Update runner.py

runner.py

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+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
+
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+import gradio as gr
+import openai
+import os
+
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+from IPython.display import Audio
+
+os.environ['OPENAI_API_KEY'] = "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"
+api_key = os.getenv('OPENAI_API_KEY')
+openai.api_key = api_key
+
+# connect your Google Drive
+"""from google.colab import drive
+drive.mount('/content/gdrive', force_remount=True)
+root_dir = "/content/gdrive/My Drive/"
+data_path = '/content/gdrive/My Drive/CDSS/LLM Demos/ASHA material'
+"""
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores import Chroma
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.llms import OpenAI
+from langchain.chains.question_answering import load_qa_chain
+
+pdf_folder_path = 'ASHA material'
+loader = PyPDFDirectoryLoader(pdf_folder_path)
+
+pages = loader.load_and_split()
+embeddings = OpenAIEmbeddings()
+docsearch = Chroma.from_documents(pages, embeddings).as_retriever()
+from pydub import AudioSegment
+
+# download and load all models
+preload_models()
+
+
+lang_dict = {
+    "English": "en",
+}
+
+
+# generate audio from text
+text_prompt = """
+     Hello, my name is Suno. And, uh — and I like pizza. [laughs] 
+     But I also have other interests such as playing tic tac toe.
+"""
+#audio_array = generate_audio(text_prompt)
+
+# save audio to disk
+#write_wav("bark_generation.wav", SAMPLE_RATE, audio_array)
+  
+# play text in notebook
+#Audio(audio_array, rate=SAMPLE_RATE)
+
+def get_asr_output(audio_path,lang = 'English'):
+    audio = AudioSegment.from_file(audio_path)
+    audio.export("temp.wav", format="wav")
+    file = open("temp.wav","rb")
+    
+    
+    transcription = openai.Audio.transcribe("whisper-1", file, language=lang)
+    op_text = transcription.text
+
+    """ if lang == "hi":
+        op_text = asr_pipe("temp.wav")['text']
+        print('whisper',transcription)
+        print('ai4b',op_text) """
+    
+    return op_text
+
+def greet(audio, lang, if_audio=True):
+    query = get_asr_output(audio, lang_dict[lang])
+    
+    docs = docsearch.get_relevant_documents(query)
+    chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+    answer = chain.run(input_documents=docs, question=query)
+
+    return query, answer
+
+def get_audio2(answer):
+    audio_array = generate_audio(answer)
+    write_wav("bark_generation.wav", SAMPLE_RATE, audio_array)
+    
+    return 24000, audio_array
+    
+def dummy(name):
+    return "bark_generation.wav"
+
+lang = gr.Radio(list(lang_dict.keys()), label="Select a Language")
+
+with gr.Blocks(title="CHO Assistant") as demo:
+    #gr.Image('assistant.png', shape = (10,10))
+    lang = gr.Radio(list(lang_dict.keys()), label="Select a Language")
+    user_audio = gr.Audio(source="microphone",type="filepath",label = "Speak your query")
+    text = gr.Textbox(placeholder="Question", name = "Question / Voice Transcription", show_label=False)
+    output = gr.Textbox(placeholder="The answer will appear here", interactive=False, show_label = False)
+    
+    submit = gr.Button("Submit")
+    submit.click(greet, [user_audio, lang], [text, output])
+
+    get_audio = gr.Button('Get Audio')
+    audio = gr.Audio()
+    get_audio.click(get_audio2, output, audio)
+    
+demo.launch(share = True)