Create app.py

app.py

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+import os
+import gradio as gr
+import openai
+
+from langdetect import detect 
+from gtts import gTTS
+from pdfminer.high_level import extract_text
+
+
+#langchain part
+from langchain.llms import OpenAI
+from langchain.text_splitter import SpacyTextSplitter
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import DirectoryLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Pinecone
+
+
+openai.api_key = os.environ['OPENAI_API_KEY']
+pinecone_key = os.environ['PINECONE_API_KEY']
+pinecone_environment='us-west1-gcp'
+
+
+user_db = {os.environ['username1']: os.environ['password1']]}
+
+messages = [{"role": "system", "content": 'You are a helpful assistant.'}]
+
+
+
+
+def init_pinecone(index_name):
+    # initialize connection to Pinecone vector DB (app.pinecone.io for API key)
+    pinecone.init(
+        api_key=pinecone_key,
+        environment=pinecone_environment
+    )
+    #using openai embedding hence dim = 1536
+    pinecone.create_index(index_name, dimension=1536, metric="euclidean")
+    index = pinecone.Index(index_name)
+    return index
+
+
+def process_file(index_name, docs):
+    init_pinecone(index_name)
+    embeddings = OpenAIEmbeddings()
+    #pipeline='zh_core_web_sm'
+    splter = SpacyTextSplitter(chunk_size=1000,chunk_overlap=200)
+    split_text = splter.split_documents(docs)
+    
+    for document in split_text:
+		Pinecone.from_documents([document], embeddings, index_name=index_name)
+
+    return list_pinecone(index_name)
+
+
+def list_pinecone(index_name):
+    index = pinecone.Index(index_name)
+    stats = index.describe_index_stats()
+    return stats
+
+
+
+def roleChoice(role):
+    global messages
+    messages = [{"role": "system", "content": role}]
+    return "role:" + role
+
+
+
+
+
+
+def textGPT(text):
+    global messages
+
+    messages.append({"role": "user", "content": text})
+
+    response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
+
+    system_message = response["choices"][0]["message"]
+    messages.append(system_message)
+
+    chats = ""
+    for msg in messages:
+        if msg['role'] != 'system':
+            chats += msg['role'] + ": " + msg['content'] + "\n\n"
+
+    return chats
+
+
+def fileGPT(prompt, file_obj):
+    global messages 
+
+    file_text = extract_text(file_obj.name)
+    text = prompt + "\n\n" + file_text
+    
+    messages.append({"role": "user", "content": text})
+    
+    response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
+
+    system_message = response["choices"][0]["message"]
+    messages.append(system_message)
+
+    chats = ""
+    for msg in messages:
+        if msg['role'] != 'system':
+            chats += msg['role'] + ": " + msg['content'] + "\n\n"
+
+    return chats
+
+
+
+def clear():
+    global messages
+    messages = [{"role": "system", "content": 'You are a helpful technology assistant.'}]
+    return
+    
+def show():
+    global messages
+    chats = ""
+    for msg in messages:
+        if msg['role'] != 'system':
+            chats += msg['role'] + ": " + msg['content'] + "\n\n"
+
+    return chats
+
+
+with gr.Blocks() as chatHistory:
+    gr.Markdown("Click the Clear button below to remove all the chat history.")
+    clear_btn = gr.Button("Clear")
+    clear_btn.click(fn=clear, inputs=None, outputs=None, queue=False)
+
+    gr.Markdown("Click the Display button below to show all the chat history.")
+    show_out = gr.Textbox()
+    show_btn = gr.Button("Display")
+    show_btn.click(fn=show, inputs=None, outputs=show_out, queue=False)
+
+
+
+role = gr.Interface(fn=roleChoice, inputs="text", outputs="text", description = "Choose your GPT roles, e.g. You are a helpful technology assistant. 你是一位 IT 架构师。 你是一位开发者关系顾问。你是一位机器学习工程师。你是一位高级 C++ 开发人员 ")
+text = gr.Interface(fn=textGPT, inputs="text", outputs="text")
+
+pinecone = gr.Interface(fn=process_file, inputs=["text", "file"], outputs="text")
+
+#audio = gr.Interface(fn=audioGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs="text")
+#siri = gr.Interface(fn=siriGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs = "audio")
+file = gr.Interface(fn=fileGPT, inputs=["text", "file"], outputs="text", description = "Enter prompt sentences and your PDF. e.g. lets think step by step, summarize this following text:  或者 让我们一步一步地思考，总结以下的内容:")
+demo = gr.TabbedInterface([role, text, file, chatHistory, pinecone], [ "roleChoice", "chatGPT", "fileGPT", "ChatHistory", "Pinecone"])
+
+if __name__ == "__main__":
+    demo.launch(enable_queue=False, auth=lambda u, p: user_db.get(u) == p,
+        auth_message="This is not designed to be used publicly as it links to a personal openAI API. However, you can copy my code and create your own multi-functional ChatGPT with your unique ID and password by utilizing the 'Repository secrets' feature in huggingface.")
+    #demo.launch()