Update app.py

app.py

@@ -2,19 +2,7 @@ from transformers import pipeline
 asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
 from difflib import SequenceMatcher
 import json
-import socket
 
-def get_local_ip():
-    try:
-        # Create a socket connection to a remote host (here, google.com)
-        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-        s.connect(("8.8.8.8", 80))
-        local_ip = s.getsockname()[0]
-        s.close()
-        return local_ip
-    except Exception as e:
-        print(f"Error getting local IP: {e}")
-        return None
 
 
 
@@ -49,28 +37,7 @@ def find_most_similar_command(statement, command_list):
 
     return best_match,reply
 
-transcript_only=["1","3","4"]
-col_names={'1':"name",'3':"address",'4':"order"}
-def send_data_to_db(menu_id,col_value,order_id):
-    import requests
-    col_name=col_names[menu_id]
-    # API endpoint URL
-    url = 'https://pizzahut.softinfix.tech/api/save_order?'+col_name+'='+col_value+"&order_id"+"="+order_id
-    payload = {}
-    headers = {}
-
-    response = requests.request("GET", url, headers=headers, data=payload)
-
-    # Print response
-    print(response.status_code)
-    print(response.text)
-
-def transcribe_the_command(audio,menu_id,order_id):
-      local_ip = get_local_ip()
-      if local_ip:
-         print(f"Local IP Address: {local_ip}")
-      else:
-         print("Local IP could not be determined.")
+def transcribe_the_command(audio,menu_id):
       import soundfile as sf
       sample_rate, audio_data = audio
       file_name = "recorded_audio.wav"
@@ -79,25 +46,20 @@ def transcribe_the_command(audio,menu_id,order_id):
       print(menu_id)
 
       transcript = asr_pipe(file_name)["text"]
-      if menu_id in transcript_only:
-        col_value=transcript
-        send_data_to_db(menu_id,col_value,order_id)
-        print("data uploaded successfully!")
-      else:
-        commands=urdu_data[menu_id]
-        print(commands)
-        most_similar_command,reply = find_most_similar_command(transcript, commands)
-        print(f"Given Statement: {transcript}")
-        print(f"Most Similar Command: {most_similar_command}\n")
-        print(reply)
-        return reply
+      commands=urdu_data[menu_id]
+      print(commands)
+      most_similar_command,reply = find_most_similar_command(transcript, commands)
+      print(f"Given Statement: {transcript}")
+      print(f"Most Similar Command: {most_similar_command}\n")
+      print(reply)
+      return reply
 # get_text_from_voice("urdu.wav")
 import gradio as gr
 
 
 iface = gr.Interface(
     fn=transcribe_the_command,
-    inputs=[gr.inputs.Audio(label="Recorded Audio",source="microphone"),gr.inputs.Textbox(label="menu_id"),gr.inputs.Textbox(label="order_id")],
+    inputs=[gr.inputs.Audio(label="Recorded Audio",source="microphone"),gr.inputs.Textbox(label="menu_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.",