Upload folder using huggingface_hub

.gitattributes

@@ -1,35 +1,4 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.dylib filter=lfs diff=lfs merge=lfs -text
+*.dylibs filter=lfs diff=lfs merge=lfs -text
+ffmpeg-osx64-v4.2.2 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Ignore virtual environment
+venv/
+
+# Ignore Python cache files
+__pycache__/
+*.pyc
+
+# Ignore large files
+*.mp4
+*.dylib
+*.so
+*.a
+venv/
+
+venv/
+*.mp4
+*.log
+*.mp4

Procfile

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+web: python app.py
+

README.md

@@ -1,12 +1,6 @@
 ---
-title: Ai Dreams X
-emoji: 🦀
-colorFrom: gray
-colorTo: indigo
+title: ai-dreams-x
+app_file: gradio_interface_extended.py
 sdk: gradio
 sdk_version: 4.31.5
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+import time
+from moviepy.editor import VideoFileClip, concatenate_videoclips, AudioFileClip
+from moviepy.video.fx.all import volumex
+import os
+import datetime
+
+# Load the pipeline
+model_id = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionPipeline.from_pretrained(model_id)
+pipe = pipe.to("cpu")
+
+# Create a sample video
+sample_video_path = "sample_video.mp4"
+video_clip = VideoFileClip(sample_video_path)
+
+# Define paths
+output_directory = os.path.expanduser("~/Desktop/AI DREAMS & VISIONS/")
+
+# Ensure the output directory exists
+os.makedirs(output_directory, exist_ok=True)
+
+# Function to generate video with visualizer
+def generate_video(prompt, duration=10, frame_rate=24):
+    start_time = time.time()
+    
+    # Placeholder: Simulate video generation
+    output_video_path = os.path.join(output_directory, f"{prompt.replace(' ', '_')}.mp4")
+    video_clip = VideoFileClip(sample_video_path).subclip(0, duration)
+    
+    # Add a visualizer (simple volume visualizer as a placeholder)
+    audio_clip = video_clip.audio
+    visualizer = volumex(video_clip, 0.5)
+    final_clip = concatenate_videoclips([visualizer.set_audio(audio_clip)])
+    
+    final_clip.write_videofile(output_video_path, fps=frame_rate)
+    
+    end_time = time.time()
+    time_taken = end_time - start_time
+    estimated_time = str(datetime.timedelta(seconds=int(time_taken)))
+    
+    return output_video_path, estimated_time
+
+# Function to upload music and sync with video
+def sync_music_to_video(video_path, music_path):
+    video_clip = VideoFileClip(video_path)
+    audio_clip = AudioFileClip(music_path)
+    
+    # Sync music to video duration
+    synced_audio_clip = audio_clip.subclip(0, video_clip.duration)
+    
+    # Apply the music to the video
+    final_clip = video_clip.set_audio(synced_audio_clip)
+    synced_video_path = video_path.replace('.mp4', '_synced.mp4')
+    final_clip.write_videofile(synced_video_path)
+    
+    return synced_video_path
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI DREAMS X Video Generator")
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(label="Text Prompt")
+            duration_input = gr.Slider(minimum=1, maximum=60, step=1, label="Duration (seconds)", value=10)
+            frame_rate_input = gr.Slider(minimum=1, maximum=60, step=1, label="Frame Rate (fps)", value=24)
+            music_upload = gr.File(label="Upload Music File")
+            generate_button = gr.Button("Generate Video")
+        with gr.Column():
+            output_video = gr.Video(label="Generated Video")
+            download_link = gr.File(label="Download Video")
+            estimated_time = gr.Textbox(label="Estimated Time of Completion")
+
+    def generate_and_display(prompt, duration, frame_rate, music_file):
+        video_path, estimated_time = generate_video(prompt, duration, frame_rate)
+        if music_file:
+            video_path = sync_music_to_video(video_path, music_file.name)
+        return video_path, video_path, estimated_time
+
+    generate_button.click(generate_and_display, inputs=[text_input, duration_input, frame_rate_input, music_upload], outputs=[output_video, download_link, estimated_time])
+
+    gr.Markdown("[Contact Us](mailto:aidreams@aidreams.company) | [Follow @TheKingofJewelz](https://x.com/TheKingofJewelz)")
+
+demo.launch(share=True, server_name="0.0.0.0", server_port=7863)
+

