diff --git a/main3.py b/main3.py
new file mode 100644
index 0000000..63b6c98
--- /dev/null
+++ b/main3.py
@@ -0,0 +1,53 @@
+from speechlib import Transcriptor
+from configparser import ConfigParser
+import os
+import streamlit as st
+import tempfile
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+def str2bool(v):
+  return v.lower() in ("yes", "true", "t", "1")
+
+config = ConfigParser()
+config.read('config.ini')
+audiofolder = config.get('FILE', 'audiofolder')
+access_token = config.get('FILE', 'accesstoken')
+voicefolder = config.get('FILE', 'voicefolder')
+outputfolder2 = config.get('FILE', 'outputfolder2')
+language = "id"
+quantization = str2bool(config.get('FILE', 'quantization'))
+modelSize = "medium"
+
+st.title("Sales Transcription")
+
+audio = st.file_uploader("Upload audio file (.mp3/.wav/.m4a)", type=["mp3", "wav", "m4a"])
+crew_id = st.text_input("Sales ID")
+crew_name = st.text_input("Sales Name")
+customer_name = st.text_input("Customer Name")
+date = st.date_input("Date")
+if st.button("Transcribe"):
+    if audio is not None and crew_id:
+        with st.spinner("Transcribing..."):
+            # Save the uploaded audio file to a temporary location
+            file_details = {"filename": audio.name, "filetype": audio.type, "filesize": audio.size}
+            print(file_details)
+            
+            temp_dir = tempfile.mkdtemp()
+            path = os.path.join(temp_dir, audio.name)
+            
+            with open(path, "wb") as f:
+                f.write(audio.getvalue())
+            print(f"Temporary file saved at: {path}")
+            
+            #audiofilewithoutextension = path.split(".mp3")[0].split(".wav")[0].split(".m4a")[0]
+            #filepath = os.path.join(outputfolder, os.path.basename(audiofilewithoutextension).split('/')[-1]+".txt")
+            filepath = os.path.join(outputfolder2, f"{crew_id}-{crew_name}-{customer_name}-{date}-Transkrip.txt")
+            print(f"Output file: {filepath}")
+            filename = open(filepath, "w")
+
+            ### transcribe ###
+            transcriptor = Transcriptor(path, filepath, language, modelSize, access_token, voicefolder, quantization)
+            res = transcriptor.faster_whisper()
+            print(f"Content has been written to {filepath}")
+            st.success(f"Transcribe successful!")
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