Create app.py

app.py

@@ -0,0 +1,156 @@
+from langchain import OpenAI, Wikipedia
+from langchain.agents import initialize_agent, Tool
+
+import os
+os.environ["OPENAI_API_KEY"] = os.environ.get("open_ai_key") #openai key
+
+import pickle
+
+"""# Model Implementation"""
+
+import_folder_name = "./embedded_kubernetes_docs"
+
+with open(import_folder_name + '.pkl', 'rb') as f:
+    store = pickle.load(f)
+
+from typing import Union
+
+from langchain.docstore.base import Docstore
+from langchain.docstore.document import Document
+
+
+class CustomData(Docstore):
+    """Wrapper around embedded custom data"""
+    datastore = None
+  
+    def __init__(self, store) -> None:
+        """Check that embedded custom data is available."""
+        print(store)
+        self.datastore = store
+        print("initialized")
+
+    def search(self, search: str) -> Union[str, Document]:
+        """Try to search for wiki page.
+
+        If page exists, return the page summary, and a PageWithLookups object.
+        If page does not exist, return similar entries.
+
+        Try to search for embedded data. 
+        If doc page exists, return the first one. 
+        
+        """
+        docs = self.datastore.similarity_search(search)
+        # print(docs[0].page_content)
+        return docs[0].page_content
+        # try:
+          
+        # except wikipedia.PageError:
+        #     result = f"Could not find [{search}]. Similar: {wikipedia.search(search)}"
+        # except wikipedia.DisambiguationError:
+        #     result = f"Could not find [{search}]. Similar: {wikipedia.search(search)}"
+        # return result
+
+from typing import Any, List, Optional, Tuple
+from langchain.docstore.base import Docstore
+from langchain.docstore.document import Document
+
+class DocstoreExplorer:
+    """Class to assist with exploration of a document store."""
+    def __init__(self, docstore: Docstore):
+        """Initialize with a docstore, and set initial document to None."""
+        self.docstore = docstore
+        self.document: Optional[Document] = None
+        self.llm = OpenAI(temperature=0.7)
+        self.prompt = "You are an expert at Kubernetes. Summarize the following input: "
+
+    def summarize (self, result: Document) -> str:
+        text = self.prompt + result
+        return self.llm(text)
+
+    def search(self, term: str) -> str:
+        """Search for a term in the docstore, and if found save."""
+        result = self.docstore.search(term)
+        summary = self.summarize(result)
+        print("summary: ", summary)
+        if isinstance(result, Document):
+            self.document = result
+            return summary # REPLACE THIS by having an LLM run a summarize on this based on the fact that it's an expert programmer. 
+        else:
+            self.document = None
+            return summary
+
+    def lookup(self, term: str) -> str:
+        """Lookup a term in document (if saved)."""
+        if self.document is None:
+            raise ValueError("Cannot lookup without a successful search first")
+        return self.document.lookup(term)
+
+docstore=DocstoreExplorer(CustomData(store))
+tools = [
+    Tool(
+        name="Search",
+        func=docstore.search
+    ),
+    Tool(
+        name="Lookup",
+        func=docstore.search
+    )
+]
+
+llm = OpenAI(temperature=0, model_name="text-davinci-003")
+react = initialize_agent(tools, llm, agent="react-docstore", verbose=True, return_intermediate_steps=True)
+
+question = "What kubernetes command can i run to see what's happening in my pod?"
+response = react({"input":question})
+
+"""# Gradio Implementation"""
+
+clerkieExamples=["What kubernetes command can i run to see what's happening in my pod", "How can I create a Secret object in Kubernetes?"]
+
+import random
+import gradio as gr
+import openai
+import re
+
+chat_variables = {
+    "Context": "", 
+    "StackTrace": "", 
+    "isLanguage": "", 
+}
+def chat(message, history):
+    print(message)
+    history = history or []
+    print("len(history: ", len(history))
+    response = react({"input":message})
+    history.append((message, response['output']))
+    return history, history
+
+def set_text(inp):
+  return inp
+
+def clear(arg): 
+  return "" 
+
+with gr.Blocks() as demo:
+    user_state=gr.State([])
+    gr.Markdown("""# Welcome to Kuber-Clerkie 🤖""")
+    gr.Markdown("""Kuber-Clerkie is finetuned on Kubernetes documentation to help you debug your complex Kubernetes errors / answer questions. Please feel free to give it a try and let us know what you think!""")
+    gr.Markdown("""### 👀 P.S. [Check out our GPT-3 based Chrome Extension that debugs your code](https://chrome.google.com/webstore/detail/clerkie-ai/oenpmifpfnikheaolfpabffojfjakfnn) 🔥🔥🔥""")
+    with gr.Row():
+      with gr.Column():
+        output = gr.Chatbot().style(color_map=("green", "pink"))
+        # allow_flagging="never"
+        inp = gr.Textbox(placeholder="enter your question here")
+        print(type(inp))
+        btn = gr.Button("Enter message")
+        inp.submit(chat, [inp, user_state], [output, user_state])
+        inp.submit(clear, inp, inp)
+        btn.click(chat, [inp, user_state], [output, user_state])
+        btn.click(clear, inp, inp)
+    gr.Markdown("""### need help? got feedback? have thoughts? etc. ➜ Join the [Discord](https://discord.gg/KvG3azf39U)""")
+    gr.Examples(clerkieExamples,
+        inputs=inp,
+        cache_examples=False,
+    )
+if __name__ == "__main__":
+    demo.launch(debug=True, share=True)