Upload 18 files

chatbot/.gitattributes

@@ -0,0 +1,3 @@
+build/LMS_Chatbot/LMS_Chatbot.pkg filter=lfs diff=lfs merge=lfs -text
+build/LMS_Chatbot/*.pyz filter=lfs diff=lfs merge=lfs -text
+build/LMS_Chatbot/*.zip filter=lfs diff=lfs merge=lfs -text

chatbot/chatbot

@@ -0,0 +1,7 @@
+-----BEGIN OPENSSH PRIVATE KEY-----
+b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAAAMwAAAAtzc2gtZW
+QyNTUxOQAAACAwnOVBd+a3fINwdYA09Am0riox/ADpztA5vZJdVKFLPgAAAKBUZqnRVGap
+0QAAAAtzc2gtZWQyNTUxOQAAACAwnOVBd+a3fINwdYA09Am0riox/ADpztA5vZJdVKFLPg
+AAAEByNpoQkGuaG3/+yt9+W0vEZPWNAH1W7sKU1lI9v131cDCc5UF35rd8g3B1gDT0CbSu
+KjH8AOnO0Dm9kl1UoUs+AAAAFnlvdXJfZW1haWxAZXhhbXBsZS5jb20BAgMEBQYH
+-----END OPENSSH PRIVATE KEY-----

chatbot/chatbot.pub

@@ -0,0 +1 @@
+ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIDCc5UF35rd8g3B1gDT0CbSuKjH8AOnO0Dm9kl1UoUs+ [email protected]

chatbot/data/createcsv.py

@@ -0,0 +1,83 @@
+import pandas as pd
+from pathlib import Path
+
+DATA = {
+    "greeting": {
+        "patterns": ["Hello", "Hi there", "Hey", "Good morning", "Good evening"],
+        "responses": ["Hello! How can I help you today?", "Hi! What would you like to know about your LMS?"]
+    },
+    "goodbye": {
+        "patterns": ["Goodbye", "Bye", "See you", "Catch you later"],
+        "responses": ["Goodbye! Have a great day.", "See you soon!"]
+    },
+    "courses": {
+        "patterns": ["What courses am I enrolled in?", "List all my courses", "How many credits do I have?"],
+        "responses": [
+            "You are enrolled in several courses. Please check your LMS dashboard.",
+            "Here is the list of your enrolled courses in the LMS.",
+            "You can view your total credits in your academic profile."
+        ]
+    },
+    "grades": {
+        "patterns": ["Show my grades", "How do I view my grades?", "Check my performance"],
+        "responses": [
+            "Your grades are available in the 'Grades' section of the LMS.",
+            "Go to the 'Grades' tab in the LMS to check your performance."
+        ]
+    },
+    "assignment": {
+        "patterns": ["When is the assignment due?", "How do I submit my homework?", "Upload assignment"],
+        "responses": [
+            "You can check assignment deadlines in the 'Assignments' section.",
+            "Upload your homework in the LMS under 'Assignments'."
+        ]
+    },
+    "schedule": {
+        "patterns": ["List all upcoming lectures", "What is the next lecture topic?", "Show timetable"],
+        "responses": [
+            "Upcoming lectures are listed in your calendar on the LMS.",
+            "The next lecture topic is available in the course schedule."
+        ]
+    },
+    "instructor": {
+        "patterns": ["Who is my instructor?", "Who teaches this course?"],
+        "responses": ["You can find your instructor details on the course information page."]
+    },
+    "technical_support": {
+        "patterns": ["How can I reset my password?", "My portal is not loading", "LMS not working"],
+        "responses": [
+            "Go to account settings and click on 'Reset Password'.",
+            "Try clearing your cache or contact IT support."
+        ]
+    },
+    "feedback": {
+        "patterns": ["How do I give feedback?", "I want to share my opinion"],
+        "responses": ["You can provide feedback through the LMS feedback form."]
+    },
+    "resources": {
+        "patterns": ["Where can I find course materials?", "How can I access lecture notes?", "Show learning materials"],
+        "responses": [
+            "Course materials are available under the 'Resources' section.",
+            "Lecture notes are uploaded in the course resources."
+        ]
+    }
+}
+
+# Convert into rows for DataFrame (expand patterns into rows)
+rows = []
+for intent, data in DATA.items():
+    for pattern in data["patterns"]:  # one row per pattern
+        rows.append({
+            "tag": intent,                       # <-- renamed to tag
+            "patterns": pattern,
+            "responses": ";".join(data["responses"])  # keep multiple responses joined
+        })
+
+df = pd.DataFrame(rows, columns=["tag", "patterns", "responses"])
+
+# Save CSV inside data/ folder
+OUT = Path(__file__).resolve().parent.parent / "data" / "intents.csv"
+OUT.parent.mkdir(parents=True, exist_ok=True)
+df.to_csv(OUT, index=False, encoding="utf-8")
+
+print(f"✅ Created intents CSV at {OUT}")