build.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+
+set -e
+
+# Install system dependencies
+apt-get update && apt-get install -y portaudio19-dev
+
+# Install Python dependencies
+pip install -r requirements.txt
+

deploy_loop.sh

@@ -0,0 +1,87 @@
+#!/bin/bash
+
+# Define the repository and branch
+REPO_URL="https://github.com/UnseenSeven/GEMFINDER.git"
+BRANCH="main"
+APP_DIR="~/ai-dreams-x"
+
+# Function to update the app.py file to remove the deprecated import
+function fix_imports() {
+    # Navigate to the app directory
+    cd $APP_DIR
+    
+    # Ensure the correct import
+    sed -i 's/from werkzeug.urls import url_quote/from urllib.parse import quote as url_quote/' app.py
+    
+    # Stage and commit the changes
+    git add app.py
+    git commit -m "Remove deprecated Werkzeug import and use urllib.parse.quote"
+    git push origin $BRANCH
+}
+
+# Function to deploy the application
+function deploy_app() {
+    cd $APP_DIR
+    # Force a new deployment by making a dummy change
+    touch deploy_trigger.txt
+    git add deploy_trigger.txt
+    git commit -m "Trigger redeployment"
+    git push origin $BRANCH
+}
+
+# Function to check deployment status
+function check_deployment() {
+    # Check logs for specific errors
+    ERROR_LOG=$(render logs ai-dreams-x | grep "ImportError: cannot import name 'url_quote'")
+    
+    if [ -n "$ERROR_LOG" ]; then
+        echo "Found ImportError, attempting to fix..."
+        return 1
+    else
+        echo "No ImportError found. Checking for other issues..."
+        return 0
+    fi
+}
+
+# Function to clean and redeploy
+function clean_redeploy() {
+    # Clean and reinstall dependencies
+    rm -rf .venv
+    python3 -m venv .venv
+    source .venv/bin/activate
+    pip install -r requirements.txt
+
+    # Run the deployment
+    deploy_app
+}
+
+# Main loop
+while true; do
+    # Pull latest changes
+    git pull origin $BRANCH
+
+    # Fix imports
+    fix_imports
+
+    # Deploy the application
+    deploy_app
+
+    # Wait for a few seconds to let deployment finish
+    sleep 60
+
+    # Check deployment status
+    check_deployment
+    DEPLOY_STATUS=$?
+
+    if [ $DEPLOY_STATUS -eq 0 ]; then
+        echo "Deployment successful!"
+        break
+    else
+        echo "Deployment failed. Retrying..."
+        clean_redeploy
+    fi
+
+    # Wait a bit before retrying
+    sleep 30
+done
+

gradio_interface.py

@@ -0,0 +1,23 @@
+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+model_id = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionPipeline.from_pretrained(model_id)
+pipe = pipe.to("cpu")  # Use CPU instead of CUDA
+
+def generate_image(prompt):
+    image = pipe(prompt).images[0]
+    return image
+
+demo = gr.Interface(
+    fn=generate_image,
+    inputs=gr.Textbox(label="Text Prompt"),
+    outputs="image",
+    title="AI Image Generator",
+    description="Generate images from text prompts using Stable Diffusion."
+)
+
+if __name__ == "__main__":
+    demo.launch()
+

