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| import gradio as gr | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import json | |
| import os | |
| import torch | |
| data_dict = {} | |
| with open('./intents.json', 'r') as file: | |
| data = json.load(file) | |
| intents_dict = data | |
| tokenizer = AutoTokenizer.from_pretrained("roberta-base") | |
| model = AutoModelForSequenceClassification.from_pretrained("./") | |
| def preprocess(text): | |
| inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") | |
| return inputs | |
| def postprocess(outputs): | |
| logits = outputs.logits | |
| predicted_labels = logits.argmax(dim=1).tolist() | |
| return predicted_labels | |
| def predict(text): | |
| inputs = preprocess(text) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| predicted_labels = postprocess(outputs) | |
| ans = intents_dict[str(predicted_labels[0])] | |
| return ans | |
| from transformers import pipeline | |
| p = pipeline(model="openai/whisper-medium") | |
| def transcribe(text,audio): | |
| if audio: | |
| t = p(audio)['text'] | |
| ans = predict(t) | |
| elif text: | |
| ans = predict(text) | |
| else: | |
| ans = "please give input" | |
| return ans | |
| get_intent = gr.Interface(fn = transcribe, | |
| inputs=[gr.Textbox(label="Enter Text Input", type="text"),gr.Audio(source="microphone", type="filepath")], | |
| outputs="text").launch() | |