UPDATE: chatHistory

app.py

@@ -81,7 +81,7 @@ async def sign_in(email, password):
         if store_session_check and store_session_check.data:
             store_id = store_session_check.data[0].get("StoreID")
 
-        userData = client.table("ConversAI_UserInfo").select("*").filter("user_id", "eq", user_id).execute().data
+        userData = supabase.table("ConversAI_UserInfo").select("*").filter("user_id", "eq", user_id).execute().data
         username = userData[0]["username"]
 
         if not store_id:
@@ -215,13 +215,13 @@ async def oauth(provider):
 @app.post("/newChatbot")
 async def newChatbot(chatbotName: str, username: str):
     currentBotCount = len(listTables(username=username)["output"])
-    limit = client.table("ConversAI_UserConfig").select("chatbotLimit").eq("user_id", username).execute().data[0][
+    limit = supabase.table("ConversAI_UserConfig").select("chatbotLimit").eq("user_id", username).execute().data[0][
         "chatbotLimit"]
     if currentBotCount >= int(limit):
         return {
             "output": "CHATBOT LIMIT EXCEEDED"
         }
-    client.table("ConversAI_ChatbotInfo").insert({"user_id": username, "chatbotname": chatbotName}).execute()
+    supabase.table("ConversAI_ChatbotInfo").insert({"user_id": username, "chatbotname": chatbotName}).execute()
     chatbotName = f"convai${username}${chatbotName}"
     return createTable(tablename=chatbotName)
 
@@ -238,13 +238,13 @@ async def addPDFData(vectorstore: str, pdf: UploadFile = File(...)):
     textExtraction = time.time()
     os.remove(temp_file_path)
     username, chatbotname = vectorstore.split("$")[1], vectorstore.split("$")[2]
-    df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
+    df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data)
     currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
-    limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
+    limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
         "tokenLimit"]
     newCount = currentCount + len(text)
     if newCount < int(limit):
-        client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
+        supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
             "chatbotname", chatbotname).execute()
         uploadStart = time.time()
         output = addDocuments(text=text, source=source, vectorstore=vectorstore)
@@ -271,29 +271,46 @@ async def addPDFData(vectorstore: str, pdf: UploadFile = File(...)):
 
 @app.post("/scanAndReturnText")
 async def returnText(pdf: UploadFile = File(...)):
+    source = pdf.filename
     pdf = await pdf.read()
     start = time.time()
     text = getTextFromImagePDF(pdfBytes=pdf)
     end = time.time()
     timeTaken = f"{end - start}s"
     return {
+        "source": source,
         "extractionTime": timeTaken,
         "output": text 
     }
 
 
 @app.post("/addText")
-async def addText(vectorstore: str, text: str):
+async def addText(vectorstore: str, text: str, source: str | None = None):
     username, chatbotname = vectorstore.split("$")[1], vectorstore.split("$")[2]
-    df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
+    df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data)
     currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
     newCount = currentCount + len(text)
-    limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
+    limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
         "tokenLimit"]
     if newCount < int(limit):
-        client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
+        supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
             "chatbotname", chatbotname).execute()
-        return addDocuments(text=text, source="text", vectorstore=vectorstore)
+        uploadStart = time.time()
+        output = addDocuments(text=text, source=source, vectorstore=vectorstore)
+        uploadEnd = time.time()
+        uploadTime = f"VECTOR UPLOAD TIME: {uploadEnd - uploadStart}s" + "\n"
+        tokenCount = f"TOKEN COUNT: {len(text)}" + "\n"
+        tokenizer = nltk.tokenize.RegexpTokenizer(r"\w+")
+        wordCount = f"WORD COUNT: {len(tokenizer.tokenize(text))}" + "\n"
+        newText = ("=" * 75 + "\n").join([uploadTime, wordCount, tokenCount, "TEXT: \n" + text + "\n"])
+        fileId = str(uuid.uuid4())
+        with open(f"{fileId}.txt", "w") as file:
+            file.write(newText)
+        with open(f"{fileId}.txt", "rb") as f:
+            supabase.storage.from_("ConversAI").upload(file = f, path = os.path.join("/", f.name), file_options={"content-type": "text/plain"})
+        os.remove(f"{fileId}.txt")
+        output["supabaseFileName"] = f"{fileId}.txt"
+        return output
     else:
         return {
             "output": "WEBSITE EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT."
@@ -309,14 +326,14 @@ class AddQAPair(BaseModel):
 @app.post("/addQAPair")
 async def addText(addQaPair: AddQAPair):
     username, chatbotname = addQaPair.vectorstore.split("$")[1], addQaPair.vectorstore.split("$")[2]
-    df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
+    df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data)
     currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
     qa = f"QUESTION: {addQaPair.question}\tANSWER: {addQaPair.answer}"
     newCount = currentCount + len(qa)
-    limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
+    limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][
         "tokenLimit"]
     if newCount < int(limit):
-        client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
+        supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
             "chatbotname", chatbotname).execute()
         return addDocuments(text=qa, source="Q&A Pairs", vectorstore=addQaPair.vectorstore)
     else:
@@ -331,12 +348,12 @@ async def addWebsite(vectorstore: str, websiteUrls: list[str]):
     text = extractTextFromUrlList(urls = websiteUrls)
     textExtraction = time.time()
     username, chatbotname = vectorstore.split("$")[1], vectorstore.split("$")[2]
-    df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
+    df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data)
     currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
     newCount = currentCount + len(text)
-    limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0]["tokenLimit"]
+    limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0]["tokenLimit"]
     if newCount < int(limit):
-        client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
+        supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq(
             "chatbotname", chatbotname).execute()
         uploadStart = time.time()
         output = addDocuments(text=text, source=urlparse(websiteUrls[0]).netloc, vectorstore=vectorstore)
@@ -364,13 +381,20 @@ async def addWebsite(vectorstore: str, websiteUrls: list[str]):
 
 @app.post("/answerQuery")
 async def answerQuestion(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192"):
-    return answerQuery(query=query, vectorstore=vectorstore, llmModel=llmModel)
+    username, chatbotName = vectorstore.split("$")[1], vectorstore.split("$")[2]
+    output = answerQuery(query=query, vectorstore=vectorstore, llmModel=llmModel)
+    response = (
+        supabase.table("ConversAI_ChatHistory")
+        .insert({"username": username, "chatbotName": chatbotName, "llmModel": llmModel, "question": query, "response": output["output"]})
+        .execute()
+    )
+    return output
 
 
 @app.post("/deleteChatbot")
 async def delete(chatbotName: str):
     username, chatbotName = chatbotName.split("$")[1], chatbotName.split("$")[2]
-    client.table('ConversAI_ChatbotInfo').delete().eq('user_id', username).eq('chatbotname', chatbotName).execute()
+    supabase.table('ConversAI_ChatbotInfo').delete().eq('user_id', username).eq('chatbotname', chatbotName).execute()
     return deleteTable(tableName=chatbotName)
 
 
@@ -389,7 +413,7 @@ async def crawlUrl(baseUrl: str):
 @app.post("/getCurrentCount")
 async def getCount(vectorstore: str):
     username, chatbotName = vectorstore.split("$")[1], vectorstore.split("$")[2]
-    df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
+    df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data)
     return {
         "currentCount": df[(df['user_id'] == username) & (df['chatbotname'] == chatbotName)]['charactercount'].iloc[0]
     }