firts_commit

S_A_Front

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+#vader-lexicon model with NLTK
+import pandas as pd
+import matplotlib.pyplot as plt
+import tkinter as tk
+from tkinter import messagebox
+from transformers import pipeline
+
+model_id = "cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual"
+classifier = pipeline("sentiment-analysis", model=model_id)
+
+def process_string(input_string):
+    result =  classifier(input_string)
+    if result[0]['label'] == 'positive':
+        return 'This comment is: positive 😃'
+    elif result[0]['label'] == 'negative':
+        return 'This  comment is: negative 😒'
+    else: 
+        return 'This comment is: positive 😃'
+
+def process_input():
+    input_string = entry.get()
+    if input_string.strip() == "":
+        messagebox.showerror("Error", "Please enter a text.")
+    else:
+        processed_string = process_string(input_string)
+        messagebox.showinfo("Sentiment", f"Comment: {input_string}\n{processed_string}")
+# Create the main application window
+root = tk.Tk()
+root.title("Sentiment Analyser: Hugging face 😃😒")
+
+# Set the size of the window
+root.geometry("500x100")
+
+# Create and place widgets
+label = tk.Label(root, text="Enter a text:")
+label.pack()
+
+entry = tk.Entry(root, width=80)
+entry.pack()
+
+button = tk.Button(root, text="Process", command=process_input, bg='green', fg='white')
+button.pack()
+
+# Run the Tkinter event loop
+root.mainloop()