import time
import torch
import requests
import gradio as gr
from transformers import pipeline
from usellm import Message, Options, UseLLM
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor1 = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
model1 = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")

tokenizer1 = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large-PubMedQA", add_special_tokens=False)
model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large-PubMedQA")#.to('cuda:0')


def text_to_speech(text_input):
    CHUNK_SIZE = 1024
    url = "https://api.elevenlabs.io/v1/text-to-speech/TxGEqnHWrfWFTfGW9XjX"
    
    headers = {
        "Accept": "audio/mpeg",
        "Content-Type": "application/json",
        "xi-api-key": "7f91dfdd5390bbfd9d44148c59644039"
    }
    
    data = {
        "text": text_input,
        "model_id": "eleven_monolingual_v1"
    }
    
    audio_write_path = f"""output_{int(time.time())}.mp3"""
    
    response = requests.post(url, json=data, headers=headers)
    with open(audio_write_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
            if chunk:
                f.write(chunk)

    return audio_write_path


def whisper_inference(input_audio):
    
    processor1 = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
    model1 = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
    
    forced_decoder_ids = processor1.get_decoder_prompt_ids(task="translate")
    
    input_features = processor1(input_audio, sampling_rate=16000, return_tensors="pt").input_features
    predicted_ids = model1.generate(input_features, forced_decoder_ids=forced_decoder_ids)
    transcription = processor1.batch_decode(predicted_ids, skip_special_tokens=True)
    
    return transcription



def biogpt_large_infer(input_text):
    
    tokenizer1 = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large-PubMedQA", add_special_tokens=False)
    model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large-PubMedQA")#.to('cuda:0')
    
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer1)#, device="cuda:0")
    output = generator(input_text, min_length=100,max_length=1024,num_beams=5,early_stopping=True, 
                       num_return_sequences=1, do_sample=True)
    output = output[0]['generated_text']
    output = output.replace('▃','').replace('FREETEXT','').replace('TITLE','').replace('PARAGRAPH','').replace('ABSTRACT','').replace('<','').replace('>','').replace('/','').strip()
    
    return output


    
def chatgpt_infer(input_text):

    # Initialize the service
    service = UseLLM(service_url="https://usellm.org/api/llm")

    # Prepare the conversation
    messages = [
      Message(role="system", content="You are a medical assistant, which answers the query based on factual medical information only."),
      Message(role="user", content=f"Give me few points on the disease {input_text} and its treatment."),
    ]
    
    options = Options(messages=messages)

    # Interact with the service
    response = service.chat(options)
    
    return response.content


def audio_interface_demo(input_audio):
    
    en_prompt = whisper_inference(input_audio)
    
    biogpt_output = biogpt_large_infer(en_prompt)
    chatgpt_output = chatgpt_infer(en_prompt)
    
    bio_audio_output = text_to_speech(biogpt_output)
    chat_audio_output = text_to_speech(chatgpt_output)
            
    return biogpt_output, chatgpt_output, bio_audio_output, chat_audio_output


def text_interface_demo(input_text):
    
    #en_prompt = whisper_inference(input_audio)
    
    biogpt_output = biogpt_large_infer(input_text)
    chatgpt_output = chatgpt_infer(input_text)
            
    return biogpt_output, chatgpt_output


examples = [
    ["Meningitis is"],
    ["Brain Tumour is"]  
]

app = gr.Blocks()
with app:
    gr.Markdown("# **<h4 align='center'>Voice based Medical Informational Bot<h4>**")
     
    with gr.Row():
        with gr.Column():
            
            with gr.Tab("Text"):
                input_text = gr.Textbox(lines=3, value="Brain Tumour is", label="Text")
                text_button = gr.Button(value="Predict")

            with gr.Tab("Audio"):
                input_audio = gr.Audio(value="input.mp3", source="upload", type="filepath", label='Audio')
                audio_button = gr.Button(value="Predict")  
                  
    with gr.Row():
        with gr.Column():
            with gr.Tab("Output Text"):

                biogpt_output = gr.Textbox(lines=3, label="BioGpt Output")
                chatgpt_output = gr.Textbox(lines=3,label="ChatGPT Output")

            with gr.Tab("Output Audio"):

                biogpt_output = gr.Textbox(lines=3, label="BioGpt Output")
                chatgpt_output = gr.Textbox(lines=3,label="ChatGPT Output")

                audio_output1 = gr.Audio(value=None, label="ChatGPT Audio Output")
                audio_output2 = gr.Audio(value=None, label="BioGpt Audio Output")
                    
    #gr.Examples(examples, inputs=[input_text], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=False)
    text_button.click(text_interface_demo, inputs=[input_text], outputs=[biogpt_output, chatgpt_output])
    audio_button.click(audio_interface_demo, inputs=[input_audio], outputs=[biogpt_output, chatgpt_output, audio_output2, audio_output1])
    
app.launch(debug=True)