import torch
import torch.nn as nn
import torch.nn.functional as F
import whisperx
from transformers import  AutoTokenizer
from transformers import AutoModelForCausalLM
from transformers import CLIPVisionModel, CLIPImageProcessor
import peft
import gradio as gr 

device = 'cpu'

user = "VarunSivamani"  
model_name = "QLoRA-phi2"
model_id = f"{user}/{model_name}"

model_name = "microsoft/phi-2"
phi2_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True, 
    device_map = 'cpu'
)
phi2_model.config.use_cache = False

whisper_model = whisperx.load_model('small', device='cpu', compute_type='float32')
image_processor = CLIPImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
clip_model = CLIPVisionModel.from_pretrained('openai/clip-vit-base-patch32')

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.bos_token = tokenizer.eos_token

def text_to_embeddings(text): 
    input_tokens = tokenizer(text, return_tensors="pt", return_attention_mask=False)    
    return phi2_model.get_input_embeddings()(input_tokens.input_ids)

def audio_to_text_embeds(file_name): 
    result = whisper_model.transcribe(file_name)
    res_text = ''

    for segment in result['segments']: 
        res_text = res_text + segment['text']

    return res_text.strip()

def select_features(image_out):
    image_features = image_out.hidden_states[-1]
    return image_features[:, 1:, :]

def CLIP_embeddings(image):
    _ = clip_model.requires_grad_(False) 
    image = image_processor(images=image, return_tensors="pt")
    image_out = clip_model(image['pixel_values'].to(device=clip_model.device), output_hidden_states=True)
    return select_features(image_out)

    
class ResBlock(nn.Module):
    def __init__(self, input_size):
        super().__init__()
        self.pre_norm = nn.LayerNorm(input_size)
        self.proj = nn.Sequential(
            nn.Linear(input_size, input_size),
            nn.GELU(),
            nn.Linear(input_size, input_size)
        )

    def forward(self, x):
        x = self.pre_norm(x)
        return x + self.proj(x)

class CLIP_projection(nn.Module): 
    def __init__(
        self,  
        dim_input_CLIP = 768, 
        dim_input_Phi2 = 2560
    ):
        super(CLIP_projection, self).__init__()
        self.projection_img = nn.Linear(
            dim_input_CLIP, dim_input_Phi2, bias=False
        )
        self.resblock = ResBlock(dim_input_Phi2)

    def forward(self, x): 
        x = self.projection_img(x)
        return self.resblock(x)


proj_layer = CLIP_projection()
proj_layer.projection_img.load_state_dict(torch.load("proj.pth", map_location='cpu'))
proj_layer.resblock.load_state_dict(torch.load("block.pth", map_location='cpu'))


def img_embeddings(image): 
    clip_embeddings = CLIP_embeddings(image)
    return proj_layer(clip_embeddings)

phi2_model_peft = peft.PeftModel.from_pretrained(phi2_model, model_id)

def multimodal_phi2(image=None, audio=None, text=None):
    if len(text) == 0: 
        text = None 

    if image is None and audio is None and text is None: 
        return None
    
    context = tokenizer("Context: ", return_tensors="pt", return_attention_mask=False)
    input_embeds = phi2_model_peft.get_input_embeddings()(context.input_ids)
    
    if image is not None: 
        query = text
        image_embeds = img_embeddings(image)
        input_embeds = torch.cat((input_embeds, image_embeds), dim=1)
    
    if audio is not None: 
        audio_transcribed = audio_to_text_embeds(audio)
        audio_embeds = text_to_embeddings(audio_transcribed)
        input_embeds = torch.cat((input_embeds, audio_embeds), dim=1)

    if text is not None: 
        query = text
        text_embeds = text_to_embeddings(text)
        input_embeds = torch.cat((input_embeds, text_embeds), dim=1)

    question = tokenizer(" Question: " + query, return_tensors="pt", return_attention_mask=False)
    
    question_embeds = phi2_model_peft.get_input_embeddings()(question.input_ids)
    input_embeds = torch.cat((input_embeds, question_embeds), dim=1) 

    answer = tokenizer(" Answer: ", return_tensors="pt", return_attention_mask=False)
    answer_embeds = phi2_model_peft.get_input_embeddings()(answer.input_ids)
    input_embeds = torch.cat((input_embeds, answer_embeds), dim=1) 

    result = phi2_model_peft.generate(inputs_embeds=input_embeds, bos_token_id = tokenizer.bos_token_id)
    
    process = tokenizer.batch_decode(result)[0]
    process = process.split(tokenizer.eos_token)

    if process[0] == '': 
        return process[1]
    else: 
        return process[0]


demo = gr.Interface(
    fn=multimodal_phi2,
    inputs = [
        gr.Image(label="Image"),
        gr.Audio(label="Audio", sources=["microphone", "upload"], type="filepath"),
        gr.Textbox(label="Text"), 
    ],
    outputs = [
        gr.Textbox(label='Answer'),
    ],
)

demo.launch()