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+# Use official Python base image
+FROM python:3.10-slim
+
+# Prevent Python from writing .pyc files & enable buffered output
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies (useful for pandas, numpy, etc.)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirement file
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application files
+COPY . .
+
+# Expose Flask default port
+EXPOSE 5000
+
+# Set Flask environment variables
+ENV FLASK_APP=app.py
+ENV FLASK_RUN_HOST=0.0.0.0
+
+# Run Flask application
+CMD ["flask", "run"]

app.py

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+# from flask import Flask, render_template, request, jsonify
+# import joblib
+# import numpy as np
+# from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
+# from datetime import datetime
+
+# app = Flask(__name__)
+
+# # -----------------------------
+# # Load Models
+# # -----------------------------
+# print("⏳ Loading models...")
+
+# # 1️⃣ Category Classifier
+# clf_model = joblib.load("models/complaint_classifier_rf.pkl")
+# vectorizer = joblib.load("models/tfidf_vectorizer.pkl")
+
+# # 2️⃣ Fine-Tuned BERT Sentiment Model
+# sentiment_path = "models/bert_finetuned"
+# sent_tokenizer = AutoTokenizer.from_pretrained(sentiment_path)
+# sent_model = AutoModelForSequenceClassification.from_pretrained(sentiment_path)
+# sentiment_pipe = pipeline("text-classification", model=sent_model, tokenizer=sent_tokenizer)
+
+# # 3️⃣ Chatbot (T5)
+# chatbot_path = "models/chatbot_t5"
+# chat_tokenizer = AutoTokenizer.from_pretrained(chatbot_path)
+# chat_model = AutoModelForSeq2SeqLM.from_pretrained(chatbot_path)
+# chatbot = pipeline("text2text-generation", model=chat_model, tokenizer=chat_tokenizer)
+
+# print("✅ All models loaded successfully!")
+
+# # In-memory sentiment data (for analytics)
+# sentiment_log = {"Positive": 0, "Negative": 0, "Neutral": 0}
+# time_log = []
+
+# # -----------------------------
+# # Helper Functions
+# # -----------------------------
+# def predict_category(text):
+#     vec = vectorizer.transform([text])
+#     return clf_model.predict(vec)[0]
+
+# def predict_sentiment(text):
+#     result = sentiment_pipe(text, truncation=True, max_length=128)[0]
+#     label = result["label"]
+#     score = round(result["score"] * 100, 2)
+#     if "NEG" in label.upper():
+#         sentiment = "Negative"
+#     elif "POS" in label.upper():
+#         sentiment = "Positive"
+#     else:
+#         sentiment = "Neutral"
+#     return sentiment, score
+
+# def chatbot_reply(msg, category, sentiment):
+#     prompt = f"User: {msg} | Category: {category} | Sentiment: {sentiment}"
+#     reply = chatbot(prompt, max_new_tokens=80, num_return_sequences=1)[0]["generated_text"]
+#     return reply
+
+# # -----------------------------
+# # Routes
+# # -----------------------------
+# @app.route('/')
+# def home():
+#     return render_template('index.html')
+
+# @app.route('/analyze', methods=['POST'])
+# def analyze():
+#     user_text = request.form['user_input'].strip().lower()
+
+#     # 🧠 1️⃣ Check for simple greetings or small talk
+#     smalltalk_responses = {
+#         "hi": "Hey there 👋 How can I help you today?",
+#         "hello": "Hello! How’s your day going?",
+#         "hey": "Hey! What can I do for you?",
+#         "thanks": "You're welcome 😊 Always happy to help!",
+#         "thank you": "No problem! Glad to assist 🙌",
+#         "good morning": "Good morning ☀️ Ready to get your deliveries moving?",
+#         "good evening": "Good evening 🌙 How can I assist?",
+#         "bye": "Goodbye 👋 Take care!",
+#     }
+
+#     for phrase, reply in smalltalk_responses.items():
+#         if user_text.startswith(phrase):
+#             return jsonify({
+#                 "category": "Small Talk",
+#                 "sentiment": "Neutral",
+#                 "confidence": 100,
+#                 "reply": reply
+#             })
+
+#     # 🧩 2️⃣ Continue with complaint classification
+#     category = predict_category(user_text)
+#     sentiment, confidence = predict_sentiment(user_text)
+#     bot_reply = chatbot_reply(user_text, category, sentiment)
+
+#     # 🧠 Log analytics
+#     sentiment_log[sentiment] += 1
+#     time_log.append({
+#         "time": datetime.now().strftime("%H:%M:%S"),
+#         "sentiment": sentiment
+#     })
+
+#     return jsonify({
+#         "category": category,
+#         "sentiment": sentiment,
+#         "confidence": confidence,
+#         "reply": bot_reply
+#     })
+
+# @app.route('/analytics', methods=['GET'])
+# def analytics():
+#     """Return sentiment distribution for chart"""
+#     return jsonify({
+#         "labels": list(sentiment_log.keys()),
+#         "values": list(sentiment_log.values()),
+#         "timeline": time_log[-20:]
+#     })
+
+
+# if __name__ == "__main__":
+#     app.run(debug=True)
+# app.py  -- merged app (auth + DB + ML + chat + debug)
+# app.py  -- merged app (auth + DB + ML + chat + debug) - NO SMALL-TALK
+import os
+import random
+import string
+import threading
+from datetime import datetime, timedelta
+
+from flask import Flask, render_template, request, redirect, url_for, flash, jsonify
+from flask_sqlalchemy import SQLAlchemy
+from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
+from flask_mail import Mail, Message
+from werkzeug.security import generate_password_hash, check_password_hash
+
+# ML imports
+import joblib
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
+
+# ---------------------------
+# Config
+# ---------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DB_PATH = os.path.join(BASE_DIR, "models.db")
+
+app = Flask(__name__)
+app.config['SECRET_KEY'] = os.environ.get("FLASK_SECRET", "super-secret-key")
+app.config['SQLALCHEMY_DATABASE_URI'] = f"sqlite:///{DB_PATH}"
+app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
+
+# Mail config (dev defaults)
+app.config['MAIL_SERVER'] = os.environ.get("MAIL_SERVER", "localhost")
+app.config['MAIL_PORT'] = int(os.environ.get("MAIL_PORT", 1025))
+app.config['MAIL_USERNAME'] = os.environ.get("MAIL_USERNAME", "")
