Dev

backend/database/data_profile_manager.py

@@ -5,9 +5,14 @@ class DataProfileManager:
     def __init__(self, session):
         self.session = session
 
-    def get_dataprofile_by_name(self, name):
+    def get_dataprofile_by_name_and_org(self, name, org_id) -> DataProfile:
         """Retrieve a DataProfile by its name."""
-        return self.session.query(DataProfile).filter(DataProfile.name == name).first()
+        return (
+            self.session.query(DataProfile)
+            .filter(DataProfile.name == name)
+            .filter(DataProfile.organization_id == org_id)
+            .first()
+        )
 
     def get_all_data_profiles(self):
         """Retrieve all DataProfiles."""

backend/envs/dev/initialization/setup_dev_environment.py

@@ -178,7 +178,9 @@ def create_sample_dataprofile():
     # Using DatabaseManager to manage the database session
     with DatabaseManager() as session:
         profile_manager = DataProfileManager(session)
-        existing_profile = profile_manager.get_dataprofile_by_name(sample_profile.name)
+        existing_profile = profile_manager.get_dataprofile_by_name_and_org(
+            sample_profile.name, 1
+        )
         if not existing_profile:
             profile_manager.create_dataprofile(sample_profile)
             logger.debug("Sample data profile created.")

backend/llms/gpt.py

@@ -7,6 +7,7 @@
 from database.database_manager import DatabaseManager
 from llms.prompt_manager import PromptManager
 from llms.system_message_manager import SystemMessageManager
+from models.data_profile import DataProfile
 from models.user import User
 from openai import ChatCompletion
 from settings import OPENAI_API_KEY
@@ -402,11 +403,12 @@ def generate_text(self, input_text):
         return assistant_message_content
 
     async def extract_data_from_jpgs(
-        self, instructions: str, jpg_presigned_urls: List[str]
+        self, data_profile: DataProfile, jpg_presigned_urls: List[str]
     ):
         self._add_system_message(assistant_type="jpg_data_extraction")
         self._set_model(model_type="img")
 
+        instructions = data_profile.description
         prompt = self.prompt_manager.jpg_data_extraction_prompt(instructions)
 
         assistant_message_content = await self._send_and_receive_message(

backend/llms/prompt_manager.py

@@ -102,6 +102,7 @@ def jpg_data_extraction_prompt(self, instructions: str):
             {instructions}
 
             Provide output in a JSON string using the requested information as keys.
+            The JSON string should be flat, not nested.
 
             Example output:
             {{

backend/object_storage/digitalocean_space_manager.py

@@ -11,6 +11,7 @@
 from typing import List
 
 import boto3
+from fastapi import UploadFile
 from settings import (
     SPACES_ACCESS_KEY,
     SPACES_BUCKET_NAME,
@@ -21,7 +22,12 @@
 
 
 class DigitalOceanSpaceManager:
-    def __init__(self, organization_name: str = "", file_paths: List[str] = []):
+    def __init__(
+        self,
+        organization_name: str = "",
+        files: List[UploadFile] = [],
+        file_paths: List[str] = [],
+    ):
         session = boto3.session.Session()
         self.client = session.client(
             "s3",
@@ -34,6 +40,7 @@ def __init__(self, organization_name: str = "", file_paths: List[str] = []):
 
         self.organization_name = organization_name.replace(" ", "_")
 
+        self.files = files
         self.file_paths = file_paths
         self.file_names = [os.path.basename(file_path) for file_path in file_paths]
         self.object_names: List[str] = []
@@ -52,6 +59,27 @@ def upload_files(self):
         """
         all_uploaded = True
 
+        # Upload the files
+        for file in self.files:
+            # Prepend the organization_name to the object_name
+            object_name = f"{self.organization_name}/{file.filename}"
+            try:
+                file.file.seek(0)  # Ensure we're at the start of the file
+                self.client.upload_fileobj(file.file, self.bucket_name, object_name)
+                self.object_names.append(object_name)
+            except Exception as e:
+                print(e)
+                all_uploaded = False
+
+        return all_uploaded
+
+    def upload_files_by_paths(self):
+        """Upload multiple files using their file paths to an S3 bucket
+
+        :return: True if files were uploaded, else False
+        """
+        all_uploaded = True
+
         # Upload the files
         for file_path, file_name in zip(self.file_paths, self.file_names):
             # Prepend the organization_name to the object_name

