Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+import tempfile
+import torchaudio
+
+# Load Whisper for transcription
+asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
+
+# Load grammar scoring model
+cola_model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-CoLA")
+cola_tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-CoLA")
+grammar_pipeline = pipeline("text-classification", model=cola_model, tokenizer=cola_tokenizer)
+
+# Load grammar correction model
+correction_pipeline = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
+
+def process_audio(audio_file):
+    # Save uploaded file to temporary path
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+        tmp.write(audio_file.read())
+        tmp_path = tmp.name
+
+    # Transcription
+    transcription = asr_pipeline(tmp_path)["text"]
+
+    # Grammar Scoring
+    grammar_result = grammar_pipeline(transcription)[0]
+    score_label = grammar_result["label"]
+    score_confidence = grammar_result["score"]
+
+    # Correction
+    corrected_text = correction_pipeline(transcription, max_length=128)[0]["generated_text"]
+
+    return transcription, f"{score_label} ({score_confidence:.2f})", corrected_text
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="file", label="Upload your .wav file"),
+    outputs=[
+        gr.Textbox(label="Transcription"),
+        gr.Textbox(label="Grammar Score"),
+        gr.Textbox(label="Grammar Correction")
+    ],
+    title="🎙️ Voice Grammar Scorer",
+    description="Upload your voice (WAV file). This app transcribes it, scores grammar, and suggests corrections."
+)
+
+if __name__ == "__main__":
+    interface.launch()