new changes

py_server/handlers/chat.py

@@ -1,4 +1,4 @@
-from models import ChatBody
+from models import ChatBody, ChatAppBody
 from bs4 import BeautifulSoup
 
 from langchain.docstore.document import Document as LDocument
@@ -14,21 +14,43 @@ from langchain.prompts.chat import (
 )
 from langchain.vectorstores import Chroma
 
+from db.supa import SupaService
 
-async def chat_extension_handler(body: ChatBody):
+
+supabase = SupaService()
+
+
+
+async def chat_app_handler(body: ChatAppBody, jwt: str):
     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()
+
+        user = supabase.get_user(jwt)
+
+        if not user:
+            return {
+                "bot_response": "You are not logged in",
+                "human_message": body.user_message,
+            }
+        
+
+        user_id = user.user.id
+
+
+        website_response = supabase.find_website(body.id, user_id)
+
+        website = website_response.data
+
+        if len(website) == 0:
+            return {
+                "bot_response": "Website not found",
+                "human_message": body.user_message,
+            }
+        
+        website = website[0]
+
+
+        text = website["html"]
 
         result = [LDocument(page_content=text, metadata={"source": "test"})]
         token_splitter =  CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
@@ -43,7 +65,76 @@ async def chat_extension_handler(body: ChatBody):
         messages = [
             SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user.
 If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
-If user want recommendation, helping based on the context then help the user.
+If user want recommendation, help from the context, or any other information, please provide it.
+If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
+-----------------
+{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(search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,)
+
+        history = [(d["human_message"], d["bot_response"]) for d in body.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,
+        }
+
+
+
+
+
+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 = Chroma.from_documents(doc, OpenAIEmbeddings())
+
+
+        messages = [
+            SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user.
+If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
+If user want recommendation, help from the context, or any other information, please provide it.
 If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
 -----------------
 {context}