Upload boot.py

boot.py

@@ -0,0 +1,140 @@
+# -*- 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
+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)
+
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    index = GPTSimpleVectorIndex.from_documents(documents)
+
+    index.save_to_disk('index.json')
+
+    return index
+
+def ask_ai():
+    index = GPTSimpleVectorIndex.load_from_disk('index.json')
+    while True: 
+        query = input("What do you want to ask? ")
+        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"] = 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)