+app.config['MAIL_PASSWORD'] = os.environ.get("MAIL_PASSWORD", "")
+app.config['MAIL_USE_TLS'] = os.environ.get("MAIL_USE_TLS", "False") == "True"
+app.config['MAIL_USE_SSL'] = os.environ.get("MAIL_USE_SSL", "False") == "True"
+app.config['MAIL_DEFAULT_SENDER'] = os.environ.get("MAIL_DEFAULT_SENDER", "noreply@fastship.ai")
+
+# ---------------------------
+# Extensions
+# ---------------------------
+db = SQLAlchemy(app)
+login_manager = LoginManager(app)
+login_manager.login_view = "login"
+mail = Mail(app)
+
+# ---------------------------
+# DB Models
+# ---------------------------
+class User(UserMixin, db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    fullname = db.Column(db.String(120))
+    email = db.Column(db.String(120), unique=True, nullable=False)
+    password_hash = db.Column(db.String(256), nullable=False)
+    phone = db.Column(db.String(32))
+    address = db.Column(db.String(256))
+    is_admin = db.Column(db.Boolean, default=False)
+    created_at = db.Column(db.DateTime, default=datetime.utcnow)
+
+    def set_password(self, password):
+        self.password_hash = generate_password_hash(password)
+
+    def check_password(self, password):
+        return check_password_hash(self.password_hash, password)
+
+
+class OTP(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    email = db.Column(db.String(120), nullable=False)
+    code = db.Column(db.String(8), nullable=False)
+    created_at = db.Column(db.DateTime, default=datetime.utcnow)
+    expires_at = db.Column(db.DateTime, nullable=False)
+
+
+class ChatMessage(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    user_id = db.Column(db.Integer, nullable=True)
+    username = db.Column(db.String(120))
+    address = db.Column(db.String(256))
+    order_id = db.Column(db.String(64), nullable=True)
+    message = db.Column(db.Text, nullable=False)
+    category = db.Column(db.String(120))
+    sentiment = db.Column(db.String(32))
+    status = db.Column(db.String(32), default="Pending")
+    created_at = db.Column(db.DateTime, default=datetime.utcnow)
+
+with app.app_context():
+    db.create_all()
+
+# ---------------------------
+# Login loader
+# ---------------------------
+@login_manager.user_loader
+def load_user(user_id):
+    return User.query.get(int(user_id))
+
+# ---------------------------
+# Utilities
+# ---------------------------
+def gen_otp_code(length=6):
+    return "".join(random.choices(string.digits, k=length))
+
+def send_email(to_email, subject, body):
+    """Try to send email. If SMTP not configured, fallback to printing to console."""
+    try:
+        msg = Message(subject, recipients=[to_email], html=body)
+        mail.send(msg)
+        app.logger.info("Email sent to %s", to_email)
+        return True
+    except Exception as e:
+        app.logger.warning("Could not send email (fallback). Exception: %s", e)
+        print("=== EMAIL Fallback ===")
+        print("To:", to_email)
+        print("Subject:", subject)
+        print(body)
+        print("======================")
+        return False
+
+# ---------------------------
+# ML model loading (attempt) - uses variable names matching your simple script
+# ---------------------------
+models_lock = threading.Lock()
+
+clf_model = None
+vectorizer = None
+sentiment_pipe = None
+sent_model = None
+chatbot = None
+chatbot_model = None
+
+def load_models():
+    global clf_model, vectorizer, sentiment_pipe, sent_model, chatbot, chatbot_model
+    app.logger.info("Loading models from models/ ...")
+
+    # classifier + vectorizer (joblib)
+    try:
+        clf_model = joblib.load(os.path.join("models", "complaint_classifier_rf.pkl"))
+        vectorizer = joblib.load(os.path.join("models", "tfidf_vectorizer.pkl"))
+        app.logger.info("Classifier & vectorizer loaded.")
+    except Exception as e:
+        app.logger.warning("Classifier/vectorizer not loaded: %s", e)
+        clf_model = None
+        vectorizer = None
+
+    # sentiment (transformers)
+    try:
+        sentiment_path = os.path.join("models", "bert_finetuned")
+        sent_tokenizer = AutoTokenizer.from_pretrained(sentiment_path)
+        sent_model = AutoModelForSequenceClassification.from_pretrained(sentiment_path)
+        sentiment_pipe_local = pipeline("text-classification", model=sent_model, tokenizer=sent_tokenizer)
+        sentiment_pipe = sentiment_pipe_local
+        globals()['sentiment_pipe'] = sentiment_pipe
+        globals()['sent_model'] = sent_model
+        app.logger.info("Sentiment model loaded.")
+    except Exception as e:
+        app.logger.warning("Sentiment model not loaded: %s", e)
+        globals()['sentiment_pipe'] = None
+        globals()['sent_model'] = None
+
+    # chatbot (t5)
+    try:
+        chatbot_path = os.path.join("models", "chatbot_t5")
+        chat_tokenizer = AutoTokenizer.from_pretrained(chatbot_path)
+        chatbot_model_local = AutoModelForSeq2SeqLM.from_pretrained(chatbot_path)
+        chatbot_pipe = pipeline("text2text-generation", model=chatbot_model_local, tokenizer=chat_tokenizer)
+        globals()['chatbot'] = chatbot_pipe
+        globals()['chatbot_model'] = chatbot_model_local
+        app.logger.info("Chatbot model loaded.")
+    except Exception as e:
+        app.logger.warning("Chatbot model not loaded: %s", e)
+        globals()['chatbot'] = None
+        globals()['chatbot_model'] = None
+
+with app.app_context():
+    load_models()
+
+# -----------------------------
+# Model functions (model-driven, no heuristics / no small-talk)
+# -----------------------------
+def predict_category(text):
+    """
+    Return predicted category string if model available,
+    otherwise return None (so caller returns models_missing).
+    """
+    if clf_model is None or vectorizer is None:
+        return None
+    try:
+        vec = vectorizer.transform([text])
+        return clf_model.predict(vec)[0]
+    except Exception as e:
+        app.logger.error("Classifier predict error: %s", e)
+        return None
+
+def predict_sentiment(text):
+    """
+    Robust model mapping. Returns (sentiment_label, confidence_score) or (None, None) if model missing/error.
+    """
