Create app.py

app.py

@@ -0,0 +1,48 @@
+
+import gradio as gr
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+# Load Whisper for ASR
+asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
+
+# Load Grammar Scoring Model (CoLA)
+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 (T5)
+correction_pipeline = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
+
+def process_audio(audio):
+    if audio is None:
+        return "No audio provided.", "", ""
+
+    # Step 1: Transcription
+    transcription = asr_pipeline(audio)["text"]
+
+    # Step 2: Grammar Scoring
+    score_output = grammar_pipeline(transcription)[0]
+    label = score_output["label"]
+    confidence = score_output["score"]
+
+    # Step 3: Grammar Correction
+    corrected = correction_pipeline(transcription, max_length=128)[0]["generated_text"]
+
+    return transcription, f"{label} ({confidence:.2f})", corrected
+
+demo = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="🎤 Speak or Upload Audio (.wav)"),
+    outputs=[
+        gr.Textbox(label="📝 Transcription"),
+        gr.Textbox(label="✅ Grammar Score"),
+        gr.Textbox(label="✍️ Grammar Correction")
+    ],
+    title="🎙️ Voice Grammar Scorer",
+    description="Record or upload a WAV file. This app transcribes your voice, scores its grammar, and suggests corrections.",
+)
+
+if __name__ == "__main__":
+    demo.launch()
+
+