chatbot/data/intents.csv

@@ -0,0 +1,32 @@
+tag,patterns,responses
+greeting,Hello,Hello! How can I help you today?;Hi! What would you like to know about your LMS?
+greeting,Hi there,Hello! How can I help you today?;Hi! What would you like to know about your LMS?
+greeting,Hey,Hello! How can I help you today?;Hi! What would you like to know about your LMS?
+greeting,Good morning,Hello! How can I help you today?;Hi! What would you like to know about your LMS?
+greeting,Good evening,Hello! How can I help you today?;Hi! What would you like to know about your LMS?
+goodbye,Goodbye,Goodbye! Have a great day.;See you soon!
+goodbye,Bye,Goodbye! Have a great day.;See you soon!
+goodbye,See you,Goodbye! Have a great day.;See you soon!
+goodbye,Catch you later,Goodbye! Have a great day.;See you soon!
+courses,What courses am I enrolled in?,You are enrolled in several courses. Please check your LMS dashboard.;Here is the list of your enrolled courses in the LMS.;You can view your total credits in your academic profile.
+courses,List all my courses,You are enrolled in several courses. Please check your LMS dashboard.;Here is the list of your enrolled courses in the LMS.;You can view your total credits in your academic profile.
+courses,How many credits do I have?,You are enrolled in several courses. Please check your LMS dashboard.;Here is the list of your enrolled courses in the LMS.;You can view your total credits in your academic profile.
+grades,Show my grades,Your grades are available in the 'Grades' section of the LMS.;Go to the 'Grades' tab in the LMS to check your performance.
+grades,How do I view my grades?,Your grades are available in the 'Grades' section of the LMS.;Go to the 'Grades' tab in the LMS to check your performance.
+grades,Check my performance,Your grades are available in the 'Grades' section of the LMS.;Go to the 'Grades' tab in the LMS to check your performance.
+assignment,When is the assignment due?,You can check assignment deadlines in the 'Assignments' section.;Upload your homework in the LMS under 'Assignments'.
+assignment,How do I submit my homework?,You can check assignment deadlines in the 'Assignments' section.;Upload your homework in the LMS under 'Assignments'.
+assignment,Upload assignment,You can check assignment deadlines in the 'Assignments' section.;Upload your homework in the LMS under 'Assignments'.
+schedule,List all upcoming lectures,Upcoming lectures are listed in your calendar on the LMS.;The next lecture topic is available in the course schedule.
+schedule,What is the next lecture topic?,Upcoming lectures are listed in your calendar on the LMS.;The next lecture topic is available in the course schedule.
+schedule,Show timetable,Upcoming lectures are listed in your calendar on the LMS.;The next lecture topic is available in the course schedule.
+instructor,Who is my instructor?,You can find your instructor details on the course information page.
+instructor,Who teaches this course?,You can find your instructor details on the course information page.
+technical_support,How can I reset my password?,Go to account settings and click on 'Reset Password'.;Try clearing your cache or contact IT support.
+technical_support,My portal is not loading,Go to account settings and click on 'Reset Password'.;Try clearing your cache or contact IT support.
+technical_support,LMS not working,Go to account settings and click on 'Reset Password'.;Try clearing your cache or contact IT support.
+feedback,How do I give feedback?,You can provide feedback through the LMS feedback form.
+feedback,I want to share my opinion,You can provide feedback through the LMS feedback form.
+resources,Where can I find course materials?,Course materials are available under the 'Resources' section.;Lecture notes are uploaded in the course resources.
+resources,How can I access lecture notes?,Course materials are available under the 'Resources' section.;Lecture notes are uploaded in the course resources.
+resources,Show learning materials,Course materials are available under the 'Resources' section.;Lecture notes are uploaded in the course resources.