+    if sentiment_pipe is None:
+        return None, None
+    try:
+        with models_lock:
+            out = sentiment_pipe(text, truncation=True, max_length=128)[0]
+        raw_label = out.get("label", "")
+        score = round(out.get("score", 0.0) * 100, 2)
+
+        # Map LABEL_0 style to real labels if model.config.id2label exists
+        try:
+            cfg = getattr(sent_model, "config", None)
+            if cfg is not None and hasattr(cfg, "id2label"):
+                if raw_label.upper().startswith("LABEL_"):
+                    try:
+                        idx = int(raw_label.split("_")[-1])
+                        mapped = cfg.id2label.get(idx, raw_label)
+                        raw_label = mapped
+                    except Exception:
+                        pass
+        except Exception:
+            pass
+
+        rl = raw_label.upper()
+        if "NEG" in rl:
+            return "Negative", score
+        if "POS" in rl:
+            return "Positive", score
+        if "NEU" in rl:
+            return "Neutral", score
+
+        # default to Neutral to stay model-driven
+        return "Neutral", score
+
+    except Exception as e:
+        app.logger.error("Sentiment pipeline error: %s", e)
+        return None, None
+
+def chatbot_reply(msg, category, sentiment):
+    """
+    Return chatbot-generated string if model exists, otherwise None.
+    """
+    if chatbot is None:
+        return None
+    try:
+        prompt = f"User: {msg} | Category: {category} | Sentiment: {sentiment}"
+        with models_lock:
+            out = chatbot(prompt, max_new_tokens=80, num_return_sequences=1)[0]
+        return out.get("generated_text") or out.get("text") or str(out)
+    except Exception as e:
+        app.logger.error("Chatbot pipeline error: %s", e)
+        return None
+
+# -----------------------------
+# In-memory analytics (optional)
+# -----------------------------
+sentiment_log = {"Positive": 0, "Negative": 0, "Neutral": 0}
+time_log = []
+
+# ---------------------------
+# Debug / Health endpoints
+# ---------------------------
+@app.route('/health')
+def health():
+    return jsonify({
+        "classifier_loaded": clf_model is not None,
+        "vectorizer_loaded": vectorizer is not None,
+        "sentiment_loaded": sentiment_pipe is not None,
+        "chatbot_loaded": chatbot is not None
+    })
+
+@app.route('/debug/sentiment', methods=['GET'])
+def debug_sentiment():
+    text = request.args.get('text', None)
+    if not text:
+        return jsonify({"error": "provide ?text=..."}), 400
+    pipeline_output = None
+    pipeline_error = None
+    id2label = None
+    if sentiment_pipe is not None:
+        try:
+            with models_lock:
+                pipeline_output = sentiment_pipe(text, truncation=True, max_length=128)
+        except Exception as e:
+            pipeline_error = str(e)
+    try:
+        model_obj = sent_model
+        if model_obj is not None and hasattr(model_obj.config, "id2label"):
+            id2label = dict(model_obj.config.id2label)
+    except Exception:
+        id2label = None
+    return jsonify({
+        "text": text,
+        "pipeline_output": pipeline_output,
+        "pipeline_error": pipeline_error,
+        "id2label": id2label
+    })
+
+# ---------------------------
+# Routes: Auth pages
+# ---------------------------
+@app.route('/')
+def index():
+    return redirect(url_for('login'))
+
+@app.route('/register', methods=['GET', 'POST'])
+def register():
+    if request.method == "POST":
+        fullname = request.form.get("fullname", "").strip()
+        email = request.form.get("email", "").strip().lower()
+        password = request.form.get("password", "")
+        phone = request.form.get("phone", "")
+        address = request.form.get("address", "")
+
+        if not fullname or not email or not password:
+            flash("Please fill required fields", "warning")
+            return redirect(url_for("register"))
+
+        if User.query.filter_by(email=email).first():
+            flash("Email already registered. Try logging in.", "warning")
+            return redirect(url_for("login"))
+
+        user = User(fullname=fullname, email=email, phone=phone, address=address)
+        user.set_password(password)
+        db.session.add(user)
+        db.session.commit()
+        flash("Registered successfully. Please log in.", "success")
+        return redirect(url_for("login"))
+    return render_template("register.html")
+
+@app.route('/login', methods=['GET', 'POST'])
+def login():
+    if request.method == "POST":
+        email = request.form.get("email", "").strip().lower()
+        password = request.form.get("password", "")
+        user = User.query.filter_by(email=email).first()
+        if not user or not user.check_password(password):
+            flash("Invalid credentials", "danger")
+            return redirect(url_for("login"))
+        login_user(user)
+        flash("Logged in successfully", "success")
+        return redirect(url_for("chat"))
+    return render_template("login.html")
+
+@app.route('/logout')
+@login_required
+def logout():
+    logout_user()
+    flash("Logged out", "info")
+    return redirect(url_for("login"))
+
+# ---------------------------
+# Forgot / Reset
+# ---------------------------
+@app.route('/forgot', methods=['GET', 'POST'])
+def forgot():
+    if request.method == "POST":
+        email = request.form.get("email", "").strip().lower()
+        user = User.query.filter_by(email=email).first()
+        if not user:
+            flash("Email not registered", "warning")
+            return redirect(url_for("forgot"))
+        code = gen_otp_code()
+        expires = datetime.utcnow() + timedelta(minutes=10)
+        otp = OTP(email=email, code=code, expires_at=expires)
+        db.session.add(otp)
+        db.session.commit()
+
+        body = f"<p>Your FastShip OTP to reset password is <b>{code}</b>. It expires in 10 minutes.</p>"
+        send_email(email, "Your FastShip Password Reset OTP", body)
+        flash("OTP sent to your email (check console if SMTP is not configured).", "info")
