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| import torch | |
| import gradio as gr | |
| import speech_recognition as sr | |
| import pyttsx3 | |
| import time | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| from happytransformer import HappyTextToText, TTSettings # Using HappyTransformer | |
| # Load models only once for efficiency | |
| def load_models(): | |
| model_name = "prithivida/grammar_error_correcter_v1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| happy_tt = HappyTextToText("T5", "prithivida/grammar_error_correcter_v1") # Using T5-based model | |
| return tokenizer, model, happy_tt | |
| tokenizer, model, happy_tt = load_models() # Load models at startup | |
| # Speech-to-text conversion | |
| def transcribe_audio(audio): | |
| recognizer = sr.Recognizer() | |
| with sr.AudioFile(audio) as source: | |
| audio_data = recognizer.record(source) | |
| try: | |
| text = recognizer.recognize_google(audio_data) | |
| return text | |
| except sr.UnknownValueError: | |
| return "Could not understand the audio." | |
| except sr.RequestError as e: | |
| return f"Speech recognition error: {e}" | |
| # Grammar correction function | |
| def correct_grammar(text): | |
| if not text.strip(): | |
| return "No input provided.", 0, "No correction available." | |
| inputs = tokenizer.encode("gec: " + text, return_tensors="pt", max_length=128, truncation=True) | |
| with torch.no_grad(): | |
| outputs = model.generate(inputs, max_length=128, num_return_sequences=1) | |
| corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| args = TTSettings(num_beams=5, min_length=1) | |
| correction = happy_tt.generate_text("gec: " + text, args=args).text # Better correction method | |
| grammar_score = 100 - abs(len(text) - len(correction)) # Scoring based on text change ratio | |
| return corrected_text, grammar_score, correction | |
| # Unified function for both speech and text input | |
| def process_input(audio, text): | |
| if audio: | |
| text = transcribe_audio(audio) # If audio is provided, transcribe it | |
| return correct_grammar(text) | |
| # Gradio UI | |
| def main(): | |
| iface = gr.Interface( | |
| fn=process_input, | |
| inputs=[ | |
| gr.Audio(sources=["microphone"], type="filepath", label="Speak your sentence"), | |
| gr.Textbox(placeholder="Or type here if not speaking...", label="Text Input"), | |
| ], | |
| outputs=["text", "number", "text"], | |
| title="AI Grammar Checker", | |
| description="Speak or type a sentence to check its grammar, get corrections, and see a score.", | |
| live=False, # Only processes when user submit | |
| api_name = "/predict", | |
| ) | |
| iface.launch() | |
| if __name__ == "__main__": | |
| main() |