backend/routes/data_profile_routes.py

@@ -1,9 +1,11 @@
+import os
 import tempfile
 from typing import List
 
 from database.data_profile_manager import DataProfileManager
 from database.database_manager import DatabaseManager
 from database.organization_manager import OrganizationManager
+from database.table_manager import TableManager
 from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
 from llms.gpt import GPTLLM
 from models.data_profile import (
@@ -43,12 +45,15 @@ async def save_data_profiles(
 ) -> DataProfileCreateResponse:
     with DatabaseManager() as session:
         data_profile_manager = DataProfileManager(session)
-        if data_profile_manager.get_dataprofile_by_name(request.name):
+        if data_profile_manager.get_dataprofile_by_name_and_org(
+            request.name, current_user.organization_id
+        ):
             raise HTTPException(status_code=400, detail="Data Profile already exists")
 
         new_data_profile = DataProfile(
             name=request.name,
             description=request.description,
+            organization_id=current_user.organization_id,
         )
         created_data_profile = data_profile_manager.create_dataprofile(new_data_profile)
 
@@ -104,11 +109,99 @@ async def preview_data_profile(
         with DigitalOceanSpaceManager(
             organization_name=organization_name, file_paths=jpg_file_paths
         ) as space_manager:
-            space_manager.upload_files()
+            space_manager.upload_files_by_paths()
             jpg_presigned_urls = space_manager.create_presigned_urls()
             gpt = GPTLLM(chat_id=1, user=current_user)
             extracted_data = await gpt.extract_data_from_jpgs(
                 instructions, jpg_presigned_urls
             )
 
-        return extracted_data
+    # Delete the temporary files
+    for path in temp_file_paths:
+        os.remove(path)
+
+    return extracted_data
+
+
+@data_profile_router.post("/data-profiles/{data_profile_name}/preview/")
+async def preview_data_profile_upload(
+    data_profile_name: str,
+    files: List[UploadFile] = File(...),
+    current_user: User = Depends(get_current_user),
+):
+    temp_file_paths = []
+    for file in files:
+        if file.filename:
+            suffix = file.filename.split(".")[-1]
+
+        # Save the uploaded file temporarily
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix="." + suffix)
+        temp_file.write(await file.read())
+        temp_file.close()
+        temp_file_paths.append(temp_file.name)
+
+    # Get the organization name
+    with DatabaseManager() as session:
+        org_manager = OrganizationManager(session)
+        organization_name = org_manager.get_organization(
+            current_user.organization_id
+        ).name
+
+        data_profile_manager = DataProfileManager(session)
+        data_profile = data_profile_manager.get_dataprofile_by_name_and_org(
+            data_profile_name, current_user.organization_id
+        )
+
+    # Use the ImageConversionManager context manager to convert the PDF to JPG
+    with ImageConversionManager(temp_file_paths) as manager:
+        jpg_file_paths = manager.convert_to_jpgs()
+
+        # Upload the JPG file to DigitalOcean Spaces, automatically deleting it when done
+        with DigitalOceanSpaceManager(
+            organization_name=organization_name, file_paths=jpg_file_paths
+        ) as space_manager:
+            space_manager.upload_files_by_paths()
+            jpg_presigned_urls = space_manager.create_presigned_urls()
+            gpt = GPTLLM(chat_id=1, user=current_user)
+            extracted_data = await gpt.extract_data_from_jpgs(
+                data_profile, jpg_presigned_urls
+            )
+
+    # Delete the temporary files
+    for path in temp_file_paths:
+        os.remove(path)
+
+    return extracted_data
+
+
+@data_profile_router.post("/data-profiles/{data_profile_name}/extracted-data/")
+async def save_extracted_data(
+    data_profile_name: str,
+    extracted_data: dict,
+    files: List[UploadFile] = File(...),
+    current_user: User = Depends(get_current_user),
+):
+    # Get the organization name
+    with DatabaseManager() as session:
+        org_manager = OrganizationManager(session)
+        organization_name = org_manager.get_organization(
+            current_user.organization_id
+        ).name
+
+        data_profile_manager = DataProfileManager(session)
+        data_profile: DataProfile = (
+            data_profile_manager.get_dataprofile_by_name_and_org(
+                data_profile_name, current_user.organization_id
+            )
+        )
+
+        table_manager = TableManager(session)
+        print(data_profile, table_manager)  # TODO: To be further implemented
+
+    # Upload the JPG file to DigitalOcean Spaces, automatically deleting it when done
+    with DigitalOceanSpaceManager(
+        organization_name=organization_name, files=files
+    ) as space_manager:
+        space_manager.upload_files()
+
+    return {"message": "Extracted data saved successfully"}

