Revert "deleted irrevelant files"

This reverts commit a484acb.

Author: aanushkaguptaa

Emotion Analysis/EmotionAnalysis.ipynb renamed to Emotion Anaylisis/EmotionAnalysis.ipynb

@@ -0,0 +1,125 @@
+import pytesseract
+from PIL import Image
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.common.exceptions import TimeoutException
+from io import BytesIO
+import random
+import os
+import csv
+import time
+import tkinter as tk
+from tkinter import filedialog, messagebox
+
+# Update this line with your Tesseract installation path
+pytesseract.pytesseract.tesseract_cmd = r'C:\Users\kulitesh\Scrape-ML\Tesseract-OCR\tesseract.exe'
+
+def take_screenshot_and_analyze(url, output_csv, save_location, num_screenshots=4):
+    options = Options()
+    options.headless = True
+
+    try:
+        driver = webdriver.Chrome(options=options)
+        driver.get(url)
+        WebDriverWait(driver, 20).until(lambda driver: driver.execute_script('return document.readyState') == 'complete')
+
+        # Create a directory to store screenshots if it doesn't exist
+        screenshot_dir = os.path.join(save_location, "Screenshots")
+        if not os.path.exists(screenshot_dir):
+            os.makedirs(screenshot_dir)
+
+        data = []  # List to store scraped data
+
+        for i in range(num_screenshots):
+            # Scroll down a random amount
+            scroll_amount = random.randint(500, 1000)  # Adjust as needed
+            driver.execute_script(f"window.scrollBy(0, {scroll_amount});")
+            # Add some waiting time after scrolling
+            time.sleep(1)  # Adjust scroll time as needed
+
+            # Capture screenshot
+            screenshot = driver.get_screenshot_as_png()
+            image = Image.open(BytesIO(screenshot))
+
+            # Save screenshot to file
+            screenshot_path = os.path.join(screenshot_dir, f"screenshot_{i + 1}.png")
+            image.save(screenshot_path)
+
+            # Use Tesseract OCR to extract text
+            extracted_text = pytesseract.image_to_string(image)
+            print(f"Extracted Text from screenshot {i + 1}:", extracted_text)
+
+            # Add the extracted text to the data list
+            data.append({"Screenshot": screenshot_path, "Extracted Text": extracted_text})
+
+        # Write the scraped data to a CSV file
+        write_to_csv(data, output_csv, save_location)
+
+    except TimeoutException:
+        print("Timed out waiting for page to load")
+
+    finally:
+        if 'driver' in locals():
+            driver.quit()
+
+def write_to_csv(data, output_csv, save_location):
+    # Define CSV file path
+    csv_file = os.path.join(save_location, output_csv)
+
+    # Write data to CSV file
+    with open(csv_file, 'w', newline='', encoding='utf-8') as file:
+        writer = csv.DictWriter(file, fieldnames=["Screenshot", "Extracted Text"])
+        writer.writeheader()
+        writer.writerows(data)
+
+    print(f"Scraped data written to {csv_file}")
+
+def browse_folder():
+    folder_selected = filedialog.askdirectory()
+    save_location_entry.delete(0, tk.END)
+    save_location_entry.insert(0, folder_selected)
+
+def start_analysis():
+    url_to_analyze = url_entry.get()
+    output_csv = output_csv_entry.get()
+    save_location = save_location_entry.get()
+    
+    if not url_to_analyze or not output_csv or not save_location:
+        messagebox.showwarning("Input Error", "Please fill in all fields.")
+        return
+    
+    take_screenshot_and_analyze(url_to_analyze, output_csv, save_location)
+    messagebox.showinfo("Success", f"Scraped data written to {os.path.join(save_location, output_csv)}")
+
+# Set up the main application window
+root = tk.Tk()
+root.title("Sentiment Analysis Tool")
+
+# Set window size and background color
+root.geometry("600x300")
+root.configure(bg="black")
+
+# URL input
+tk.Label(root, text="Website URL:", bg="black", fg="white").grid(row=0, column=0, padx=10, pady=5, sticky="e")
+url_entry = tk.Entry(root, width=50, bg="white", fg="black")
+url_entry.grid(row=0, column=1, padx=10, pady=5)
+
+# Output CSV file name input
+tk.Label(root, text="Output CSV File Name:", bg="black", fg="white").grid(row=1, column=0, padx=10, pady=5, sticky="e")
+output_csv_entry = tk.Entry(root, width=50, bg="white", fg="black")
+output_csv_entry.grid(row=1, column=1, padx=10, pady=5)
+
+# Save location input
+tk.Label(root, text="Save Location:", bg="black", fg="white").grid(row=2, column=0, padx=10, pady=5, sticky="e")
+save_location_entry = tk.Entry(root, width=50, bg="white", fg="black")
+save_location_entry.grid(row=2, column=1, padx=10, pady=5)
+browse_button = tk.Button(root, text="Browse", command=browse_folder, bg="white", fg="black")
+browse_button.grid(row=2, column=2, padx=10, pady=5)
+
+# Start analysis button
+start_button = tk.Button(root, text="Start Analysis", command=start_analysis, bg="white", fg="black")
+start_button.grid(row=3, column=1, padx=10, pady=20)
+
+# Start the Tkinter event loop
+root.mainloop()