+        return redirect(url_for("reset"))
+    return render_template("forgot.html")
+
+@app.route('/reset', methods=['GET', 'POST'])
+def reset():
+    if request.method == "POST":
+        email = request.form.get("email", "").strip().lower()
+        code = request.form.get("otp", "").strip()
+        new_password = request.form.get("password", "")
+
+        otp = OTP.query.filter_by(email=email, code=code).order_by(OTP.created_at.desc()).first()
+        if not otp:
+            flash("Invalid OTP", "danger")
+            return redirect(url_for("reset"))
+        if datetime.utcnow() > otp.expires_at:
+            flash("OTP expired", "danger")
+            return redirect(url_for("forgot"))
+
+        user = User.query.filter_by(email=email).first()
+        if not user:
+            flash("User not found", "danger")
+            return redirect(url_for("register"))
+
+        user.set_password(new_password)
+        db.session.commit()
+
+        OTP.query.filter_by(email=email).delete()
+        db.session.commit()
+
+        flash("Password reset successful. Please login.", "success")
+        return redirect(url_for("login"))
+    return render_template("reset.html")
+
+# ---------------------------
+# Chat page & analyze (direct model flow, NO small-talk)
+# ---------------------------
+@app.route('/chat', methods=['GET', 'POST'])
+@login_required
+def chat():
+    if request.method == "GET":
+        return render_template("chat.html", username=current_user.fullname)
+
+    user_text = request.form.get("user_input", "").strip()
+    order_id = request.form.get("order_id", "").strip()
+    username = current_user.fullname
+    address = current_user.address or ""
+
+    if not user_text:
+        return jsonify({"error": "empty"}), 400
+
+    category = predict_category(user_text)
+    sentiment, confidence = predict_sentiment(user_text)
+    bot_reply = chatbot_reply(user_text, category, sentiment)
+
+    missing = []
+    if category is None:
+        missing.append("category classifier")
+    if sentiment is None:
+        missing.append("sentiment model")
+    if bot_reply is None:
+        missing.append("chatbot model")
+    if missing:
+        return jsonify({
+            "error": "models_missing",
+            "message": f"Required model(s) not loaded: {', '.join(missing)}"
+        }), 503
+
+    # Save message
+    msg = ChatMessage(user_id=current_user.id, username=username, address=address,
+                      order_id=order_id, message=user_text, category=category,
+                      sentiment=sentiment, status="Pending")
+    db.session.add(msg)
+    db.session.commit()
+
+    # Escalate if negative
+    if sentiment == "Negative":
+        if "delivery" in (category or "").lower():
+            assigned = "Delivery Team"
+        elif "refund" in (category or "").lower():
+            assigned = "Finance"
+        elif "damage" in (category or "").lower():
+            assigned = "Warehouse"
+        else:
+            assigned = "Customer Care"
+        msg.status = f"Escalated to {assigned}"
+        db.session.commit()
+
+    sentiment_log.setdefault(sentiment, 0)
+    sentiment_log[sentiment] += 1
+    time_log.append({"time": datetime.now().strftime("%H:%M:%S"), "sentiment": sentiment})
+
+    return jsonify({
+        "category": category,
+        "sentiment": sentiment,
+        "confidence": confidence,
+        "reply": bot_reply
+    })
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    user_text = request.form.get('user_input', '').strip()
+    if not user_text:
+        return jsonify({"error": "empty"}), 400
+
+    category = predict_category(user_text)
+    sentiment, confidence = predict_sentiment(user_text)
+    reply = chatbot_reply(user_text, category, sentiment)
+
+    missing = []
+    if category is None:
+        missing.append("category classifier")
+    if sentiment is None:
+        missing.append("sentiment model")
+    if reply is None:
+        missing.append("chatbot model")
+    if missing:
+        return jsonify({
+            "error": "models_missing",
+            "message": f"Required model(s) not loaded: {', '.join(missing)}"
+        }), 503
+
+    sentiment_log.setdefault(sentiment, 0)
+    sentiment_log[sentiment] += 1
+    time_log.append({"time": datetime.now().strftime("%H:%M:%S"), "sentiment": sentiment})
+
+    return jsonify({
+        "category": category,
+        "sentiment": sentiment,
+        "confidence": confidence,
+        "reply": reply
+    })
+
+# ---------------------------
+# Admin / Dashboard / Analytics
+# ---------------------------
+@app.route('/analytics', methods=['GET'])
+def analytics():
+    return jsonify({
+        "labels": list(sentiment_log.keys()),
+        "values": list(sentiment_log.values()),
+        "timeline": time_log[-20:]
+    })
+
+@app.route('/dashboard')
+@login_required
+def dashboard():
+    total = ChatMessage.query.count()
+    neg = ChatMessage.query.filter_by(sentiment="Negative").count()
+    pos = ChatMessage.query.filter_by(sentiment="Positive").count()
+    neu = ChatMessage.query.filter_by(sentiment="Neutral").count()
+    return render_template("dashboard.html", total=total, neg=neg, pos=pos, neu=neu)
+
+@app.route('/admin/negatives')
+@login_required
+def admin_negatives():
+    if not current_user.is_admin:
+        flash("Admin access required", "danger")
+        return redirect(url_for("chat"))
+    negatives = ChatMessage.query.filter_by(sentiment="Negative").order_by(ChatMessage.created_at.desc()).limit(200).all()
+    return render_template("admin_negatives.html", negatives=negatives)
+
+# ---------------------------
+# Run
+# ---------------------------
+if __name__ == "__main__":
+    with app.app_context():
+        if User.query.filter_by(is_admin=True).count() == 0:
+            admin = User(fullname="Admin", email="admin@fastship.ai", phone="", address="")
+            admin.set_password("admin123")
+            admin.is_admin = True
+            db.session.add(admin)
+            db.session.commit()
+            print("Admin user created: admin@fastship.ai / admin123")
+
+    app.run(debug=True)

