Update app.py

app.py

@@ -22,87 +22,87 @@ if local_ip:
 else:
     print("Local IP could not be determined.")
 
-with open("tasks.json", "r",encoding="utf-8") 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 "
-# ]
-# replies = [
-# 1,2,
-# ]
-# Function to find the most similar command
-def find_most_similar_command(statement, command_list):
-    best_match = None
-    highest_similarity = 0
-    i=0
-    for sub_list in command_list:
-     for command in sub_list:
-        similarity = SequenceMatcher(None, statement, command).ratio()
-        print(i,"similarity",similarity)
-        if similarity > highest_similarity:
-            highest_similarity = similarity
-            best_match = command
-            reply=i
-     i+=1
-
-    return best_match,reply
-
-
-def send_data_to_db(order_id,col_name):
-    import requests
-
-# API endpoint URL
-    url = 'https://pizzahut.softinfix.tech/api/save_order/'+order_id
-
-# Data to send (in dictionary format)
-    data = {
-    col_name: col_value,
-    }
-
-# Send POST request with data
-    response = requests.post(url, data=data)
-
-# Print response
-    print(response.status_code)
-    print(response.text)
-
-def transcribe_the_command(audio,menu_id,order_id,db_col="0"):
-
-      import soundfile as sf
-      sample_rate, audio_data = audio
-      file_name = "recorded_audio.wav"
-      sf.write(file_name, audio_data, sample_rate)
-    # Convert stereo to mono by averaging the two channels
-      print(menu_id)
-
-      transcript = asr_pipe(file_name)["text"]
-      if menu_id == "transcript_only":
-        reply=transcript
-        print(reply)
-      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
-# 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="id"),gr.inputs.Textbox(label="col_name(optional)")],
-    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.",
-)
-
-iface.launch()
+# with open("tasks.json", "r",encoding="utf-8") 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 "
+# # ]
+# # replies = [
+# # 1,2,
+# # ]
+# # Function to find the most similar command
+# def find_most_similar_command(statement, command_list):
+#     best_match = None
+#     highest_similarity = 0
+#     i=0
+#     for sub_list in command_list:
+#      for command in sub_list:
+#         similarity = SequenceMatcher(None, statement, command).ratio()
+#         print(i,"similarity",similarity)
+#         if similarity > highest_similarity:
+#             highest_similarity = similarity
+#             best_match = command
+#             reply=i
+#      i+=1
+
+#     return best_match,reply
+
+
+# def send_data_to_db(order_id,col_name):
+#     import requests
+
+# # API endpoint URL
+#     url = 'https://pizzahut.softinfix.tech/api/save_order/'+order_id
+
+# # Data to send (in dictionary format)
+#     data = {
+#     col_name: col_value,
+#     }
+
+# # Send POST request with data
+#     response = requests.post(url, data=data)
+
+# # Print response
+#     print(response.status_code)
+#     print(response.text)
+
+# def transcribe_the_command(audio,menu_id,order_id,db_col="0"):
+
+#       import soundfile as sf
+#       sample_rate, audio_data = audio
+#       file_name = "recorded_audio.wav"
+#       sf.write(file_name, audio_data, sample_rate)
+#     # Convert stereo to mono by averaging the two channels
+#       print(menu_id)
+
+#       transcript = asr_pipe(file_name)["text"]
+#       if menu_id == "transcript_only":
+#         reply=transcript
+#         print(reply)
+#       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
+# # 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="id"),gr.inputs.Textbox(label="col_name(optional)")],
+#     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.",
+# )
+
+# iface.launch()