Update app.py

app.py

@@ -16,10 +16,10 @@ import shlex
 # Define available Whisper models
 whisper_models = {
     "Tiny (Fast, Less Accurate)": "tiny",
-    "Base (Faster, Moderate Accuracy)": "base",
-    "Small (Moderate Speed, Good Accuracy)": "small",
-    "Medium (Slower, High Accuracy)": "medium",
-    "Large (Slow, Very High Accuracy)": "large",
+    "Base (Medium Speed, Medium Accuracy)": "base",
+    "Small (Good Speed, Good Accuracy)": "small",
+    "Medium (Slow, High Accuracy)": "medium",
+    "Large (Very Slow, Highest Accuracy)": "large"
 }
 
 # Load M2M100 translation model for different languages
@@ -184,7 +184,7 @@ def write_ppt(transcription, output_file, tokenizer=None, translation_model=None
     ppt.save(output_file)
 
 # Transcribing video and generating output
-def transcribe_video(video_file, language, target_language, output_format, model_name):
+def transcribe_video(video_file, language, target_language, model_name, output_format):
     actual_model_name = whisper_models[model_name]  # Map user selection to model name
     model = whisper.load_model(actual_model_name)  # Load the selected model
 
@@ -230,7 +230,7 @@ def transcribe_video(video_file, language, target_language, output_format, model
     else:
         raise ValueError("Invalid output format selected.")
 
-# Gradio interface
+# Gradio Interface setup
 iface = gr.Interface(
     fn=transcribe_video,
     inputs=[
@@ -250,5 +250,5 @@ iface = gr.Interface(
     live=False
 )
 
-if __name__ == "__main__":
-    iface.launch()
+# Run the interface
+iface.launch(share=True)