magic-8-ball

ThirdAI/Terms and Conditions/Readme.md

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+# ThirdAIApp and NeuralDBClient
+
+This repository contains two components: `ThirdAIApp` and `NeuralDBClient`. `ThirdAIApp` is a graphical user interface (GUI) application for interacting with the ThirdAI neural database client. It allows you to perform training with PDF files and query the database. `NeuralDBClient` is a Python class that serves as a client for interacting with the ThirdAI neural database. It allows you to train the database with PDF files and perform queries to retrieve information.
+
+## ThirdAIApp
+
+### Features
+
+- Insert PDF files for training.
+- Train the neural database client.
+- Enter queries to retrieve information from the database.
+- Display the output in a new window.
+
+### Installation
+
+To run `ThirdAIApp`, you need to have Python and Tkinter installed. You also need the `ThirdAI` library, which you can install using pip:
+
+```bash
+pip install ThirdAI
+```
+
+### Usage
+
+1. Run the `ThirdAIApp.py` script.
+2. The main window will appear.
+3. Click the "Insert File!" button to select a PDF file for training.
+4. Click the "Training" button to train the neural database client with the selected file.
+5. Enter your query in the "Query" field.
+6. Click the "Processing" button to process the query and display the output in a new window.
+7. You can click the "Clear" button to clear the query and file selections.
+
+### Dependencies
+
+- Python 3.x
+- Tkinter
+- ThirdAI
+
+## NeuralDBClient
+
+### Features
+
+- Train the neural database with PDF files.
+- Perform queries on the neural database.
+
+### Installation
+
+To use `NeuralDBClient`, you need to have the `thirdai` library installed, and you'll need an API key from ThirdAI.
+
+You can install the `thirdai` library using pip:
+
+```bash
+pip install thirdai
+```
+
+### Usage
+
+1. Import the `NeuralDBClient` class from `neural_db_client.py`.
+2. Create an instance of the `NeuralDBClient` class, providing your ThirdAI API key as an argument.
+
+   ```python
+   from neural_db_client import NeuralDBClient
+
+   client = NeuralDBClient(api_key="YOUR_API_KEY")
+   ```
+
+3. Train the neural database with PDF files using the `train` method. Provide a list of file paths to the PDF files you want to use for training.
+
+   ```python
+   client.train(file_paths=["file1.pdf", "file2.pdf"])
+   ```
+
+4. Perform queries on the neural database using the `query` method. Provide your query as a string, and the method will return the query results as a string.
+
+   ```python
+   result = client.query(question="What is the capital of France?")
+   ```
+
+### Dependencies
+
+- `thirdai` library
+

ThirdAI/Terms and Conditions/ThirdAI.py

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+from thirdai import licensing, neural_db as ndb
+
+
+class NeuralDBClient:
+    def __init__(self):
+        # Activating ThirdAI Key
+        licensing.activate("ADD-YOUR-THIRDAI-ACTIVATION-KEY")
+
+        # Creating NeuralBD variable to access Neural Database
+        self.db = ndb.NeuralDB(user_id="my_user")
+
+    def train(self, file_paths):
+        # Retrieving path of file
+        insertable_docs = []
+        pdf_files = file_paths
+
+        # Appending PDF file to the Database stack
+        pdf_doc = ndb.PDF(pdf_files)
+        insertable_docs.append(pdf_doc)
+
+        # Inserting/Uploading PDF file to Neural database for training
+        self.db.insert(insertable_docs, train=True)
+
+    def query(self, question):
+        # Searching of required query in neural database
+        search_results = self.db.search(
+            query=question,
+            top_k=2,
+            on_error=lambda error_msg: print(f"Error! {error_msg}"))
+
+        output = ""
+        for result in search_results:
+            output += result.text + "\n\n"
+
+        return output
+

