added supabase

py_server/db/supa.py

@@ -0,0 +1,25 @@
+import supabase
+import os
+
+class SupaService:
+
+    def __init__(self):
+        self.supabase_url = os.environ.get("SUPABASE_URL")
+        self.supabase_key = os.environ.get("SUPABASE_KEY")
+        self.supabase = supabase.create_client(self.supabase_url, self.supabase_key)
+
+
+    def validate_user(self, token):
+        user = self.supabase.table("User").select("*").eq("access_token", token).execute()
+        return user
+    
+
+    def save_webiste(self, title: str, icon: str, html: str, url: str, user_id: str):
+        result = self.supabase.table("Website").insert(   {
+                "title": title,
+                "icon": icon,
+                "html": html,
+                "url": url,
+                "user_id": user_id
+            }).execute()
+        return result

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")

py_server/main.py

@@ -1,23 +1,8 @@
-from fastapi import FastAPI, Header, UploadFile, File
+from fastapi import FastAPI
 import os
-from pydantic import BaseModel
 from uvicorn import run
 from fastapi.middleware.cors import CORSMiddleware
-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
-)
-
-
+from routers import chat, user
 
 os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")
 
@@ -36,79 +21,9 @@ app.add_middleware(
     allow_headers=headers
 )
 
+app.include_router(chat.router)
 
-class ChatBody(BaseModel):
-    user_message: str
-    html: str
-    history: list
-
-
-@app.post("/chat")
-async def chat(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,
-        }
-
-
+app.include_router(user.router)
 
 if __name__ == "__main__":
     port = int(os.environ.get('PORT', 5000))

py_server/models/__init__.py

@@ -0,0 +1,2 @@
+from .chat import ChatBody
+from .user import UserValidation, SaveChatToApp

py_server/models/chat.py

@@ -0,0 +1,7 @@
+from pydantic import BaseModel
+
+class ChatBody(BaseModel):
+    user_message: str
+    html: str
+    history: list
+    # url: str

py_server/models/user.py

@@ -0,0 +1,11 @@
+from pydantic import BaseModel
+
+
+class UserValidation(BaseModel):
+    token: str
+
+
+
+class SaveChatToApp(BaseModel):
+    html: str
+    url: str

py_server/requirements.txt

@@ -8,4 +8,5 @@ numpy
 pydantic
 langchain
 lxml
-faiss-cpu
+faiss-cpu
+supabase

py_server/routers/chat.py

@@ -0,0 +1,9 @@
+from fastapi import APIRouter
+from models import ChatBody
+from handlers.chat import chat_extension_handler
+
+router = APIRouter(prefix="/api/v1")
+
+@router.post("/chat/chrome", tags=["chat"])
+async def chat_extension(body: ChatBody):
+    return await chat_extension_handler(body)

py_server/routers/user.py

@@ -0,0 +1,15 @@
+from fastapi import APIRouter, Header
+from models import UserValidation, SaveChatToApp
+from handlers.user import validate_user_handler, save_website_handler
+
+
+router = APIRouter(prefix="/api/v1")
+
+@router.post("/user/validate", tags=["user"])
+async def validate_user(user: UserValidation):
+    return await validate_user_handler(user)
+
+
+@router.post("/user/save", tags=["user"])
+async def save_website(body: SaveChatToApp, x_auth_token: str = Header(None)):
+    return await save_website_handler(body, x_auth_token)