gradio_interface_extended.py

@@ -0,0 +1,69 @@
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import moviepy.editor as mp
+import torch
+import os
+from datetime import datetime
+
+def generate_video(prompt, duration, frame_rate, music_file):
+    # Initialize the pipeline
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if device == "cuda":
+        pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
+    else:
+        pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        
+    pipeline = pipeline.to(device)
+
+    # Generate frames
+    num_frames = duration * frame_rate
+    temp_dir = f"/tmp/{datetime.now().strftime('%Y%m%d%H%M%S')}"
+    os.makedirs(temp_dir, exist_ok=True)
+
+    for i in range(num_frames):
+        frame = pipeline(prompt).images[0]
+        frame_path = os.path.join(temp_dir, f"frame_{i:04d}.png")
+        frame.save(frame_path)
+
+    # Create video from frames
+    video_path = os.path.join(temp_dir, "video.mp4")
+    video_clip = mp.ImageSequenceClip(temp_dir, fps=frame_rate)
+    
+    if music_file:
+        audio_clip = mp.AudioFileClip(music_file)
+        audio_clip = audio_clip.set_duration(video_clip.duration)
+        video_clip = video_clip.set_audio(audio_clip)
+    
+    video_clip.write_videofile(video_path, codec="libx264")
+
+    return video_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Dreams & Visions Video Generator")
+    gr.Markdown("Generate stunning videos from text prompts using AI technology. For inquiries, contact us at [aidreams@aidreams.company](mailto:aidreams@aidreams.company). Follow us on X: [@TheKingofJewelz](https://x.com/TheKingofJewelz).")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Text Prompt", placeholder="Enter your video description here...")
+            duration = gr.Slider(label="Duration (seconds)", minimum=1, maximum=30, step=1, value=5)
+            frame_rate = gr.Slider(label="Frame Rate", minimum=1, maximum=60, step=1, value=24)
+            music_file = gr.Audio(label="Music File (Optional)", type="filepath")
+            generate_btn = gr.Button("Generate Video")
+
+        with gr.Column():
+            video_output = gr.Video(label="Generated Video")
+            download_link = gr.File(label="Download Video")
+
+    def generate_and_display_video(prompt, duration, frame_rate, music_file):
+        video_path = generate_video(prompt, duration, frame_rate, music_file)
+        return video_path, video_path
+
+    generate_btn.click(
+        generate_and_display_video,
+        inputs=[prompt, duration, frame_rate, music_file],
+        outputs=[video_output, download_link],
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)
+

gradio_interface_extended.py.save

@@ -0,0 +1,44 @@
+import gradio as gr
+import os
+import moviepy.editor as mp
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Load the Stable Diffusion model
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe.to("cpu")  # Change to "cuda" if you have a GPU
+
+def generate_video(prompt, duration, framerate):
+    temp_dir = "/tmp/sd_frames"
+    if not os.path.exists(temp_dir):
+        os.makedirs(temp_dir, exist_ok=True)
+    
+    # Generate frames using the prompt
+    for i in range(1, int(duration * framerate) + 1):
+        frame = pipe(prompt).images[0]
+        frame.save(f"{temp_dir}/frame_{i:04d}.png")
+    
+    # Generate video from frames
+    video = mp.ImageSequenceClip(temp_dir, fps=framerate)
+    output_path = "/tmp/sd_video.mp4"
+    video.write_videofile(output_path, codec="libx264")
+    
+    return output_path
+
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(label="Duration (seconds)", minimum=1, maximum=30, step=1, default=5),
+        gr.Slider(label="Framerate (fps)", minimum=1, maximum=60, step=1, default=30)
+    ],
+    outputs=gr.Video(label="Generated Video"),
+    title="AI Dreams & Visions Video Generator",
+    description="Generate a video based on a prompt. Enter the prompt, set the duration and framerate, and click 'Generate Video'.",
+    theme="dark",
+    css="footer {visibility: hidden}"
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)
+

pipeline_utils.py

@@ -0,0 +1,15 @@
+@classmethod
+def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    # Add debugging prints here
+    print(f"Loading model from path: {pretrained_model_name_or_path}")
+    print(f"Class: {cls}")
+    print(f"Model args: {model_args}")
+    print(f"Kwargs: {kwargs}")
+
+    load_method_name = kwargs.pop("_from_pretrained_load_method", "from_config")
+    if not isinstance(load_method_name, str):
+        raise TypeError("load_method_name must be a string")
+
+    load_method = getattr(cls, load_method_name)
+    return load_method(pretrained_model_name_or_path, *model_args, **kwargs)
+

