadows, expressions) support the horror/suspense atmosphere.
8. NEVER generate prompts that could lead to NSFW images or any explicit content. Use safe and appropriate descriptions.

### Example:
**Summary:**  
This text is a story of a man who ventured into a dark jungle and encountered a mysterious lion.

**Chunks:**  
1. A man enters the dark jungle, mist swirling around him.  
2. He comes face-to-face with a majestic yet eerie lion.

**Combined Context:**  
"A man ventures into a dense, eerie jungle and unexpectedly meets a mysterious lion."

**Generated Prompts:**  
- **Chunk 1:**  
  "[style: 3D | theme: dark jungle] A lone man steps into a dense, eerie jungle at twilight. Thick mist swirls around his feet as towering, twisted trees loom overhead. Dim, bluish moonlight filters through the foliage, casting long, haunting shadows."
  
- **Chunk 2:**  
  "[style: 3D | theme: dark jungle] In a clearing within the jungle, a majestic lion appears with an unsettling aura. Its eyes glow faintly in the dim light, and the surrounding trees seem to lean in, enhancing the mysterious tension."

TASK: Here is the summary: {summary}\n\n and \n\n Total of {chunks_count} chunks, generate an Image Prompt for each chunk\n\n {chunks}
"""
    result = extractor.extract(prompt)
    return result.model_dump()   # returns dictionary version pydantic model
    
    



def generate_image(prompt, path='test_image.png'):
    try:
        # Initialize the Gradio Client with Hugging Face token
        client = Client(constants.IMAGE_GENERATION_SPACE_NAME, hf_token=constants.HF_TOKEN)

        # Make the API request
        result = client.predict(
            param_0=prompt,  # Text prompt for image generation
            api_name="/predict"
        )

        image = Image.open(result)
        image.save(path)

        # Return the result (which includes the URL or file path)
        return result

    except Exception as e:
        print(f"Error during image generation: {e}")
        return {"error": str(e)}
    
def generate_images(image_prompts, folder_name='test_folder'):
    folder_path = tmp_folder(folder_name)
    for index, prompt in enumerate(image_prompts):
        print(index, prompt)
        image_path = generate_image(prompt=prompt, path=f"{folder_path}/{index}.png")
        yield prompt, image_path
    


def tmp_folder(folder_name: str) -> str:
    # Use the current working directory or any other accessible path for temp folders
    base_tmp_path = os.path.join(os.getcwd(), "tmp_dir")  # Change this to any path you prefer
    
    # Ensure that the base temp folder exists
    if not os.path.exists(base_tmp_path):
        os.makedirs(base_tmp_path)
        print(f"Base temporary folder '{base_tmp_path}' created.")
    
    # Define the path for the specific temporary folder
    folder_path = os.path.join(base_tmp_path, folder_name)
    
    # Create the specific temporary folder if it doesn't exist
    os.makedirs(folder_path, exist_ok=True)
    
    print(f"Temporary folder '{folder_name}' is ready at {folder_path}.")
    
    return folder_path



from moviepy.editor import *


import os
import tempfile
from moviepy.editor import AudioFileClip, ImageClip, concatenate_videoclips


def generate_video(audio_file, images, segments):
    try:
        video_dir = "./generated_videos"
        os.makedirs(video_dir, exist_ok=True)

        file_extension = os.path.splitext(audio_file.name)[1]
        temp_audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=f"{file_extension}")
        temp_audio_path.write(audio_file.read())
        temp_audio_path.close()

        audio = AudioFileClip(temp_audio_path.name)

        frame_width = 1280
        frame_height = 720

        video_clips = []
        total_segments = len(segments)
        for i, current_segment in enumerate(segments):
            start_time = current_segment["start"]
            end_time = current_segment["end"]

            if i < total_segments - 1:
                next_segment = segments[i + 1]
                actual_end_time = next_segment["start"]
            else:
                actual_end_time = end_time

            segment_duration = actual_end_time - start_time

            image_path = images[min(i, len(images) - 1)]

            image_clip = ImageClip(image_path)
            image_clip = image_clip.resize(height=frame_height).on_color(
                size=(frame_width, frame_height),
                color=(0, 0, 0),
                pos="center"
            )

            image_clip = image_clip.set_duration(segment_duration).set_start(start_time)
            video_clips.append(image_clip)

        video = concatenate_videoclips(video_clips, method="compose")
        video = video.set_audio(audio)

        video_filename = f"generated_video_{uuid.uuid4().hex}.mp4"
        video_path = os.path.join(video_dir, video_filename)

        video.write_videofile(video_path, fps=30, codec="libx264", audio_codec="aac")
        os.remove(temp_audio_path.name)

        return video_path

    except Exception as e:
        print(f"Error generating video: {e}")
        return None