update app.py

app.py

@@ -1,10 +1,4 @@
-
-
-
 import whisper
-
-
-
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
@@ -13,10 +7,7 @@ import gradio as gr
 Asr_model = whisper.load_model("base")
 Asr_model.device
 
-
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
-
 model_name = "ai4bharat/Airavata"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
@@ -60,7 +51,6 @@ def inference(input_prompt, model, tokenizer):
     return output_text
 
 
-
 def transcribe(audio):
     
     #time.sleep(3)
@@ -91,6 +81,5 @@ demo = gr.Interface(
     title = 'CAMAI - Centralized Actionable Multimodal Agri Assistant on Edge Intelligence for Farmers ', 
     fn=chat_interface, 
     gr.inputs.Audio(source="microphone", type="filepath"),
-    outputs=["textbox"],
-    live=True)
+    outputs=["textbox"] )
 demo.launch()