File size: 3,701 Bytes
03d4021 ed9bd4b 03d4021 ed9bd4b 78a6e57 ed9bd4b 78a6e57 03d4021 78a6e57 0c910e6 78a6e57 03d4021 78a6e57 03d4021 5cb4388 0c910e6 b2acaa4 5cb4388 0c910e6 78a6e57 03d4021 51926bb |
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 |
import gradio as gr
import openai
import os
#import torch
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#print("Device type:", device)
#from transformers import pipeline
#from transformers import (
# AutoTokenizer,
# WhisperProcessor,
# WhisperForConditionalGeneration,
#)
api_key = os.getenv('OPEN_API_KEY')
openai.api_key = api_key
global_history = [{"role": "assistant", "content": "Hi, I am a chatbot. I can converse in English. I can answer your questions about farming in India. Ask me anything!"}]
from pydub import AudioSegment
def get_asr_model_processor(model_id):
processor = WhisperProcessor.from_pretrained(model_id,model_max_length=225)
model = WhisperForConditionalGeneration.from_pretrained(model_id).to(device)
# model.forced_decoder_ids =None
model.config.max_new_token = 200
return {
"model": model,
"processor": processor
}
#model_proc_dict = get_asr_model_processor("vasista22/whisper-hindi-large-v2")
#asr_pipe = pipeline("automatic-speech-recognition", model=model_proc_dict["model"], tokenizer=model_proc_dict["processor"].tokenizer, feature_extractor=model_proc_dict["processor"].feature_extractor,device=device)
def get_asr_output(audio_path,lang):
audio = AudioSegment.from_file(audio_path)
audio.export("temp.wav", format="wav")
file = open("temp.wav","rb")
transcription = openai.Audio.transcribe("whisper-1", file, language=lang)
op_text = transcription.text
""" if lang == "hi":
op_text = asr_pipe("temp.wav")['text']
print('whisper',transcription)
print('ai4b',op_text) """
return op_text
def add_text(history, audio_path,lang):
global global_history
text = get_asr_output(audio_path,lang_dict[lang])
history = history + [(text, None)]
global_history = global_history+[{"role": "user", "content": text}]
print(global_history)
return history, ""
def get_chatgpt_response(history):
output = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=history)
history.append({"role": "assistant", "content": output.choices[0].message.content})
return output.choices[0].message.content, history
def bot(history):
global global_history
response, global_history = get_chatgpt_response(global_history)
history[-1][1] = response
return history
def clear_history(lang = "English"):
global global_history
global_history = [{"role": "assistant", "content": "Hi, I am a chatbot. I can converse in {}. I can answer your questions about farming in India. Ask me anything!".format(lang)}]
return None
lang_dict = {
"English": "en",
"Hindi": "hi",
"Bengali": "bn",
"Gujarati": "gu",
"Kannada": "kn",
"Marathi": "mr",
"Tamil": "ta",
"Telugu": "te"
}
with gr.Blocks(title="Krishi GPT Demo") as demo:
lang = gr.Radio(list(lang_dict.keys()), label="Select a Language")
with gr.Row():
with gr.Column():
user_audio = gr.Audio(source="microphone",type="filepath",label = "Speak your query")
txt = gr.Textbox(
show_label=True,
placeholder="Enter text and press enter, or Record your Audio",
visible=False,
).style(container=False)
submit = gr.Button("Submit")
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=500)
clear = gr.Button("Clear")
submit.click(add_text, [chatbot, user_audio,lang], [chatbot, txt]).then(bot, chatbot, chatbot)
clear.click(clear_history, [lang], chatbot, queue=False)
lang.change(clear_history, [lang], chatbot, queue=False)
demo.launch(share=False) |