Delete app-backup.py

app-backup.py

@@ -1,48 +0,0 @@
-
-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()
-
-