ThirdAI/Terms and Conditions/TkinterUI.py

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+import tkinter as tk
+from tkinter.font import Font
+from tkinter import messagebox
+from tkinter import filedialog
+from ThirdAI import NeuralDBClient as Ndb
+
+
+class ThirdAIApp:
+    """
+    A GUI application for using the ThirdAI neural database client to train and query data.
+    """
+    def __init__(self, root):
+        """
+        Initialize the user interface window.
+
+        Args:
+            root (tk.Tk): The main Tkinter window.
+        """
+        # Initialize the main window
+        self.root = root
+        self.root.geometry("600x500")
+        self.root.title('ThirdAI - T&C')
+
+        # Initialize variables
+        self.path = []
+        self.client = Ndb()
+
+        # GUI elements
+
+        # Labels and buttons
+        self.menu = tk.Label(self.root, text="Terms & Conditions", font=self.custom_font(30), fg='black',
+                             highlightthickness=2, highlightbackground="red")
+        self.menu.place(x=125, y=10)
+
+        self.insert_button = tk.Button(self.root, text="Insert File!", font=self.custom_font(15), fg='black', bg="grey",
+                                       width=10, command=self.file_input)
+        self.insert_button.place(x=245, y=100)
+
+        self.text_box = tk.Text(self.root, wrap=tk.WORD, width=30, height=1)
+        self.text_box.place(x=165, y=150)
+
+        self.training_button = tk.Button(self.root, text="Training", font=self.custom_font(15), fg='black', bg="grey",
+                                         width=10, command=self.training)
+        self.training_button.place(x=245, y=195)
+
+        self.query_label = tk.Label(self.root, text="Query", font=self.custom_font(20), fg='black')
+        self.query_label.place(x=255, y=255)
+
+        self.query_entry = tk.Entry(self.root, font=self.custom_font(20), width=30)
+        self.query_entry.place(x=70, y=300)
+
+        self.processing_button = tk.Button(self.root, text="Processing", font=self.custom_font(15), fg='black',
+                                           bg="grey", width=10, command=self.processing)
+        self.processing_button.place(x=245, y=355)
+
+        self.clear_button = tk.Button(self.root, text="Clear", font=15, fg='black', bg="grey", width=10,
+                                      command=self.clear_all)
+        self.clear_button.place(x=245, y=405)
+
+    @staticmethod
+    def custom_font(size):
+        """
+        Create a custom font with the specified size.
+
+        Args:
+            size (int): The font size.
+
+        Returns:
+            Font: The custom Font object.
+        """
+        return Font(size=size)
+
+    def file_input(self):
+        """
+        Open a file dialog to select a PDF file and display its name in the text box.
+        """
+        file_type = dict(defaultextension=".pdf", filetypes=[("pdf file", "*.pdf")])
+        file_path = filedialog.askopenfilename(**file_type)
+
+        if file_path:
+            self.path.append(file_path)
+            file_name = file_path.split("/")[-1]
+            self.text_box.delete(1.0, tk.END)
+            self.text_box.insert(tk.INSERT, file_name)
+
+    def clear_all(self):
+        """
+        Clear the query entry, text box, and reset the path.
+        """
+        self.query_entry.delete(0, tk.END)
+        self.text_box.delete(1.0, tk.END)
+        self.path.clear()
+
+    def training(self):
+        """
+        Train the neural database client with the selected PDF file.
+        """
+        if not self.path:
+            messagebox.showwarning("No File Selected", "Please select a PDF file before training.")
+            return
+
+        self.client.train(self.path[0])
+
+        messagebox.showinfo("Training Complete", "Training is done!")
+
+    def processing(self):
+        """
+        Process a user query and display the output in a new window.
+        """
+        question = self.query_entry.get()
+
+        # When there is no query submitted by the user
+        if not question:
+            messagebox.showwarning("No Query", "Please enter a query.")
+            return
+
+        output = self.client.query(question)
+        self.display_output(output)
+
+    def display_output(self, output_data):
+        """
+        Display the output data in a new window.
+
+        Args:
+            output_data (str): The output text to be displayed.
+        """
+        output_window = tk.Toplevel(self.root)
+        output_window.title("Output Data")
+        output_window.geometry("500x500")
+
+        output_text = tk.Text(output_window, wrap=tk.WORD, width=50, height=50)
+        output_text.pack(padx=10, pady=10)
+        output_text.insert(tk.END, output_data)
+
+
+if __name__ == "__main__":
+    """
+    Initializing the main application window
+    """
+
+    # Calling the main application window
+    win = tk.Tk()
+    app = ThirdAIApp(win)
+    win.mainloop()

Wikipdedia/flask_rendering.py

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+from flask import Flask, render_template, request
+import practice_beautifulsoap as data
+
+app = Flask(__name__, template_folder='template')
+
+
+@app.route('/', methods=["GET", "POST"])
+def index():
+    languages = data.lang()
+    return render_template('index.html', languages=languages)
+
+
+@app.route("/display", methods=["POST"])
+def output():
+    if request.method == "POST":
+        entered_topic = request.form.get("topic")
+        selected_language = request.form.get("language")
+
+        soup_data = data.data(entered_topic, selected_language)
+        soup_image = data.get_image_urls(entered_topic)
+
+        return render_template('output.html', heading=entered_topic.upper(), data=soup_data,
+                               url=soup_image, language=selected_language)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

Wikipdedia/main.py

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+# This is a sample Python script.
+
+# Press Shift+F10 to execute it or replace it with your code.
+# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    print_hi('PyCharm')
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/

Wikipdedia/practice_beautifulsoap.py

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+from bs4 import BeautifulSoup
+import requests
+
+language_symbols = {}
+
+
+def lang():
+    try:
+        response = requests.get("https://www.wikipedia.org/")
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        for option in soup.find_all('option'):
+            language = option.text
+            symbol = option['lang']
+            language_symbols[language] = symbol
+
+        return list(language_symbols.keys())
+
+    except requests.exceptions.RequestException as e:
+        print("Error fetching language data:", e)
+        return []
+
+
+def data(selected_topic, selected_language):
+    symbol = language_symbols.get(selected_language)
+
+    try:
+        url = f"https://{symbol}.wikipedia.org/wiki/{selected_topic}"
+        data_response = requests.get(url)
+        data_response.raise_for_status()
+        data_soup = BeautifulSoup(data_response.content, 'html.parser')
+
+        main_content = data_soup.find('div', {'id': 'mw-content-text'})
+        filtered_content = ""
+
+        if main_content:
+            for element in main_content.descendants:
+                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+                    filtered_content += "\n" + element.get_text(strip=True).upper() + "\n"
+
+                elif element.name == 'p':
+                    filtered_content += element.get_text(strip=True) + "\n"
+
+        return filtered_content
+
+    except requests.exceptions.RequestException as e:
+        print("Error fetching Wikipedia content:", e)
+        return "Error fetching data."
+
+
+def get_image_urls(query):
+    try:
+        search_url = f"https://www.google.com/search?q={query}&tbm=isch"
+        image_response = requests.get(search_url)
+        image_response.raise_for_status()
+        image_soup = BeautifulSoup(image_response.content, 'html.parser')
+
+        image_urls = []
+        for img in image_soup.find_all('img'):
+            image_url = img.get('src')
+            if image_url and image_url.startswith("http"):
+                image_urls.append(image_url)
+
+        return image_urls[0]
+
+    except requests.exceptions.RequestException as e:
+        print("Error fetching image URLs:", e)
+        return None

Wikipdedia/static/js/output.js

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+function validateForm() {
+    var language = document.getElementById("language").value;
+
+    if (language === "Select") {
+        alert("Please select a language.");
+        return false;
+    }
+}
+