Update app.py

app.py

@@ -1,11 +1,6 @@
-from flask import Flask, render_template, jsonify, request, redirect, url_for
-from flask_wtf.csrf import CSRFProtect
-
-# from tavily import TavilyClient
 
 from dotenv import load_dotenv
 import os
-
 from langchain_community.document_loaders import TextLoader
 from langchain_community.vectorstores import Chroma
 from langchain_text_splitters import CharacterTextSplitter
@@ -23,13 +18,7 @@ from langchain.prompts import PromptTemplate
 import time
 load_dotenv()
 
-# TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-# tavily = TavilyClient(api_key=TAVILY_API_KEY)
-
-app = Flask(__name__, static_folder='static')
-app.config['SECRET_KEY'] = 'secret'
-csrf = CSRFProtect(app)
 
 text_splitter = CharacterTextSplitter(separator = "\n", chunk_size=1000, chunk_overlap=200, length_function = len)
 embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
@@ -38,20 +27,6 @@ llm = ChatOpenAI(api_key=OPENAI_API_KEY)
 
 vectordb_path = "./vector_db"
 
-@app.route('/')
-def home():
-    return redirect(url_for('search_view'))
-
-@app.route('/search_view')
-def search_view():
-    return render_template('search.html')
-
-@app.route('/rag_view')
-def rag_view():
-    dbs = [f.name for f in os.scandir(vectordb_path) if f.is_dir()]
-    return render_template('rag.html', dbs = dbs)
-
-@app.route('/query', methods=['POST'])
 def query():
     if request.method == "POST":
         prompt = request.get_json().get("prompt")
@@ -91,14 +66,6 @@ def query():
                 output = stuff_chain({"input_documents": docs, "human_input": prompt}, return_only_outputs=False)
 
                 final_answer = output["output_text"]
-                # prompt = ChatPromptTemplate.from_messages(
-                #     [("system", "Please answer to user's query based on following context.\n\nContext: {context}")]
-                # )
-                
-                
-                # chain = create_stuff_documents_chain(llm, prompt)
-
-                # answer = chain.invoke({"context": docs, "prompt": prompt})
 
                 data = {"success": "ok", "response": final_answer}
                 
@@ -108,12 +75,10 @@ def query():
 
                 return jsonify(data)
         
-@app.route('/uploadDocuments', methods=['POST'])
-@csrf.exempt
 def uploadDocuments():
     # uploaded_files = request.files.getlist('files[]')
+    uploaded_files = ['annualreport2223.pdf', 'Airbus-Annual-Report-2023.pdf']
     dbname = request.form.get('dbname')
-    uploaded_files = ['https://www.airbus.com/sites/g/files/jlcbta136/files/2024-03/Airbus-Annual-Report-2023.pdf', 'https://www.singaporeair.com/saar5/pdf/Investor-Relations/Annual-Report/annualreport2223.pdf']
     if dbname == "":
         return {"success": "db"}
     
@@ -136,8 +101,6 @@ def uploadDocuments():
     else:
         return {"success": "bad"}
 
-@app.route('/dbcreate', methods=['POST'])
-@csrf.exempt
 def dbcreate():
     dbname = request.get_json().get("dbname")
     
@@ -147,11 +110,7 @@ def dbcreate():
     else:
         return {'success': 'bad'}
     
-# import gradio as gr
-# chatbot = gr.Chatbot(avatar_images=["user.png", "bot.jpg"], height=600)
-# clear_but = gr.Button(value="Clear Chat")
-# demo = gr.ChatInterface(fn=search, title="Mediate.com Chatbot Prototype", multimodal=False, retry_btn=None, undo_btn=None, clear_btn=clear_but, chatbot=chatbot)
-
-
-if __name__ == '__main__':
-    app.run(debug=True)
+import gradio as gr
+chatbot = gr.Chatbot(avatar_images=["user.png", "bot.jpg"], height=600)
+clear_but = gr.Button(value="Clear Chat")
+demo = gr.ChatInterface(fn=search, title="Mediate.com Chatbot Prototype", multimodal=False, retry_btn=None, undo_btn=None, clear_btn=clear_but, chatbot=chatbot)