Upload app.py

app.py

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+import os
+import openai
+import streamlit as st
+from audio_recorder_streamlit import audio_recorder
+from elevenlabs import generate
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import DeepLake
+from streamlit_chat import message
+from dotenv import load_dotenv
+
+# Load environment variables from the .env file
+load_dotenv()
+
+# Constants
+TEMP_AUDIO_PATH = "temp_audio.wav"
+AUDIO_FORMAT = "audio/wav"
+
+# Load environment variables from .env file and return the keys
+openai.api_key = os.environ.get('OPENAI_API_KEY')
+eleven_api_key = os.environ.get('ELEVEN_API_KEY')
+active_loop_data_set_path = os.environ.get('DEEPLAKE_DATASET_PATH')
+
+# Load embeddings and DeepLake database
+def load_embeddings_and_database(active_loop_data_set_path):
+    embeddings = OpenAIEmbeddings()
+    db = DeepLake(
+        dataset_path=active_loop_data_set_path,
+        read_only=True,
+        embedding_function=embeddings
+    )
+    return db
+
+# Transcribe audio using OpenAI Whisper API
+def transcribe_audio(audio_file_path, openai_key):
+    openai.api_key = openai_key
+    try:
+        with open(audio_file_path, "rb") as audio_file:
+            response = openai.Audio.transcribe("whisper-1", audio_file)
+        return response["text"]
+    except Exception as e:
+        print(f"Error calling Whisper API: {str(e)}")
+        return None
+
+# Record audio using audio_recorder and transcribe using transcribe_audio
+def record_and_transcribe_audio():
+    audio_bytes = audio_recorder()
+    transcription = None
+    if audio_bytes:
+        st.audio(audio_bytes, format=AUDIO_FORMAT)
+
+        with open(TEMP_AUDIO_PATH, "wb") as f:
+            f.write(audio_bytes)
+
+        if st.button("Transcribe"):
+            transcription = transcribe_audio(TEMP_AUDIO_PATH, openai.api_key)
+            os.remove(TEMP_AUDIO_PATH)
+            display_transcription(transcription)
+
+    return transcription
+
+# Display the transcription of the audio on the app
+def display_transcription(transcription):
+    if transcription:
+        st.write(f"Transcription: {transcription}")
+        with open("audio_transcription.txt", "w+") as f:
+            f.write(transcription)
+    else:
+        st.write("Error transcribing audio.")
+
+# Get user input from Streamlit text input field
+def get_user_input(transcription):
+    return st.text_input("", value=transcription if transcription else "", key="input")
+
+# Search the database for a response based on the user's query
+def search_db(user_input, db):
+    print(user_input)
+    retriever = db.as_retriever()
+    retriever.search_kwargs['distance_metric'] = 'cos'
+    retriever.search_kwargs['fetch_k'] = 100
+    retriever.search_kwargs['maximal_marginal_relevance'] = True
+    retriever.search_kwargs['k'] = 10
+    model = ChatOpenAI(model='gpt-3.5-turbo')
+    qa = RetrievalQA.from_llm(model, retriever=retriever, return_source_documents=True)
+    return qa({'query': user_input})
+
+# Display conversation history using Streamlit messages
+def display_conversation(history):
+    for i in range(len(history["generated"])):
+        message(history["past"][i], is_user=True, key=str(i) + "_user")
+        message(history["generated"][i],key=str(i))
+        #Voice using Eleven API
+        voice= "Anish de"
+        text= history["generated"][i]
+        audio = generate(text=text, voice=voice,api_key=eleven_api_key)
+        st.audio(audio, format='audio/mp3')
+
+# Main function to run the app
+def main():
+    # Initialize Streamlit app with a title
+    st.write("# KPMG VOICE GPT")
+   
+    # Load embeddings and the DeepLake database
+    db = load_embeddings_and_database(active_loop_data_set_path)
+
+    # Record and transcribe audio
+    transcription = record_and_transcribe_audio()
+
+    # Get user input from text input or audio transcription
+    user_input = get_user_input(transcription)
+
+    # Initialize session state for generated responses and past messages
+    if "generated" not in st.session_state:
+        st.session_state["generated"] = ["I am ready to help you"]
+    if "past" not in st.session_state:
+        st.session_state["past"] = ["Hey there!"]
+        
+    # Search the database for a response based on user input and update session state
+    if user_input:
+        output = search_db(user_input, db)
+        print(output['source_documents'])
+        st.session_state.past.append(user_input)
+        response = str(output["result"])
+        st.session_state.generated.append(response)
+
+    # Display conversation history using Streamlit messages
+    if st.session_state["generated"]:
+        display_conversation(st.session_state)
+
+# Run the main function when the script is executed
+if __name__ == "__main__":
+    main()