post_deploy.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+
+# Ensure dependencies are installed
+pip install accelerate moviepy numpy
+
+# Move cache to the right location if needed
+python -c "from transformers.utils import move_cache; move_cache()"
+
+<<<<<<< HEAD
+# Function to check and download the model
+check_and_download_model() {
+  local model_name=$1
+  python -c "
+import sys
+from diffusers import StableDiffusionPipeline
+import torch
+
+try:
+    StableDiffusionPipeline.from_pretrained('${model_name}', torch_dtype=torch.float16)
+    sys.exit(0)
+except Exception as e:
+    print(f'Error: {e}', file=sys.stderr)
+    sys.exit(1)
+"
+}
+
+# Function to ensure setup is complete
+ensure_setup() {
+  install_packages
+
+  local models=("stabilityai/stable-diffusion-2-1-base" "CompVis/stable-diffusion-v1-4" "runwayml/stable-diffusion-v1-5")
+
+  for model in "${models[@]}"
+  do
+    echo "Checking model: ${model}"
+    if check_and_download_model ${model}; then
+      echo "Model ${model} is ready."
+      export MODEL_NAME=${model}
+      return 0
+=======
+# Install necessary Python packages
+pip install transformers gradio torch
+
+# Retry loop for launching the Gradio interface
+while true; do
+    python gradio_interface_extended.py
+    if [ $? -eq 0 ]; then
+        echo "Gradio interface started successfully."
+        break
+>>>>>>> cd0bb518 (Update render.yaml and post_deploy.sh for enhanced deployment)
+    else
+        echo "Gradio interface failed to start. Retrying in 10 seconds..."
+        sleep 10
+    fi
+done
+

render.yaml

@@ -0,0 +1,16 @@
+services:
+  - type: web
+    name: ai-dreams-x
+    env: python
+    runtime: python3
+    buildCommand: "pip install -r requirements.txt"
+    startCommand: "bash post_deploy.sh && python gradio_interface_extended.py"
+    region: oregon
+    plan: starter
+    disk:
+      name: persistent
+      size: 1GB
+    envVars:
+      - key: PORT
+        value: "8080"
+

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+diffusers
+moviepy
+

run_gradio.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+while true; do
+    echo "Checking and installing required packages..."
+    pip install torch torchvision torchaudio diffusers gradio --upgrade
+
+    echo "Running the gradio_interface.py script..."
+    python ~/ai-dreams-x/gradio_interface.py
+    
+    if [ $? -eq 0 ]; then
+        echo "Script ran successfully!"
+        break
+    else
+        echo "An error occurred. Retrying in 5 seconds..."
+        sleep 5
+    fi
+done
+

setup_and_run.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+while true; do
+    echo "Checking and installing required packages..."
+
+    # Install required packages
+    pip install --upgrade pip
+    pip install torch torchvision
+    pip install diffusers==0.27.2
+    pip install gradio==3.1.5
+    pip install moviepy
+    pip install opencv-python-headless==4.5.5.64
+    pip install huggingface-hub
+
+    # Run the Gradio interface
+    python ~/ai-dreams-x/gradio_interface.py
+
+    # Check if the last command was successful
+    if [ $? -eq 0 ]; then
+        echo "Gradio interface is running successfully."
+        break
+    else
+        echo "An error occurred. Retrying in 5 seconds..."
+        sleep 5
+    fi
+done
+

