Update app.py

app.py

@@ -1,16 +1,18 @@
 from transformers import pipeline
 asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
 from difflib import SequenceMatcher
-
+import json
+with open("tasks.json", "r") as json_file:
+    urdu_data = json.load(json_file)
 # List of commands
-commands = [
-    "نمائندے ایجنٹ نمائندہ",
-    "  سم  ایکٹیویٹ ",
-    " سم  بلاک بند ",
-    "موبائل پیکیجز انٹرنیٹ پیکیج",
-    " چالان جمع چلان",
-    " گانا "
-]
+# commands = [
+#     "نمائندے ایجنٹ نمائندہ",
+#     "  سم  ایکٹیویٹ ",
+#     " سم  بلاک بند ",
+#     "موبائل پیکیجز انٹرنیٹ پیکیج",
+#     " چالان جمع چلان",
+#     " گانا "
+# ]
 # replies = [
 # 1,2,
 # ]
@@ -29,8 +31,9 @@ def find_most_similar_command(statement, command_list):
         i+=1
 
     return best_match,reply
-def transcribe_the_command(audio):
+def transcribe_the_command(audio,id):
       import soundfile as sf
+      commands=urdu_data[id]
       sample_rate, audio_data = audio
       file_name = "recorded_audio.wav"
       sf.write(file_name, audio_data, sample_rate)
@@ -50,7 +53,7 @@ import gradio as gr
 
 iface = gr.Interface(
     fn=transcribe_the_command,
-    inputs=gr.inputs.Audio(label="Recorded Audio",source="microphone"),
+    inputs=[gr.inputs.Audio(label="Recorded Audio",source="microphone"),gr.inputs.Number(label="id")],
     outputs="text",
     title="Whisper Small Urdu Command",
     description="Realtime demo for Urdu speech recognition using a fine-tuned Whisper small model and outputting the estimated command on the basis of speech transcript.",