Spaces:
Runtime error
Runtime error
File size: 5,253 Bytes
7bff9b8 2134860 7bff9b8 4eff3b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
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
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) |