setup_script.sh

@@ -0,0 +1,113 @@
+#!/bin/bash
+
+# Ensure we are in the correct directory
+cd ~/ai-dreams-x
+
+# Install required Python packages
+pip install gradio torch diffusers moviepy sounddevice soundfile numpy librosa matplotlib
+
+# Create the Python script for the Gradio interface
+cat <<EOF > gradio_interface_extended.py
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+import moviepy.editor as mp
+import sounddevice as sd
+import soundfile as sf
+import numpy as np
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+from pathlib import Path
+import time
+
+# Load the model
+model_id = "runwayml/stable-diffusion-v1-5"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionPipeline.from_pretrained(model_id)
+pipe.to(device)
+
+# Function to generate video from text
+def generate_video(prompt, duration, frame_rate, audio_file, mic_input):
+    if mic_input:
+        fs = 44100  # Sample rate
+        seconds = 10  # Duration of recording
+        print("Recording audio...")
+        audio_data = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
+        sd.wait()  # Wait until recording is finished
+        audio_path = "mic_audio.wav"
+        sf.write(audio_path, audio_data, fs)
+    elif audio_file is not None:
+        audio_path = audio_file.name
+    else:
+        audio_path = None
+
+    if audio_path:
+        y, sr = librosa.load(audio_path)
+        tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
+        duration = librosa.get_duration(y=y, sr=sr)
+    else:
+        sr = 22050
+        duration = float(duration)
+
+    frames = []
+
+    start_time = time.time()
+    for i in range(int(duration * frame_rate)):
+        frame = pipe(prompt).images[0]
+        frames.append(frame)
+
+    clip = mp.ImageSequenceClip([np.array(f) for f in frames], fps=frame_rate)
+    if audio_path:
+        audio_clip = mp.AudioFileClip(audio_path)
+        video = clip.set_audio(audio_clip)
+    else:
+        video = clip
+
+    # Add visualizer
+    if audio_path:
+        waveform = np.abs(librosa.stft(y))
+        plt.figure(figsize=(10, 4))
+        librosa.display.specshow(librosa.amplitude_to_db(waveform, ref=np.max), sr=sr, x_axis='time', y_axis='log')
+        plt.colorbar(format='%+2.0f dB')
+        plt.title('Power spectrogram')
+        plt.tight_layout()
+        visualizer_path = "/Users/unseenseven/Desktop/AI_DREAMS & VISIONS/visualizer.png"
+        plt.savefig(visualizer_path)
+        plt.close()
+
+    output_path = "/Users/unseenseven/Desktop/AI_DREAMS & VISIONS/generated_video.mp4"
+    video.write_videofile(output_path, codec="libx264")
+    
+    estimated_time = time.time() - start_time
+    return output_path, f"Estimated time to completion: {estimated_time:.2f} seconds", None
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI DREAMS & VISIONS Video Generator")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Text Prompt")
+            duration = gr.Slider(minimum=1, maximum=60, label="Duration (seconds)")
+            frame_rate = gr.Slider(minimum=1, maximum=60, label="Frame Rate (fps)")
+            audio_file = gr.File(label="Upload Audio File (optional)")
+            mic_input = gr.Checkbox(label="Use Microphone Input")
+            submit = gr.Button("Generate Video")
+            email = gr.Markdown("Contact us: [aidreams@aidreams.company](mailto:aidreams@aidreams.company)")
+        with gr.Column():
+            video_preview = gr.Video(label="Generated Video Preview")
+            estimated_time_output = gr.Textbox(label="Estimated Time to Completion", interactive=False)
+            download_link = gr.File(label="Download Video")
+
+    submit.click(
+        generate_video, 
+        inputs=[prompt, duration, frame_rate, audio_file, mic_input], 
+        outputs=[video_preview, estimated_time_output, download_link]
+    )
+
+demo.launch(share=True)
+EOF
+
+# Run the Python script
+python gradio_interface_extended.py
+

start.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+set -e
+
+MAX_RETRIES=5
+RETRY_DELAY=5
+
+for i in $(seq 1 $MAX_RETRIES); do
+    python gradio_interface_extended.py && break || {
+        if [ "$i" -eq "$MAX_RETRIES" ]; then
+            echo "Reached maximum retries, exiting."
+            exit 1
+        fi
+        echo "Retrying in $RETRY_DELAY seconds..."
+        sleep $RETRY_DELAY
+    }
+done
+

