app.py

import os
import google.generativeai as genai
import gradio as gr
import requests
from moviepy.editor import AudioFileClip, ImageClip, CompositeVideoClip

# Configure Google Gemini API
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))

# Play.ht API keys
API_KEY = os.getenv('PLAY_API_KEY')
USER_ID = os.getenv('PLAY_USER_ID')

# Theme selection
theme = gr.themes.Base(
    primary_hue="emerald",
)

# Function to upload image to Gemini and get roasted text
def upload_to_gemini(path, mime_type="image/jpeg"):
    file = genai.upload_file(path, mime_type=mime_type)
    return file

def generate_roast(image_path):
    try:
        uploaded_file = upload_to_gemini(image_path)
        generation_config = {
            "temperature": 1,
            "top_p": 0.95,
            "top_k": 40,
            "max_output_tokens": 8192,
            "response_mime_type": "text/plain",
        }
        model = genai.GenerativeModel(
            model_name="gemini-1.5-flash-002",
            generation_config=generation_config,
            system_instruction="You are a professional satirist and fashion expert. Roast the provided profile picture in less than 50 words.",
        )
        
        chat_session = model.start_chat(
            history=[{"role": "user", "parts": [uploaded_file]}]
        )
        response = chat_session.send_message("Roast this image!")
        return response.text
    except Exception as e:
        return f"Error generating roast: {e}"

# Function to convert text to speech with Play.ht
def text_to_speech(text):
    try:
        url = "https://api.play.ht/api/v2/tts/stream"
        payload = {
            "voice": "s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
            "output_format": "mp3",
            "text": text,
        }
        headers = {
            "accept": "audio/mpeg",
            "content-type": "application/json",
            "Authorization": API_KEY,
            "X-User-ID": USER_ID
        }
        
        response = requests.post(url, json=payload, headers=headers)
        if response.status_code == 200:
            audio_path = "output_audio.mp3"
            with open(audio_path, "wb") as audio_file:
                audio_file.write(response.content)
            return audio_path
        else:
            return f"Error generating audio: {response.status_code} - {response.text}"
    except Exception as e:
        return f"Error generating audio: {e}"

# Function to create video from image, audio, and add logo overlay
def create_video(image, audio):
    try:
        # Load the audio file
        audio_clip = AudioFileClip(audio)
        
        # Load the main image and set its duration to match the audio
        image_clip = ImageClip(image).set_duration(audio_clip.duration)
        
        # Load the logo image, resize it, and position it in the top-right corner
        logo = ImageClip("Logo.png").resize(height=50)  # Adjust the height as needed
        logo = logo.set_position(("right", "top")).set_duration(audio_clip.duration)
        
        # Create a composite video with the main image and the logo overlay
        video_clip = CompositeVideoClip([image_clip, logo]).set_audio(audio_clip)
        
        # Save the video to a temporary file
        output_path = "/tmp/output_video_with_logo.mp4"
        video_clip.write_videofile(output_path, fps=30)
        
        return output_path
    except Exception as e:
        return f"Error generating video: {e}"

# Function to process all steps at once
def process_roast(image_path):
    roast_text = generate_roast(image_path)
    audio_path = text_to_speech(roast_text)
    video_path = create_video(image_path, audio_path)
    return roast_text, audio_path, video_path

# Gradio Interface
with gr.Blocks(theme=theme) as demo:
    gr.Markdown("# Image Roasting App with TTS and Video")
    gr.Markdown("Upload an image, click 'Roast Image', and the AI will roast it, convert the roast to audio, and generate a video with a logo overlay.")

    with gr.Row():
        image_input = gr.Image(type="filepath", label="Upload Image")
        
        with gr.Column():
            output_text = gr.Textbox(label="Roast Text")
            audio_output = gr.Audio(label="Roast Audio")
            video_output = gr.Video(label="Roast Video")

    # Single button to handle all actions
    roast_button = gr.Button("Roast Image")
    roast_button.click(process_roast, inputs=image_input, outputs=[output_text, audio_output, video_output])

# Launch the app
demo.launch(debug=True)