backend/utils/image_conversion_manager.py

@@ -20,12 +20,19 @@ def __exit__(self, exc_type, exc_value, traceback):
                     os.unlink(converted_file_path)  # Delete the file
 
     def convert_to_jpgs(self):
-        if all(file_path.endswith(".pdf") for file_path in self.file_paths):
+        if all(file_path.lower().endswith(".pdf") for file_path in self.file_paths):
             return self._convert_pdfs_to_jpgs(self.file_paths)
-        elif all(file_path.endswith(".png") for file_path in self.file_paths):
+        elif all(file_path.lower().endswith(".png") for file_path in self.file_paths):
             return self._convert_pngs_to_jpgs(self.file_paths)
+        elif all(
+            file_path.lower().endswith((".jpg", ".jpeg"))
+            for file_path in self.file_paths
+        ):
+            return self.file_paths
         else:
-            print("All files must be of the same type (either all .pdf or all .png)")
+            print(
+                "All files must be of the same type (either all .pdf, all .png, or all .jpg/.jpeg)"
+            )
             return []
 
     def _convert_pdfs_to_jpgs(self, file_paths: List[str]):

frontend/nginx.prod.conf

@@ -2,6 +2,9 @@ server {
     listen 80;
     server_name localhost;
 
+    # Add this line to increase the client body size limit
+    client_max_body_size 100M;
+
     location / {
         root /usr/share/nginx/html;
         try_files $uri $uri/ /index.html;

frontend/src/App.jsx

@@ -40,10 +40,6 @@ function AppWrapper() {
 function App() {
   const { isLoading } = useAuth();
 
-  if (isLoading) {
-    return <div>Loading...</div>; // Or any other loading indicator
-  }
-
   useEffect(() => {
     if (APP_ENV === "dev") {
       document.title = "DocShow AI - Dev";
@@ -52,6 +48,10 @@ function App() {
     }
   }, []);
 
+  if (isLoading) {
+    return <div>Loading...</div>; // Or any other loading indicator
+  }
+
   return (
     <Routes>
       <Route

frontend/src/pages/analytics/AIAssistant.jsx

@@ -10,7 +10,7 @@ import {
 import axios from "axios";
 import { API_URL } from "../../utils/constants";
 
-const AIAssistant = ({ table }) => {
+const AIAssistant = () => {
   const [userInput, setUserInput] = useState("");
   const [chatHistory, setChatHistory] = useState([]);
   const chatEndRef = useRef(null);

frontend/src/pages/analytics/AnalyticsPage.jsx

@@ -1,41 +1,17 @@
 // AnalyticsPage.js
-import React, { useEffect, useState } from "react";
+import React from "react";
 import { Box, Typography, Grid } from "@mui/material";
 import AIAssistant from "./AIAssistant";
-import TableSelectDropdown from "../../components/tables/selects/TableSelectDropdown";
-import { fetchOrganizationTables } from "../../api/organizationTables";
 
 function AnalyticsPage() {
-  const [tables, setTables] = useState([]);
-  const [selectedTable, setSelectedTable] = useState("");
-
-  useEffect(() => {
-    const getOrganizationTables = async () => {
-      const data = await fetchOrganizationTables();
-      setTables(data);
-    };
-
-    getOrganizationTables();
-  }, []);
-
-  const handleTableSelect = (table) => {
-    setSelectedTable(table);
-  };
-
-  useEffect(() => {
-    if (selectedTable) {
-      handleTableSelect(selectedTable);
-    }
-  }, [selectedTable]);
-
   return (
     <Box>
       <Typography variant="h4" gutterBottom>
         📊 AI Analyst
       </Typography>
       <Grid container spacing={2}>
         <Grid item xs={12}>
-          <AIAssistant table={selectedTable} />
+          <AIAssistant />
         </Grid>
       </Grid>
     </Box>

frontend/src/pages/upload/CreateDataProfilePage.jsx

@@ -24,7 +24,7 @@ function CreateDataProfilePage({ open, onClose, onCreate }) {
 
   const handleSubmit = (event) => {
     event.preventDefault();
-    onCreate({ name, extractInstructions });
+    onCreate(name, extractInstructions);
   };
 
   const handlePreview = () => {
@@ -101,7 +101,7 @@ function CreateDataProfilePage({ open, onClose, onCreate }) {
               type="submit"
               color="primary"
               variant="contained"
-              disabled={!isPreviewTableOpen}
+              disabled={!isPreviewTableOpen || !name || !extractInstructions}
             >
               Create
             </Button>