makecsv.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import os
+
+# Define file paths
+raw_file = os.path.join('data', 'complaints_dataset.txt')
+clean_csv = os.path.join('data', 'complaints_dataset.csv')
+
+# Load text data
+df = pd.read_csv(raw_file, 
+                 names=['complaint_text', 'category', 'sentiment', 'urgency'])
+
+# Clean data
+df['complaint_text'] = df['complaint_text'].astype(str).str.strip()
+df['category'] = df['category'].astype(str).str.title().str.strip()
+df['sentiment'] = df['sentiment'].astype(str).str.capitalize().str.strip()
+df['urgency'] = df['urgency'].astype(str).str.capitalize().str.strip()
+
+# Remove blanks & duplicates
+df = df.dropna().drop_duplicates()
+
+# Print summary
+print("✅ Dataset cleaned successfully!")
+print("Total Rows:", df.shape[0])
+print("Categories:", df['category'].unique())
+print("Sentiments:", df['sentiment'].unique())
+print("Urgency Levels:", df['urgency'].unique())
+
+# Save clean CSV
+df.to_csv(clean_csv, index=False, encoding='utf-8')
+print(f"\n💾 Clean CSV saved to: {clean_csv}")
+
+
+
+

models/bert_finetuned/config.json

@@ -0,0 +1,40 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "High",
+    "1": "Mixed",
+    "2": "Nan",
+    "3": "Negative",
+    "4": "Neutral",
+    "5": "Positive"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "High": 0,
+    "Mixed": 1,
+    "Nan": 2,
+    "Negative": 3,
+    "Neutral": 4,
+    "Positive": 5
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.57.1",
+  "vocab_size": 30522
+}

models/bert_finetuned/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f52bec55cee880cd15a3b5d7fd34cdb39fda37687a3ff2effe6fb5ff913066d1
+size 267844872

models/bert_finetuned/special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

models/bert_finetuned/tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "special": true
+    },
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

models/bert_finetuned/training_args.bin

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+oid sha256:d8cbecec005080b4bbac951219f279f5417b9bc24736fbc1bb182fa59d8e7abf
+size 5777

models/chatbot_t5/config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

models/chatbot_t5/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": [
+    1
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "4.57.1"
+}

models/chatbot_t5/model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 242041896

models/chatbot_t5/special_tokens_map.json

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+{
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+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
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models/chatbot_t5/spiece.model

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+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
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+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
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+    "<extra_id_82>",
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+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
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+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "extra_special_tokens": {},
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

models/chatbot_t5/training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42fb2a22064282e8f09b4610ba008dd3138e1acc9ae501500af8225913ec9bdd
+size 5777

models/complaint_classifier_rf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d2c197298f8d1bfa907523c9155f4631e6055333948eb3b6a6e520aa8e2ad08
+size 38277841

models/tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f484985441c6e095fe89402b015faac5c278dc224b7140cde747954e5ee2201
+size 51265

proejectremember_andnote.txt

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+if you converting data into csv but if have code have 4 colum and i some line you hacve 5 it give error
+
+
+Ahh — nice, bro. That error happens because XGBoost doesn’t accept sparse matrices (like TF-IDF) in its native DMatrix unless you convert them properly.
+
+logistic ,svm,xboost is good in classification but in good sentiment so thaat why onaa try bert
+
+Encoding UTF-8 in Python is the process of converting human-readable text into a format that computers can store and transmit as raw data (bytes), specifically using the rules of the UTF-8 system.
+Think of it like translating a message into a secret code that a computer understands.
+
+
+Imagine you are telling a story. When you get to the very end, you might say "The End."
+The <|endoftext|> token is exactly like "The End" for a computer model.
+What it means:
+For the computer: "I am finished writing now."
+During training: It helped the computer learn where one story stops and another starts.
+When you get an answer: If the computer decides to use this token, its software stops the response immediately.
+It's a secret signal, hidden from you, that tells the AI to stop talking and give you the final answer.
+
+
+also used 
+
+def detect_mixed(text, sentiment):
+    pos_words = ["good","great","amazing","excellent","perfect","love"]
+    neg_words = ["delay","broken","bad","terrible","refund","lost","not"]
+    pos = any(w in text.lower() for w in pos_words)
+    neg = any(w in text.lower() for w in neg_words)
+
+    if pos and neg:
+        return "Mixed"
+    return sentiment
+result = bert_sentiment(text)[0]
+sentiment = detect_mixed(text, result['label'])
+
+def polarity_override(text, sentiment):
+    text = text.lower()
+    negative_keywords = ["delay", "late", "scammed", "broken", "refund not", "not received", "missing", "cancelled"]
+    positive_keywords = ["delivered", "resolved", "refunded", "on time", "working", "successfully"]
+    
+    if any(word in text for word in negative_keywords):
+        return "Negative"
+    elif any(word in text for word in positive_keywords):
+        return "Positive"
+    return sentiment
+
+
+if length problem apper try to fix bt poolingg=max_length
+
+
+you can seee model diffent after chaing epoch
+by this 
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load training history from Trainer's log
+logs = pd.DataFrame(trainer.state.log_history)
+
+# Keep only relevant columns
+logs = logs.dropna(subset=['loss','eval_loss'], how='all')
+
+plt.figure(figsize=(8,5))
+plt.plot(logs["epoch"], logs["loss"], label="Training Loss")
+if "eval_loss" in logs.columns:
+    plt.plot(logs["epoch"], logs["eval_loss"], label="Validation Loss")
+plt.xlabel("Epoch")
+plt.ylabel("Loss")
+plt.title("Training vs Validation Loss")
+plt.legend()
+plt.grid(True)
+plt.show()
+
+
+
+
+for tranfomer errors
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer
+import numpy as np
+
+model_name = "t5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    learning_rate=2e-4,
+    weight_decay=0.01,
+    eval_strategy="epoch",       # <- use eval_strategy (not evaluation_strategy)
+    save_strategy="epoch",       # save checkpoints every epoch
+    logging_strategy="steps",    # or "epoch"
+    logging_steps=100,
+    logging_dir="./logs",
+    report_to="none"
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_test,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+    # predict_with_generate=True  # enable if you want generated outputs for seq2seq eval
+)
+
+trainer.train()
+    

