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

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()