import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
import openai
import cv2
import numpy as np

# Load Stable Diffusion pipeline
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
if torch.cuda.is_available():
    sd_pipe.to("cuda")

# Load AI voiceover model
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")

# OpenAI API key for voice and physics interactions
openai.api_key = "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"

def generate_image(prompt, emotion):
    emotion_map = {"Happy": "smiling", "Angry": "frowning", "Sad": "crying"}
    prompt = f"{prompt}, {emotion_map.get(emotion, '')}"
    image = sd_pipe(prompt).images[0]
    return image

def generate_voiceover(text):
    inputs = processor(text, return_tensors="pt")
    speech = tts_model.generate(**inputs)
    return speech

def apply_physics(image, effect):
    img_array = np.array(image)
    if effect == "Wind":
        kernel = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]])
        img_array = cv2.filter2D(img_array, -1, kernel)
    elif effect == "Falling":
        img_array = np.roll(img_array, 10, axis=0)
    return img_array

def track_facial_expressions():
    cap = cv2.VideoCapture(0)
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    while True:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
        cv2.imshow('Face Tracking', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

def animate_scene(prompt, emotion, physics, text):
    image = generate_image(prompt, emotion)
    image = apply_physics(image, physics)
    speech = generate_voiceover(text)
    return image, speech

iface = gr.Interface(
    fn=animate_scene,
    inputs=[
        gr.Textbox(label="Character Description"),
        gr.Radio(["Happy", "Angry", "Sad"], label="Emotion"),
        gr.Radio(["None", "Wind", "Falling"], label="Physics Effect"),
        gr.Textbox(label="Dialogue")
    ],
    outputs=[
        gr.Image(label="Generated Scene"),
        gr.Audio(label="Voiceover")
    ]
)

iface.launch()