requirements.txt

@@ -0,0 +1,17 @@
+Flask==3.0.2
+transformers==4.45.1
+torch
+scikit-learn
+joblib
+numpy
+pandas
+
+Flask-Login==0.6.2
+Flask-SQLAlchemy==3.0.3
+Flask-Mail==0.9.1
+transformers==4.45.1
+torch
+joblib
+numpy
+pandas
+sentencepiece

static/script.js

@@ -0,0 +1,97 @@
+const chatForm = document.getElementById("chat-form");
+const userInput = document.getElementById("user-input");
+const chatBox = document.getElementById("chat-box");
+const orderIdEl = document.getElementById("order_id");
+const categoryEl = document.getElementById("category");
+const sentimentEl = document.getElementById("sentiment");
+const negativeBox = document.getElementById("negative-box");
+
+let sentimentChart = null;
+
+// 🧠 Append User Message
+function appendUser(text) {
+    chatBox.innerHTML += `<p class="user-msg">🧑 You: ${text}</p>`;
+    chatBox.scrollTop = chatBox.scrollHeight;
+}
+
+// 🤖 Append Bot Message
+function appendBot(text) {
+    chatBox.innerHTML += `<p class="bot-msg">🤖 Bot: ${text}</p>`;
+    chatBox.scrollTop = chatBox.scrollHeight;
+}
+
+// ⭐ Send chat message
+chatForm.addEventListener("submit", async (e) => {
+    e.preventDefault();
+
+    const text = userInput.value.trim();
+    const order_id = orderIdEl.value.trim();
+    if (!text) return;
+
+    appendUser(text);
+    userInput.value = "";
+
+    try {
+        const res = await fetch("/chat", {
+            method: "POST",
+            headers: { "Content-Type": "application/x-www-form-urlencoded" },
+            body: new URLSearchParams({ user_input: text, order_id })
+        });
+
+        const data = await res.json();
+
+        if (data.error) {
+            appendBot("Sorry, could not process your message.");
+            return;
+        }
+
+        appendBot(data.reply);
+
+        categoryEl.textContent = data.category || "—";
+        sentimentEl.textContent = `${data.sentiment || "—"} (${data.confidence || 0}%)`;
+
+        await updateAnalytics();
+        await updateNegatives();
+    } catch (err) {
+        console.error(err);
+        appendBot("Network error — try again.");
+    }
+});
+
+// 📊 Update Analytics Charts
+async function updateAnalytics() {
+    const res = await fetch("/analytics");
+    const data = await res.json();
+
+    const ctx = document.getElementById("sentimentChart");
+
+    if (sentimentChart) sentimentChart.destroy();
+
+    sentimentChart = new Chart(ctx, {
+        type: "pie",
+        data: {
+            labels: data.labels,
+            datasets: [{
+                data: data.values,
+                backgroundColor: ["#2ecc71", "#e74c3c", "#f1c40f"]
+            }]
+        }
+    });
+}
+
+// ⚠️ Update Negative Messages Box
+async function updateNegatives() {
+    if (!negativeBox) return; // Safety check
+
+    const res = await fetch("/admin/negatives-data");
+    const data = await res.json();
+
+    negativeBox.innerHTML = data.length === 0
+        ? "<p>No negative messages yet.</p>"
+        : data.map(msg =>
+            `<p class="neg-item">⚠️ <b>${msg.username}</b>: ${msg.message}</p>`
+        ).join("");
+}
+
+// auto-refresh negative messages
+setInterval(updateNegatives, 8000);

static/style.css

@@ -0,0 +1,31 @@
+:root{--bg:#f4f7fb;--card:#fff;--accent:#27ae60;--muted:#6b7280}
+*{box-sizing:border-box}
+body{font-family:Inter,system-ui,Arial,sans-serif;background:var(--bg);margin:0;color:#111}
+.topbar{display:flex;justify-content:space-between;align-items:center;padding:12px 20px;background:linear-gradient(90deg,#ffffff,#fbfdfe);border-bottom:1px solid #eee}
+.brand{font-weight:700}
+.nav a{margin-left:12px;text-decoration:none;color:var(--muted)}
+.container{max-width:1100px;margin:22px auto;padding:12px}
+.chat-grid{display:grid;grid-template-columns:1fr 360px;gap:20px}
+.chat-panel{background:var(--card);border-radius:12px;padding:14px;box-shadow:0 8px 24px rgba(16,24,40,0.06)}
+.side-panel{background:var(--card);border-radius:12px;padding:14px;box-shadow:0 8px 24px rgba(16,24,40,0.06)}
+.chat-box{height:360px;overflow:auto;border:1px solid #eef2f7;border-radius:8px;padding:12px;background:#fcfeff}
+.user-msg{text-align:right;margin:8px 0;color:#0f172a}
+.bot-msg{text-align:left;margin:8px 0;color:#064e3b}
+.input-row{display:flex;gap:8px;margin-top:10px}
+#user-input{flex:1;padding:10px;border:1px solid #e6eef6;border-radius:8px}
+#order_id{width:100%;padding:8px;margin-top:8px;border-radius:8px;border:1px solid #e6eef6}
+#send-btn{background:var(--accent);color:#fff;border:none;padding:10px 14px;border-radius:8px;cursor:pointer}
+.meta{display:flex;gap:14px;margin-top:12px;color:var(--muted)}
+.negative-box{height:180px;overflow:auto;border:1px solid #fdecea;background:#fff5f5;padding:8px;border-radius:8px}
+.negative-entry{border-bottom:1px dashed #ffdede;padding:8px;margin-bottom:6px}
+.flashes .flash{padding:10px;border-radius:6px;margin-bottom:8px}
+.flash.success{background:#e6ffed;color:#0b6a3a}
+.flash.warning{background:#fff7e6;color:#7a4d00}
+.flash.danger{background:#ffe6e6;color:#912d2d}
+.footer{padding:12px;text-align:center;color:var(--muted);font-size:13px;margin-top:18px}
+.home-grid{display:flex;gap:20px}
+.home-left{flex:1}
+.home-right{width:420px}
+.btn{display:inline-block;padding:10px 14px;background:var(--accent);color:#fff;border-radius:8px;text-decoration:none}
+.btn.ghost{background:transparent;color:var(--accent);border:1px solid var(--accent)}
+@media(max-width:900px){.chat-grid{grid-template-columns:1fr}.home-grid{flex-direction:column}}

