import gradio as gui
import peft
from peft import LoraConfig
from transformers import AutoTokenizer,BitsAndBytesConfig, AutoModelForCausalLM, CLIPVisionModel, AutoProcessor
import torch
from peft import PeftModel
import torch.nn as nn
import whisper
import os

clip_model_name = "openai/clip-vit-base-patch32"
phi_model_name  = "microsoft/phi-2"
tokenizer  = AutoTokenizer.from_pretrained(phi_model_name, trust_remote_code=True)
processor  = AutoProcessor.from_pretrained(clip_model_name)
tokenizer.pad_token = tokenizer.eos_token
IMAGE_TOKEN_ID = 23893 # token for word comment
QA_TOKEN_ID = 50295 # token for qa
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_embed = 768
phi_embed  = 2560
audio_batch_size = 16
current_dir = os.getcwd()

class SimpleResBlock(nn.Module):
    def __init__(self, phi_embed):
        super().__init__()
        self.pre_norm = nn.LayerNorm(phi_embed)
        self.proj = nn.Sequential(
            nn.Linear(phi_embed, phi_embed),
            nn.GELU(),
            nn.Linear(phi_embed, phi_embed)
        )
    def forward(self, x):
        x = self.pre_norm(x)
        return x + self.proj(x)
        
# models
clip_model = CLIPVisionModel.from_pretrained(clip_model_name).to(device)
projection = torch.nn.Linear(clip_embed, phi_embed).to(device)
resblock = SimpleResBlock(phi_embed).to(device)
phi_model = AutoModelForCausalLM.from_pretrained(phi_model_name,trust_remote_code=True).to(device)
audio_model = whisper.load_model("tiny", device=device)

lora_adaptor_path = os.path.join(current_dir, 'model_chkpt', 'lora_adaptor')
projection_path = os.path.join(current_dir, 'model_chkpt', 'step2_projection.pth')
resblock_path = os.path.join(current_dir, 'model_chkpt', 'step2_resblock.pth')

# load weights
model_to_merge = PeftModel.from_pretrained(phi_model,lora_adaptor_path, local_files_only=True, device_map={'': device})

merged_model   = model_to_merge.merge_and_unload()
projection.load_state_dict(torch.load(projection_path,map_location=torch.device(device)))
resblock.load_state_dict(torch.load(resblock_path,map_location=torch.device(device)))

def generate_response(img=None,img_audio=None,val_q=None):

    max_generate_length = 100
    val_combined_embeds = []
    
    with torch.no_grad():
    
        # image
        if img is not None:
            image_processed  = processor(images=img, return_tensors="pt").to(device)
            clip_val_outputs = clip_model(**image_processed).last_hidden_state[:,1:,:]
            val_image_embeds = projection(clip_val_outputs)
            val_image_embeds = resblock(val_image_embeds).to(torch.float16)
            
            img_token_tensor = torch.tensor(IMAGE_TOKEN_ID).to(device)
            img_token_embeds = merged_model.model.embed_tokens(img_token_tensor).unsqueeze(0).unsqueeze(0)

            val_combined_embeds.append(val_image_embeds)
            val_combined_embeds.append(img_token_embeds)

        # audio
        if img_audio is not None:
            audio_result = audio_model.transcribe(img_audio)
            audio_text = ''
            for seg in audio_result['segments']:
                audio_text += seg['text']
            audio_text = audio_text.strip()
            audio_tokens = tokenizer(audio_text, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
            audio_embeds    = merged_model.model.embed_tokens(audio_tokens).unsqueeze(0)
            val_combined_embeds.append(audio_embeds)
            
        # text question
        if len(val_q) != 0:
            val_q_tokenised = tokenizer(val_q, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0).to(device)
            val_q_embeds    = merged_model.model.embed_tokens(val_q_tokenised).unsqueeze(0)
            val_combined_embeds.append(val_q_embeds)


        if img_audio is not None or len(val_q) != 0: # add QA Token
            
            QA_token_tensor = torch.tensor(QA_TOKEN_ID).to(device)
            QA_token_embeds = merged_model.model.embed_tokens(QA_token_tensor).unsqueeze(0).unsqueeze(0)
            val_combined_embeds.append(QA_token_embeds)
            
        val_combined_embeds = torch.cat(val_combined_embeds,dim=1)
        predicted_caption = merged_model.generate(inputs_embeds=val_combined_embeds,
                                                  max_new_tokens=max_generate_length,
                                                  return_dict_in_generate = True)
    
        predicted_captions_decoded = tokenizer.batch_decode(predicted_caption.sequences[:, 1:])[0] 
        predicted_captions_decoded = predicted_captions_decoded.replace("<|endoftext|>", "")  
    
    return predicted_captions_decoded
    

# Gradio interface setup with added styling
with gui.Blocks() as app_interface:
    
    with gui.Row():
        with gui.Column():
            image_input = gui.Image(label='Upload Image', type="pil")
        with gui.Column():
            audio_input = gui.Audio(label="Audio Input", sources=['microphone', 'upload'], type='filepath')
            text_input = gui.Text(label='Enter Text', placeholder="Type your query here...")
    with gui.Row():
        output_response = gui.Textbox(label='Generated Response', placeholder="Response will appear here...", lines=5)
    submit_button = gui.Button("Generate Response", variant="primary")
    submit_button.click(generate_response, inputs=[image_input, audio_input, text_input], outputs=output_response)

if __name__ == "__main__":
    app_interface.launch(share=True)