chatbot/models/lms_chatbot.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5b09fc9fcc603129da2794a579456133e64038e1316f330f76713bd04f280ed
+size 11745

chatbot/models/responses.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1094bb45c27e767c181ebc2e624768568e1dc16b5066ab71303663cee89f3371
+size 1241

chatbot/requirements.txt

@@ -0,0 +1,6 @@
+pandas
+scikit-learn
+nltk
+joblib
+ttkbootstrap
+

chatbot/src/features.py

@@ -0,0 +1,11 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+
+def build_pipeline(random_state=42):
+    vect = TfidfVectorizer(max_df=0.9, min_df=1, ngram_range=(1,2))
+    clf = LogisticRegression(max_iter=1000, random_state=random_state)
+    return Pipeline([
+        ("tfidf", vect),
+        ("clf", clf)
+    ])

chatbot/src/gui.py

@@ -0,0 +1,82 @@
+import tkinter as tk
+from tkinter import Canvas, Frame, Scrollbar, Label, Entry, BOTH, RIGHT, LEFT, Y, NW
+from predict import chatbot_response
+
+class ChatbotGUI:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("LMS Chatbot")
+        self.root.geometry("500x600")
+        self.root.minsize(400, 400)
+        self.root.configure(bg="#f0f0f0")
+
+        # Chat frame with canvas for scrolling
+        self.frame = Frame(root, bg="#f0f0f0")
+        self.frame.pack(padx=10, pady=10, fill=BOTH, expand=True)
+
+        self.canvas = Canvas(self.frame, bg="#f0f0f0", highlightthickness=0)
+        self.scrollbar = Scrollbar(self.frame, orient="vertical", command=self.canvas.yview)
+        self.scrollable_frame = Frame(self.canvas, bg="#f0f0f0")
+
+        self.scrollable_frame.bind(
+            "<Configure>",
+            lambda e: self.canvas.configure(scrollregion=self.canvas.bbox("all"))
+        )
+
+        self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor=NW)
+        self.canvas.configure(yscrollcommand=self.scrollbar.set)
+
+        self.canvas.pack(side=LEFT, fill=BOTH, expand=True)
+        self.scrollbar.pack(side=RIGHT, fill=Y)
+
+        # Entry box
+        self.entry = Entry(root, font=("Helvetica", 14), bd=2, relief="groove")
+        self.entry.pack(padx=10, pady=10, fill="x")
+        self.entry.bind("<Return>", self.send_message)
+
+        # Typing indicator
+        self.typing_label = Label(root, text="", bg="#f0f0f0", fg="gray", font=("Helvetica", 10))
+        self.typing_label.pack(pady=(0,5))
+
+    def send_message(self, event):
+        user_input = self.entry.get().strip()
+        if not user_input:
+            return
+        self.entry.delete(0, tk.END)
+        self.add_message(user_input, sender="user")
+        self.typing_label.config(text="Bot is typing...")
+        self.root.after(500, lambda: self.bot_reply(user_input))
+
+    def bot_reply(self, user_input):
+        response = chatbot_response(user_input)
+        self.typing_label.config(text="")
+        self.animate_bot_response(response)
+
+    def add_message(self, message, sender="bot"):
+        bubble = Label(self.scrollable_frame, text=message, wraplength=300, justify="left", 
+                       bg="#DCF8C6" if sender=="user" else "#FFFFFF", 
+                       fg="#000000", font=("Helvetica", 12), padx=10, pady=5, bd=1, relief="solid")
+        bubble.pack(anchor="e" if sender=="user" else "w", pady=5, padx=5)
+        self.canvas.update_idletasks()
+        self.canvas.yview_moveto(1.0)
+
+    def animate_bot_response(self, text, idx=0, message=""):
+        if idx < len(text):
+            message += text[idx]
+            if hasattr(self, "bot_bubble"):
+                self.bot_bubble.config(text=message)
+            else:
+                self.bot_bubble = Label(self.scrollable_frame, text=message, wraplength=300, justify="left", 
+                                        bg="#FFFFFF", fg="#000000", font=("Helvetica", 12), padx=10, pady=5, bd=1, relief="solid")
+                self.bot_bubble.pack(anchor="w", pady=5, padx=5)
+            idx += 1
+            self.root.after(30, self.animate_bot_response, text, idx, message)
+        else:
+            del self.bot_bubble
+            self.canvas.update_idletasks()
+            self.canvas.yview_moveto(1.0)
+
+if __name__ == "__main__":
+    root = tk.Tk()
+    app = ChatbotGUI(root)
+    root.mainloop()