templates/admin_negatives.html

@@ -0,0 +1,21 @@
+{% extends "base.html" %}
+{% block content %}
+<h2>Negative Messages</h2>
+<table class="table">
+<thead>
+<tr><th>Time</th><th>User</th><th>Order ID</th><th>Message</th><th>Category</th><th>Status</th></tr>
+</thead>
+<tbody>
+{% for m in negatives %}
+<tr>
+<td>{{ m.created_at }}</td>
+<td>{{ m.username }}</td>
+<td>{{ m.order_id }}</td>
+<td>{{ m.message }}</td>
+<td>{{ m.category }}</td>
+<td>{{ m.status }}</td>
+</tr>
+{% endfor %}
+</tbody>
+</table>
+{% endblock %}

templates/base.html

@@ -0,0 +1,47 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8" />
+<meta name="viewport" content="width=device-width,initial-scale=1" />
+<title>FastShip AI</title>
+<link rel="icon" href="{{ url_for('static', filename='favicon.ico') }}">
+<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+<header class="topbar">
+<div class="brand">🚚 FastShip AI</div>
+<nav class="nav">
+{% if current_user.is_authenticated %}
+<span class="user">Hello, {{ current_user.fullname }}</span>
+<a href="{{ url_for('dashboard') }}">Dashboard</a>
+{% if current_user.is_admin %}
+<a href="{{ url_for('admin_negatives') }}">Admin</a>
+{% endif %}
+<a href="{{ url_for('logout') }}">Logout</a>
+{% else %}
+<a href="{{ url_for('login') }}">Login</a>
+<a href="{{ url_for('register') }}">Register</a>
+{% endif %}
+</nav>
+</header>
+
+
+<main class="main">
+<div class="container">
+{% with messages = get_flashed_messages(with_categories=true) %}
+{% if messages %}
+<div class="flashes">
+{% for category, msg in messages %}
+<div class="flash {{ category|lower }}">{{ msg }}</div>
+{% endfor %}
+</div>
+{% endif %}
+{% endwith %}
+{% block content %}{% endblock %}
+</div>
+</main>
+
+
+<footer class="footer">© FastShip AI</footer>
+</body>
+</html>

templates/chat.html

@@ -0,0 +1,38 @@
+{% extends "base.html" %}
+{% block content %}
+<div class="chat-grid">
+<section class="chat-panel">
+<h2>Chat</h2>
+<div id="chat-box" class="chat-box" aria-live="polite"></div>
+
+
+<form id="chat-form" class="chat-form" autocomplete="off">
+<input id="order_id" name="order_id" placeholder="Order ID (optional)" />
+<div class="input-row">
+<input id="user-input" name="user_input" placeholder="Type your message..." />
+<button id="send-btn" type="submit">Send</button>
+</div>
+</form>
+
+
+<div class="meta">
+<div><strong>Category:</strong> <span id="category">—</span></div>
+<div><strong>Sentiment:</strong> <span id="sentiment">—</span></div>
+</div>
+</section>
+
+
+<aside class="side-panel">
+<h3>Live Analytics</h3>
+<canvas id="sentimentChart" height="140"></canvas>
+
+
+<h3>Recent Negatives</h3>
+<div id="negative-box" class="negative-box"></div>
+</aside>
+</div>
+
+
+<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+<script src="{{ url_for('static', filename='script.js') }}"></script>
+{% endblock %}

templates/dashboard.html

@@ -0,0 +1,7 @@
+{% extends "base.html" %}
+{% block content %}
+<h2>Dashboard</h2>
+<p>Total messages: {{ total }}</p>
+<p>Positive: {{ pos }} | Neutral: {{ neu }} | Negative: {{ neg }}</p>
+<p><a href="{{ url_for('admin_negatives') }}">View negative messages (admin)</a></p>
+{% endblock %}

templates/forgot.html

@@ -0,0 +1,9 @@
+{% extends "base.html" %}
+{% block content %}
+<h2>Forgot Password</h2>
+<form method="post">
+  <label>Enter your registered email</label><br/>
+  <input name="email" type="email" required><br/>
+  <button type="submit">Send OTP</button>
+</form>
+{% endblock %}

templates/index.html

@@ -0,0 +1,30 @@
+{% extends "base.html" %}
+{% block content %}
+<div class="home-grid">
+<div class="home-left">
+<h2>🤖 FastShip AI Assistant</h2>
+<p>Login to chat with the assistant or test the demo below.</p>
+<a class="btn" href="{{ url_for('login') }}">Login</a>
+<a class="btn ghost" href="{{ url_for('register') }}">Register</a>
+</div>
+<div class="home-right">
+<h3>Live Demo</h3>
+<div id="chat-box-mini" class="chat-box-mini"></div>
+<div class="input-area">
+<input id="user-input-mini" type="text" placeholder="Try: "Where's my order?"" />
+<button id="send-mini" class="btn">Send</button>
+</div>
+</div>
+</div>
+<script>
+// tiny demo handler calling /analyze
+document.getElementById('send-mini').addEventListener('click', async function(){
+const t = document.getElementById('user-input-mini').value.trim();
+if(!t) return;
+const r = await fetch('/analyze', {method:'POST', headers:{'Content-Type':'application/x-www-form-urlencoded'}, body: new URLSearchParams({user_input:t})});
+const js = await r.json();
+const el = document.getElementById('chat-box-mini');
+el.innerHTML += `<div><b>You:</b> ${t}</div><div><b>Bot:</b> ${js.reply}</div>`;
+});
+</script>
+{% endblock %}

