Added streamlit app

frontend/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 Leopoldo Miranda
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

frontend/src/assets/rocket-svgrepo-com.svg:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.svgrepo.com/svg/275999/rocket
+HostUrl=https://www.svgrepo.com/download/275999/rocket.svg

frontend/src/components/Features.tsx

@@ -1,4 +1,4 @@
-import { Badge } from "./ui/badge";
+import { Badge } from "./ui/badge"; 
 import {
   Card,
   CardTitle,
@@ -6,7 +6,7 @@ import {
   CardHeader,
 } from "@/components/ui/card";
 import image4 from "../assets/looking-ahead.png";
-
+//import { Upload } from "./upload";
 
 interface FeatureProps {
   title: string;
@@ -66,6 +66,9 @@ export const Features = () => {
                 alt="About feature"
                 className="w-[150px] lg:w-[300px] mx-auto"
               />
+{/* Upload sections */}
+
+     
             </CardFooter>
           </Card>
         ))}

frontend/src/components/Navbar.tsx

@@ -1,4 +1,6 @@
 import { useState } from "react";
+import { Button } from "./ui/button";
+import { buttonVariants } from "./ui/button";
 import {
   NavigationMenu,
   NavigationMenuItem,
@@ -11,8 +13,6 @@ import {
   SheetTitle,
   SheetTrigger,
 } from "@/components/ui/sheet";
-import { GitHubLogoIcon } from "@radix-ui/react-icons";
-import { buttonVariants } from "./ui/button";
 import { Menu } from "lucide-react";
 import { ModeToggle } from "./mode-toggle";
 import { LogoIcon } from "./Icons";
@@ -73,26 +73,10 @@ export const Navbar = () => {
                   </SheetTitle>
                 </SheetHeader>
                 <nav className="flex flex-col justify-center items-center gap-2 mt-4">
-                  {routeList.map(({ href, label }: RouteProps) => (
-                    <a
-                      key={label}
-                      href={href}
-                      onClick={() => setIsOpen(false)}
-                      className={buttonVariants({ variant: "ghost" })}
-                    >
-                      {label}
-                    </a>
-                  ))}
-                  <a
-                    href="#"
-                    target="_blank"
-                    className={`w-[110px] border ${buttonVariants({
-                      variant: "secondary",
-                    })}`}
-                  >
-                    <GitHubLogoIcon className="mr-0 w-14 h-5" />
-                    Join Waitlist
-                  </a>
+                   <a>
+                <Button className="w-full md:w-2/3">Join Waitlist</Button>
+           </a>
+                  
                 </nav>
               </SheetContent>
             </Sheet>
@@ -114,15 +98,10 @@ export const Navbar = () => {
           </nav>
 
           <div className="hidden md:flex gap-2">
-            <a
-              href="https://github.com/leoMirandaa/shadcn-landing-page.git"
-              target="_blank"
-              className={`border ${buttonVariants({ variant: "secondary" })}`}
-            >
-              <GitHubLogoIcon className="mr-0 w-8 h-5" />
-              Join Waitlist
-            </a>
-
+            <a>
+		<Button className="w-full ">Join Waitlist
+</Button>
+	   </a>
             <ModeToggle />
           </div>
         </NavigationMenuList>

frontend/src/components/upload/.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#F63366"
+backgroundColor="#FFFFFF"
+secondaryBackgroundColor="#F0F2F6"
+textColor="#262730"
+font="sans serif"

frontend/src/components/upload/app-v1.py

@@ -0,0 +1,46 @@
+import streamlit as st
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+
+def generate_response(uploaded_file, openai_api_key, query_text):
+    # Load document if file is uploaded
+    if uploaded_file is not None:
+        documents = [uploaded_file.read().decode()]
+    # Split documents into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.create_documents(documents)
+    # Select embeddings
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+    # Create a vectorstore from documents
+    db = Chroma.from_documents(texts, embeddings)
+    # Create retriever interface
+    retriever = db.as_retriever()
+    # Create QA chain
+    qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever)
+    return qa.run(query_text)
+
+# Page title
+st.set_page_config(page_title='🦜🔗 Ask the Doc App')
+st.title('🦜🔗 Ask the Doc App')
+
+# File upload
+uploaded_file = st.file_uploader('Upload an article', type='txt')
+# Query text
+query_text = st.text_input('Enter your question:', placeholder = 'Please provide a short summary.', disabled=not uploaded_file)
+
+# Form input and query
+result = []
+with st.form('myform', clear_on_submit=True):
+    openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text))
+    submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text))
+    if submitted and openai_api_key.startswith('sk-'):
+        with st.spinner('Calculating...'):
+            response = generate_response(uploaded_file, openai_api_key, query_text)
+            result.append(response)
+            del openai_api_key
+
+if len(result):
+    st.info(response)

frontend/src/components/upload/requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+langchain
+openai
+chromadb
+tiktoken

frontend/src/components/upload/streamlit_app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+
+def generate_response(uploaded_file, openai_api_key, query_text):
+    # Load document if file is uploaded
+    if uploaded_file is not None:
+        documents = [uploaded_file.read().decode()]
+        # Split documents into chunks
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        texts = text_splitter.create_documents(documents)
+        # Select embeddings
+        embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+        # Create a vectorstore from documents
+        db = Chroma.from_documents(texts, embeddings)
+        # Create retriever interface
+        retriever = db.as_retriever()
+        # Create QA chain
+        qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type='stuff', retriever=retriever)
+        return qa.run(query_text)
+
+
+# Page title
+st.set_page_config(page_title='🦜🔗 Ask the Doc App')
+st.title('🦜🔗 Ask the Doc App')
+
+# File upload
+uploaded_file = st.file_uploader('Upload an article', type='txt')
+# Query text
+query_text = st.text_input('Enter your question:', placeholder = 'Please provide a short summary.', disabled=not uploaded_file)
+
+# Form input and query
+result = []
+with st.form('myform', clear_on_submit=True):
+    openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text))
+    submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text))
+    if submitted and openai_api_key.startswith('sk-'):
+        with st.spinner('Calculating...'):
+            response = generate_response(uploaded_file, openai_api_key, query_text)
+            result.append(response)
+            del openai_api_key
+
+if len(result):
+    st.info(response)