Update app.py

app.py

@@ -1,31 +1,3 @@
-# -*- coding: utf-8 -*-
-"""custom_chatbot.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1xT4n5rN6yNyzf-CO8Pifz0yWCEI4VmjV
-
-# Install the dependicies
-Run the code below to install the depencies we need for our functions
-"""
-
-# Commented out IPython magic to ensure Python compatibility.
-# %pip install llama-index
-# %pip install langchain
-# %pip install gradio
-# %pip install openai
-
-"""### **How to Train with your data. **
-You can use your github repository link. Make sure repository name should be same as given repo.
-"""
-
-!git clone https://github.com/talib-raath/context_data.git
-
-"""# Define the functions
-The following code defines the functions we need to construct the index and query it
-"""
-
 from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
 from langchain import OpenAI
 import sys
@@ -33,16 +5,11 @@ import os
 from IPython.display import Markdown, display
 
 def construct_index(directory_path):
-    # set maximum input size
     max_input_size = 4096
-    # set number of output tokens
     num_outputs = 2000
-    # set maximum chunk overlap
     max_chunk_overlap = 20
-    # set chunk size limit
     chunk_size_limit = 600
 
-    # define LLM
     llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
     prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
 
@@ -61,80 +28,15 @@ def ask_ai():
         response = index.query(query, response_mode="compact")
         display(Markdown(f"Response: <b>{response.response}</b>"))
 
-"""# Set OpenAI API Key
-You can use this key also but it may expire if it does not work you can get his own api key 
-
-**Use this Key** 
-
-
-  "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"
 
 You need an OPENAI API key to be able to run this code.
 
-If you don't have one yet, get it by [signing up](https://platform.openai.com/overview). Then click your account icon on the top right of the screen and select "View API Keys". Create an API key.
 
-Then run the code below and paste your API key into the text input.
-"""
+os.environ["OPENAI_API_KEY"] = "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
 
-os.environ["OPENAI_API_KEY"] = input("Paste your OpenAI key here and hit enter:")
-
-"""#Construct an index
-Now we are ready to construct the index. This will take every file in the folder 'data', split it into chunks, and embed it with OpenAI's embeddings API.
-
-**Notice:** running this code will cost you credits on your OpenAPI account ($0.02 for every 1,000 tokens). If you've just set up your account, the free credits that you have should be more than enough for this experiment.
-"""
 
 construct_index("context_data")
 
-import tkinter as tk
-from llama_index import GPTSimpleVectorIndex, LLMPredictor, PromptHelper
-from langchain import OpenAI
-from IPython.display import Markdown, display
-
-# Define the ask_ai() function
-def ask_ai(question):
-    index = GPTSimpleVectorIndex.load_from_disk('index.json')
-    response = index.query(question, response_mode="compact")
-    return response.response
-
-# Define the GUI
-class ChatBotGUI:
-    def __init__(self, master):
-        self.master = master
-        master.title("Chat Bot")
-
-        # Create a label and an entry for the question
-        self.label = tk.Label(master, text="Ask me anything:")
-        self.label.pack()
-
-        self.entry = tk.Entry(master)
-        self.entry.pack()
-
-        # Create a button to submit the question
-        self.button = tk.Button(master, text="Submit", command=self.submit_question)
-        self.button.pack()
-
-        # Create a text box to display the response
-        self.textbox = tk.Text(master)
-        self.textbox.pack()
-
-    def submit_question(self):
-        question = self.entry.get()
-        response = ask_ai(question)
-        self.textbox.insert(tk.END, "You: " + question + "\n")
-        self.textbox.insert(tk.END, "Bot: " + response + "\n\n")
-        self.entry.delete(0, tk.END)
-
-# Create an instance of the GUI and start the main loop
-
-'''
-root = tk.Tk()
-chatbot_gui = ChatBotGUI(root)
-root.mainloop()
-'''
-
-import gradio as gr
-
 iface = gr.Interface(fn=ask_ai, inputs="text", outputs="text" ,title="Chatbot")
 
 iface.launch(share = True)