templates/login.html

@@ -0,0 +1,120 @@
+{% extends "base.html" %}
+{% block content %}
+
+<style>
+  .auth-wrapper {
+    min-height: 80vh;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    background: linear-gradient(135deg, #4a90e2, #7b4397);
+    padding: 20px;
+  }
+
+  .auth-card {
+    width: 100%;
+    max-width: 420px;
+    background: #ffffff;
+    border-radius: 12px;
+    box-shadow: 0 10px 30px rgba(0,0,0,0.15);
+    padding: 26px 22px 28px;
+    box-sizing: border-box;
+  }
+
+  .auth-title {
+    text-align: center;
+    margin-bottom: 8px;
+    font-size: 24px;
+    color: #333;
+  }
+
+  .auth-subtitle {
+    text-align: center;
+    font-size: 13px;
+    color: #777;
+    margin-bottom: 20px;
+  }
+
+  .auth-form label {
+    font-size: 14px;
+    color: #444;
+  }
+
+  .auth-form input {
+    width: 100%;
+    padding: 10px;
+    margin: 6px 0 16px;
+    border-radius: 6px;
+    border: 1px solid #ccc;
+    font-size: 14px;
+    outline: none;
+    box-sizing: border-box;
+  }
+
+  .auth-form input:focus {
+    border-color: #4a90e2;
+    box-shadow: 0 0 0 2px rgba(74,144,226,0.2);
+  }
+
+  .auth-btn {
+    width: 100%;
+    padding: 10px;
+    border-radius: 6px;
+    border: none;
+    background: #4a90e2;
+    color: #fff;
+    font-size: 15px;
+    font-weight: 600;
+    cursor: pointer;
+    margin-top: 4px;
+  }
+
+  .auth-btn:hover {
+    background: #3a7bd5;
+  }
+
+  .auth-extra {
+    margin-top: 12px;
+    font-size: 13px;
+    text-align: center;
+  }
+
+  .auth-extra a {
+    color: #4a90e2;
+    text-decoration: none;
+    font-weight: 600;
+  }
+
+  .auth-extra a:hover {
+    text-decoration: underline;
+  }
+
+  @media (max-width: 480px) {
+    .auth-card {
+      padding: 22px 16px 24px;
+    }
+  }
+</style>
+
+<div class="auth-wrapper">
+  <div class="auth-card">
+    <h2 class="auth-title">Login</h2>
+    <p class="auth-subtitle">Welcome back! Please enter your details.</p>
+
+    <form method="post" class="auth-form">
+      <label>Email</label><br/>
+      <input name="email" type="email" required><br/>
+
+      <label>Password</label><br/>
+      <input name="password" type="password" required><br/>
+
+      <button type="submit" class="auth-btn">Login</button>
+    </form>
+
+    <p class="auth-extra">
+      <a href="{{ url_for('forgot') }}">Forgot password?</a>
+    </p>
+  </div>
+</div>
+
+{% endblock %}

templates/register.html

@@ -0,0 +1,112 @@
+{% extends "base.html" %}
+{% block content %}
+
+<style>
+  .auth-wrapper {
+    min-height: 80vh;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    background: linear-gradient(135deg, #4a90e2, #7b4397);
+    padding: 20px;
+  }
+
+  .auth-card {
+    width: 100%;
+    max-width: 480px;
+    background: #ffffff;
+    border-radius: 12px;
+    box-shadow: 0 10px 30px rgba(0,0,0,0.15);
+    padding: 26px 22px 28px;
+    box-sizing: border-box;
+  }
+
+  .auth-title {
+    text-align: center;
+    margin-bottom: 8px;
+    font-size: 24px;
+    color: #333;
+  }
+
+  .auth-subtitle {
+    text-align: center;
+    font-size: 13px;
+    color: #777;
+    margin-bottom: 20px;
+  }
+
+  .auth-form label {
+    font-size: 14px;
+    color: #444;
+  }
+
+  .auth-form input,
+  .auth-form textarea {
+    width: 100%;
+    padding: 10px;
+    margin: 6px 0 16px;
+    border-radius: 6px;
+    border: 1px solid #ccc;
+    font-size: 14px;
+    outline: none;
+    box-sizing: border-box;
+    resize: vertical;
+  }
+
+  .auth-form input:focus,
+  .auth-form textarea:focus {
+    border-color: #4a90e2;
+    box-shadow: 0 0 0 2px rgba(74,144,226,0.2);
+  }
+
+  .auth-btn {
+    width: 100%;
+    padding: 10px;
+    border-radius: 6px;
+    border: none;
+    background: #4a90e2;
+    color: #fff;
+    font-size: 15px;
+    font-weight: 600;
+    cursor: pointer;
+    margin-top: 4px;
+  }
+
+  .auth-btn:hover {
+    background: #3a7bd5;
+  }
+
+  @media (max-width: 480px) {
+    .auth-card {
+      padding: 22px 16px 24px;
+    }
+  }
+</style>
+
+<div class="auth-wrapper">
+  <div class="auth-card">
+    <h2 class="auth-title">Register</h2>
+    <p class="auth-subtitle">Create your account to get started.</p>
+
+    <form method="post" class="auth-form">
+      <label>Full name</label><br/>
+      <input name="fullname" required><br/>
+
+      <label>Email</label><br/>
+      <input name="email" type="email" required><br/>
+
+      <label>Password</label><br/>
+      <input name="password" type="password" required><br/>
+
+      <label>Phone</label><br/>
+      <input name="phone"><br/>
+
+      <label>Address</label><br/>
+      <textarea name="address" rows="3"></textarea><br/>
+
+      <button type="submit" class="auth-btn">Register</button>
+    </form>
+  </div>
+</div>
+
+{% endblock %}

templates/reset.html

@@ -0,0 +1,10 @@
+{% extends "base.html" %}
+{% block content %}
+<h2>Reset Password</h2>
+<form method="post">
+  <label>Email</label><br/><input name="email" type="email" required><br/>
+  <label>OTP (check email)</label><br/><input name="otp" required><br/>
+  <label>New password</label><br/><input name="password" type="password" required><br/>
+  <button type="submit">Reset Password</button>
+</form>
+{% endblock %}