chatbot/src/predict.py

@@ -0,0 +1,12 @@
+import joblib
+from preprocess import clean_text
+import random
+
+# Load model & responses
+model = joblib.load("models/lms_chatbot.joblib")
+responses = joblib.load("models/responses.joblib")
+
+def chatbot_response(user_input: str) -> str:
+    cleaned = clean_text(user_input)
+    tag = model.predict([cleaned])[0]
+    return random.choice(responses.get(tag, ["Sorry, I don't understand."]))

chatbot/src/preprocess.py

@@ -0,0 +1,9 @@
+import re
+import string
+
+def clean_text(text: str) -> str:
+    """Lowercase, remove punctuation, and clean text"""
+    text = text.lower()
+    text = re.sub(f"[{re.escape(string.punctuation)}]", "", text)
+    text = text.strip()
+    return text

chatbot/src/train.py

@@ -0,0 +1,43 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import joblib
+from preprocess import clean_text
+
+# Load dataset
+df = pd.read_csv("data/intents.csv")
+
+# Clean text
+df["patterns"] = df["patterns"].apply(clean_text)
+
+# Features (patterns) and Labels (tags)
+X = df["patterns"]
+y = df["tag"]
+
+# Train/Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Build pipeline
+model = Pipeline([
+    ("tfidf", TfidfVectorizer()),
+    ("clf", MultinomialNB())
+])
+
+# Train
+model.fit(X_train, y_train)
+
+# Evaluate
+y_pred = model.predict(X_test)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+
+# Save model & responses
+joblib.dump(model, "models/lms_chatbot.joblib")
+
+# Save responses by tag
+responses = df.groupby("tag")["responses"].apply(list).to_dict()
+joblib.dump(responses, "models/responses.joblib")
+
+print("✅ Training complete. Model and responses saved.")

chatbot/ssh

@@ -0,0 +1,7 @@
+-----BEGIN OPENSSH PRIVATE KEY-----
+b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAAAMwAAAAtzc2gtZW
+QyNTUxOQAAACAu2M7glBJCZlGCpulYhByrlxxAnRt5fUP27QYQmqPN/gAAAJh15fi6deX4
+ugAAAAtzc2gtZWQyNTUxOQAAACAu2M7glBJCZlGCpulYhByrlxxAnRt5fUP27QYQmqPN/g
+AAAED6iXh8YIMs76hS0gAkFg3XkYck/gm3Bu93ax77Vj+QtS7YzuCUEkJmUYKm6ViEHKuX
+HECdG3l9Q/btBhCao83+AAAAFXZpdmVrc25oOTMzQGdtYWlsLmNvbQ==
+-----END OPENSSH PRIVATE KEY-----

chatbot/ssh.pub

@@ -0,0 +1 @@
+ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIC7YzuCUEkJmUYKm6ViEHKuXHECdG3l9Q/btBhCao83+ [email protected]