# Import libraries
import streamlit as st
import gradio as gr
import torch
import transformers
import librosa
import cv2
import numpy as np

# Load models
text_model = transformers.pipeline("text-generation")
audio_model = transformers.Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
audio_tokenizer = transformers.Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
image_model = transformers.pipeline("image-classification")
video_model = transformers.VideoClassificationPipeline(model="facebook/mmf-vit-base-16", feature_extractor="facebook/mmf-vit-base-16")

# Define functions for processing inputs and outputs
def text_to_text(input):
  output = text_model(input, max_length=50)
  return output[0]["generated_text"]

def text_to_audio(input):
  output = text_model(input, max_length=50)
  output = gr.outputs.Audio.from_str(output[0]["generated_text"])
  return output

def text_to_image(input):
  output = text_model(input, max_length=50)
  output = gr.outputs.Image.from_str(output[0]["generated_text"])
  return output

def text_to_video(input):
  output = text_model(input, max_length=50)
  output = gr.outputs.Video.from_str(output[0]["generated_text"])
  return output

def audio_to_text(input):
  input = librosa.load(input)[0]
  input = torch.from_numpy(input).unsqueeze(0)
  logits = audio_model(input).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  output = audio_tokenizer.batch_decode(predicted_ids)[0]
  return output

def audio_to_audio(input):
  return input

def audio_to_image(input):
  input = librosa.load(input)[0]
  input = torch.from_numpy(input).unsqueeze(0)
  logits = audio_model(input).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  output = audio_tokenizer.batch_decode(predicted_ids)[0]
  output = gr.outputs.Image.from_str(output)
  return output

def audio_to_video(input):
  input = librosa.load(input)[0]
  input = torch.from_numpy(input).unsqueeze(0)
  logits = audio_model(input).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  output = audio_tokenizer.batch_decode(predicted_ids)[0]
  output = gr.outputs.Video.from_str(output)
  return output

def image_to_text(input):
  input = cv2.imread(input)
  input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
  input = np.expand_dims(input, axis=0)
  output = image_model(input)
  return output[0]["label"]

def image_to_audio(input):
  input = cv2.imread(input)
  input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
  input = np.expand_dims(input, axis=0)
  output = image_model(input)
  output = gr.outputs.Audio.from_str(output[0]["label"])
  return output

def image_to_image(input):
  return input

def image_to_video(input):
  input = cv2.imread(input)
  input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
  input = np.expand_dims(input, axis=0)
  output = image_model(input)
  output = gr.outputs.Video.from_str(output[0]["label"])
  return output

def video_to_text(input):
    input = cv2.VideoCapture(input)
    frames = []
    while input.isOpened():
        ret, frame = input.read()
        if ret:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frames.append(frame)
        else:
            breakpoint
    input.release()
    frames = np.array(frames)
    output = video_model(frames)
    return output[0]["label"]