Emotion Analysis/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt renamed to Emotion Anaylisis/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt

@@ -0,0 +1,60 @@
+
+
+<h1 align="center">Amazon Scrapping</h1>
+<blockquote align="center">Scrapping the product lisitng✏️ using python programming language💻.  </blockquote>
+<p align="center">For new data generation <b>Classification part</b> we have writtern a python script to fetch📊, data from the 💻, Amazon website 🌐 and converted into csv files. </p>
+
+
+
+# Introduction
+
+**`Semi-supervised-sequence-learning-Project`** :computer: replication process is done over here and for further analysis creation of new data is required.
+
+- The following script includes the following.
+- `scrapping.py` - Script to scrap the data from Amazon website
+- Product label listing: `Laptop`, `Phones`, `Printers`, `Desktops`, `Monitors`, `Mouse`, `Pendrive`, `Earphones`, `Smart TV`, `Power banks`
+
+
+
+## Dependencies
+
+- Install Selenium using `pip install -U selenium`
+
+- Install Python 3 using the [MSI available in python.org download page](http://www.python.org/download).
+
+- Load the drivers
+
+ ```python
+  from selenium import webdriver
+  
+  browser = webdriver.Firefox()
+  browser.get('http://selenium.dev/')
+ ```
+
+- Selenium Server (optional)
+
+ ```python
+  java -jar selenium-server-standalone-4.0.0.jar
+ ```
+
+## Installation
+
+**1️⃣ Fork the `Semi-supervised-sequence-learning-Project/` repository**  
+Link to [`Semi-supervised-sequence-learning-Project'](https://github.com/sanjay-kv/Semi-supervised-sequence-learning-Project) 
+Follow these instructions on [how to fork a repository](https://help.github.com/en/articles/fork-a-repo)
+
+**2️⃣ Cloning the repository**  
+Once you have set up your fork of the `/Semi-supervised-sequence-learning-Project` repository, you'll want to clone it to your local machine. This is so you can make and test all of your personal edits before adding it to the master version of `/Semi-supervised-sequence-learning-Project`.
+
+Navigate to the location on your computer where you want to host your code. Once in the appropriate folder, run the following command to clone the repository to your local machine.
+
+```bash
+git clone https://github.com/your-username/Semi-supervised-sequence-learning-Project.git
+```
+
+## Final Dataset
+
+1️⃣ Here is the Link to **Final Dataset:** [Drive Link](https://drive.google.com/drive/folders/1HB8FCUVqkQpSbV7syq2ZsZ59kT5B-SGo?usp=sharing)
+
+
+