templates/access.html

@@ -0,0 +1,16 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>AI Dreams X - Movie Maker</title>
+</head>
+<body>
+    <h1>Welcome to AI Dreams X</h1>
+    <form action="{{ url_for('generate') }}" method="post">
+        <label for="prompt">Enter your movie idea:</label>
+        <textarea id="prompt" name="prompt" rows="4" cols="50"></textarea>
+        <button type="submit">Generate</button>
+    </form>
+</body>
+</html>
+

templates/payment.html

@@ -0,0 +1,85 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI DREAMS X Payment</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #121212;
+            color: #ffffff;
+            text-align: center;
+            padding: 50px;
+        }
+        .container {
+            background-color: #1e1e1e;
+            border-radius: 10px;
+            padding: 20px;
+            max-width: 600px;
+            margin: auto;
+        }
+        .logo {
+            width: 100px;
+            margin-bottom: 20px;
+        }
+        .mission-statement {
+            font-size: 18px;
+            margin: 20px 0;
+        }
+        .payment-link {
+            background-color: #007bff;
+            color: #ffffff;
+            padding: 10px 20px;
+            border-radius: 5px;
+            text-decoration: none;
+            font-size: 16px;
+        }
+        .payment-link:hover {
+            background-color: #0056b3;
+        }
+        .coupon {
+            margin: 20px 0;
+        }
+        .email {
+            margin-top: 20px;
+        }
+        .message {
+            margin-top: 10px;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <img src="path/to/AI_DREAMS_X_logo.png" alt="AI DREAMS X Logo" class="logo">
+        <div class="mission-statement">
+            <p>Welcome to AI DREAMS X! Our mission is to harness the power of artificial intelligence to create stunning visual and audio experiences that transcend reality.</p>
+        </div>
+        <a href="{{ cash_app_url }}" class="payment-link">Pay with Cash App</a>
+        <div class="coupon">
+            <p>Enter coupon code for free access:</p>
+            <input type="text" id="coupon_code" placeholder="Coupon Code">
+            <button onclick="validateCoupon()">Submit</button>
+        </div>
+        <div class="message" id="message"></div>
+        <div class="email">
+            <p>Contact us: <a href="mailto:aidreams@aidreams.company">aidreams@aidreams.company</a></p>
+        </div>
+    </div>
+    <script>
+        function validateCoupon() {
+            const couponCode = document.getElementById('coupon_code').value;
+            const messageDiv = document.getElementById('message');
+            if (couponCode === 'FREEACCESS2024') {
+                messageDiv.innerHTML = '<p style="color: green;">Coupon code accepted! You now have free access.</p>';
+                setTimeout(() => {
+                    window.location.href = '/access';  // Redirect to the access page or whatever URL you use for free access
+                }, 2000);  // Delay to show the success message before redirecting
+            } else {
+                messageDiv.innerHTML = '<p style="color: red;">Invalid coupon code. Please try again.</p>';
+            }
+        }
+    </script>
+</body>
+</html>
+

templates/result.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>AI Dreams X - Movie Maker Results</title>
+</head>
+<body>
+    <h1>Generated Movie Content</h1>
+    <h2>Script:</h2>
+    <p>{{ script }}</p>
+    <h2>Scene Image:</h2>
+    <img src="{{ scene_image }}" alt="Scene Image">
+    <h2>Character Voice:</h2>
+    <audio controls>
+        <source src="{{ character_voice }}" type="audio/mpeg">
+        Your browser does not support the audio element.
+    </audio>
+    <h2>Background Music:</h2>
+    <audio controls>
+        <source src="{{ background_music }}" type="audio/mpeg">
+        Your browser does not support the audio element.
+    </audio>
+    <h2>Storyboard:</h2>
+    <video controls>
+        <source src="{{ storyboard }}" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+    <h2>Video with Effects:</h2>
+    <video controls>
+        <source src="{{ video_with_effects }}" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+    <h2>Sentiment Analysis:</h2>
+    <p>{{ sentiment }}</p>
+</body>
+</html>
+