Update app.py

app.py

@@ -16,93 +16,93 @@ def get_local_ip():
         print(f"Error getting local IP: {e}")
         return None
 
-local_ip = get_local_ip()
-if local_ip:
-    print(f"Local IP Address: {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"):
+      local_ip = get_local_ip()
+      if local_ip:
+         print(f"Local IP Address: {local_ip}")
+      else:
+         print("Local IP could not be determined.")
+      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()