Update app.py

app.py

@@ -1,3 +1,35 @@
 import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import load_dataset
+import torch
 
-gr.load("models/openai/whisper-large-v3-turbo").launch()
+# Load Whisper model and processor
+model_name = "openai/whisper-large-v3-turbo"
+processor = WhisperProcessor.from_pretrained(model_name)
+model = WhisperForConditionalGeneration.from_pretrained(model_name)
+
+# Load dataset (bigcode/the-stack)
+dataset = load_dataset("bigcode/the-stack", data_dir="data/html")
+
+def transcribe(audio):
+    # Process audio for transcription
+    audio_input = processor(audio, return_tensors="pt").input_values
+    with torch.no_grad():
+        logits = model(audio_input).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+    
+    # Return the transcription
+    return transcription[0]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title="Whisper Transcription for Developers",
+    description="Transcribe developer-related terms using Whisper and bigcode dataset for contextual support."
+)
+
+# Launch the Gradio app
+iface.launch()