added supabase

py_server/handlers/chat.py

@@ -0,0 +1,79 @@
+from models import ChatBody
+from bs4 import BeautifulSoup
+
+from langchain.docstore.document import Document as LDocument
+from langchain.vectorstores.faiss import FAISS
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains import ConversationalRetrievalChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate
+)
+
+async def chat_extension_handler(body: ChatBody):
+    try:
+        soup = BeautifulSoup(body.html, 'lxml')
+
+        iframe = soup.find('iframe', id='pageassist-iframe')
+        if iframe:
+            iframe.decompose()
+        div = soup.find('div', id='pageassist-icon')
+        if div:
+            div.decompose()
+        div = soup.find('div', id='__plasmo-loading__')
+        if div:
+            div.decompose()
+        text = soup.get_text()
+
+        result = [LDocument(page_content=text, metadata={"source": "test"})]
+        token_splitter =  CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        doc = token_splitter.split_documents(result)
+
+        print(f'Number of documents: {len(doc)}')
+        
+
+        vectorstore = FAISS.from_documents(doc, OpenAIEmbeddings())
+
+
+        messages = [
+            SystemMessagePromptTemplate.from_template("""I want you to act as a webpage that I am having a conversation witu. Your name is "OpenChatX". You will provide me with answers from the given text from webpage. Your answer should be original, concise, accurate, and helpful. You can recommend, translate and can do anything based on the context given. If the answer is not included in the text and you know the answer you can resonpond the answer othwerwise say exactly "I don't know the answer " and stop after that. Never break character. Answer must be in markdown format.
+-----------------
+{context}
+            """),
+            HumanMessagePromptTemplate.from_template("{question}")
+        ]
+
+        prompt = ChatPromptTemplate.from_messages(messages)
+
+
+        chat =  ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(), return_source_documents=True, qa_prompt=prompt,)
+
+        history = [(d["human_message"], d["bot_response"]) for d in body.history]
+
+        print(history)
+
+        response = chat({
+            "question": body.user_message,
+            "chat_history": history
+        })
+        
+
+        answer = response["answer"]
+        answer = answer[answer.find(":")+1:].strip()
+
+        
+        return {
+            "bot_response": answer,
+            "human_message": body.user_message,
+        }
+
+    except Exception as e:
+        print(e)
+        return {
+            "bot_response": "Something went wrong please try again later",
+            "human_message": body.user_message,
+        }
+

py_server/handlers/user.py

@@ -0,0 +1,57 @@
+from fastapi import HTTPException, Header
+from models import UserValidation, SaveChatToApp
+from db.supa import SupaService
+from bs4 import BeautifulSoup
+
+
+supabase = SupaService()
+
+async def validate_user_handler(user: UserValidation):
+    if user.token is None or user.token == "":
+        raise HTTPException(status_code=400, detail="Token is required")
+    user = supabase.validate_user(user.token)
+    data = user.data
+
+    if len(data) == 0:
+        raise HTTPException(status_code=400, detail="Invalid token")
+
+    return {
+        "status": "success",
+    }
+
+
+async def save_website_handler(body: SaveChatToApp, x_auth_token):
+    try:
+        if x_auth_token is None or x_auth_token == "":
+            raise HTTPException(status_code=400, detail="Token is required")
+        
+        user = supabase.validate_user(x_auth_token)
+        data = user.data
+        if len(data) == 0:
+            raise HTTPException(status_code=400, detail="Invalid token")
+        
+        soup = BeautifulSoup(body.html, 'lxml')
+
+        title = soup.title.string if soup.title else "Untitled Page"
+        icon = soup.find('link', rel='icon').get('href') if soup.find('link', rel='icon') else None
+
+        iframe = soup.find('iframe', id='pageassist-iframe')
+        if iframe:
+            iframe.decompose()
+        div = soup.find('div', id='pageassist-icon')
+        if div:
+            div.decompose()
+        div = soup.find('div', id='__plasmo-loading__')
+        if div:
+            div.decompose()
+        text = soup.get_text()
+
+
+        result = supabase.save_webiste(html=text, title=title, icon=icon, url=body.url, user_id=data[0]["id"])
+
+        return {
+            "status": "Success"
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="Internal server error")