made a git file retriever

app.py

@@ -1,180 +1,18 @@
-import streamlit as st
-from st_audiorec import st_audiorec #pip install streamlit-audiorec
-import nemo.collections.asr as nemo_asr
-from pydub import AudioSegment
-import subprocess
-import io
-import os
-import uuid 
-# from func_timeout import func_timeout, FunctionTimedOut
-import streamlit.components.v1 as components
-import wave
-import shutil
-import contextlib
+import git
+from git import Repo
 
+GITHUB_USERNAME = "AkshatJain1402"
+GITHUB_PAT = "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"
+REPO_URL = "https://github.com/AkshatJain1402/finalASRNEMO"  
 
-def download_model(model_path, model_link, lang):
+def clone_and_execute():
+    repo = Repo.clone_from(f"{REPO_URL}", "repo_folder", auth=("token", GITHUB_PAT))  # Clone into a local folder
 
-    if not os.path.exists(model_path):
-        print("Downloading AM: ", lang)
-        download = subprocess.run(["wget","-P",model_path,model_link],capture_output=True, text=True)
-        
-        if download.returncode != 0:
-                raise Exception(lang + " Model Download Failed: {download.stderr}")
-        
-        else:
-            print('Downloaded AM: ' + lang)
+    # Import the module (assuming app.py is in the root)
+    import repo_folder.app as app
 
+    # Execute the main function
+    if __name__ == "__main__":
+        app.main() 
 
-def main():
-    @st.cache_resource
-    def get_model():
-        try:
-            os.makedirs("audio_cache")
-        except: 
-            shutil.rmtree("audio_cache")
-            os.makedirs("audio_cache")
-        
-        download_model("./hi_am_model", "https://storage.googleapis.com/vakyansh-open-models/conformer_models/hindi/filtered_v1_ssl_2022-07-08_19-43-25/Conformer-CTC-BPE-Large.nemo", "hindi")
-        download_model("./en_am_model", "https://storage.googleapis.com/vakyansh-open-models/conformer_models/english/2022-09-13_15-50-48/Conformer-CTC-BPE-Large.nemo", "english")
-
-        try:
-            en_asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from("./en_am_model/Conformer-CTC-BPE-Large.nemo") #("/home/tanmay/zb/en_Conformer-CTC-BPE-Large.nemo")
-            hi_asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from("./hi_am_model/Conformer-CTC-BPE-Large.nemo") #("/home/tanmay/zb/hi_Conformer-CTC-BPE-Large.nemo")
-        except Exception as e:
-            print("ERROR Loading Model... ",e)
-            exit (1)
-
-        return en_asr_model, hi_asr_model
-
-
-    en_asr_model, hi_asr_model = get_model()
-
-
-    def get_audio_length_wave(filename):
-        with contextlib.closing(wave.open(filename,'r')) as f:
-            frames = f.getnframes()
-            rate = f.getframerate()
-            duration = frames / float(rate)
-            return duration
-
-    def save_audio_file_using_mic(audio_location, wav_audio_data):        
-        audio_file = io.BytesIO(wav_audio_data)
-        audio = AudioSegment.from_file(audio_file)
-        audio = audio.set_sample_width(2)
-        audio = audio.set_channels(1)
-        audio = audio.set_frame_rate(16000)
-        audio.export(audio_location, format="wav")
-
-    def delete_audio_file(audio_location):
-        if os.path.exists(audio_location):
-            try:
-                os.remove(audio_location)
-                print(f"File deleted: {audio_location}")
-            except OSError as e:
-                print(f"Error deleting file: {e}")
-
-    def save_audio_file_using_upload(audio_location, uploaded_file):
-
-        if not uploaded_file.name.endswith(".wav"):
-            st.write("ERROR! File extension should be wav")
-            return 0
-    
-        # get audio file length 
-        with open(audio_location, "wb") as f:
-            f.write(uploaded_file.getvalue())
-        audioDuration = get_audio_length_wave(audio_location)
-
-        st.write("Audio Duration: ",audioDuration)
-        if audioDuration < 60 and audioDuration > 0:
-            return 1
-        else:
-            st.write('ERROR! File is more than 1 minute')
-            st.write('Uploaded File duration is restricted upto 1 minute')
-            return 0
-
-########################################################################################################################################
-# APP FUNCTIONALITY STARTS #
-########################################################################################################################################
-        
-    st.title("💬 Vocalize: Empower Your Voice ")
-    st.write("You can either try to record your own voice using a microphone or upload a small file, up to 1 minute in length, to transcribe.")
-    st.write('')
-
-    col1, ___, ____ = st.columns(3)
-
-    with col1:
-        language = st.selectbox('Select Your Preferred Language.',('English', 'Hindi'))
-
-
-    st.header("Transcribe Your Voice Using Mic")
-    wav_audio_data = st_audiorec()
-
-    if wav_audio_data:
-        
-        audio_location = "audio_cache/" + str(uuid.uuid4()) + ".wav"
-
-        save_audio_file_using_mic(audio_location, wav_audio_data)
-
-        if language == "Hindi":
-            text = hi_asr_model.transcribe([audio_location], logprobs=False)[0]
-
-        else:
-            text = en_asr_model.transcribe([audio_location], logprobs=False)[0]
-
-        print(text)
-        st.write("Transcription:")
-        st.write(text)
-
-        delete_audio_file(audio_location)
-
-
-    st.header("Transcribe Files")
-    st.write("Ensure that the file extension is .wav with a sample rate of 16,000 Hz and a single channel (mono)")
-
-    try:
-
-        uploaded_file = st.file_uploader("Upload Your Recording", disabled=False,type='')
-
-        if uploaded_file is not None:         
-
-            # Store the uploaded file:
-            audio_location = "audio_cache/" + str(uuid.uuid4()) + ".wav"
-                                        
-            flag = save_audio_file_using_upload(audio_location, uploaded_file)
-            if flag == 1:
-                if language == "Hindi":
-                    with st.spinner():                  
-                        text = hi_asr_model.transcribe([audio_location], logprobs=False)[0]
-                        print(text)
-                else:
-                    with st.spinner():          
-                        text = en_asr_model.transcribe([audio_location], logprobs=False)[0]
-                        print(text)
-
-                st.write(text)
-
-            delete_audio_file(audio_location)
-
-    except Exception as e:
-        st.write("ERROR! Something is wrong with the uploaded file.")
-        st.write("The file extension should be .wav with a sample rate of 16,000 Hz and a mono channel")
-        print(str(e))
-        delete_audio_file(audio_location)
-
-
-    # Footer mentioning the website
-    footer = """
-    <style>
-    .footer {
-        width: 100%;
-        text-align: center;
-        padding: 10px 0;
-        margin-top: 240px; /* Add margin-top for some gap */
-    }
-    </style>
-    <div class="footer">
-        <p>Powered by: <a href="https://zinglebytes.com">zinglebytes.com</a> | <a href="mailto:info@zinglebytes.com">Email us</a></p>
-    </div>
-    """
-    st.markdown(footer, unsafe_allow_html=True)
+clone_and_execute()