From 650142d56e480048efbfce410a182ae9cfae8b9c Mon Sep 17 00:00:00 2001
From: Tejaswani <tejaswani394@gmail.com>
Date: Mon, 8 Sep 2025 12:27:09 +0530
Subject: [PATCH] Added voice-to-SMS input feature

---
 __pycache__/app.cpython-312.pyc               | Bin 0 -> 93269 bytes
 ...ensemble_classifier_method.cpython-312.pyc | Bin 0 -> 29392 bytes
 __pycache__/export_feature.cpython-312.pyc    | Bin 0 -> 3168 bytes
 __pycache__/smart_preprocess.cpython-312.pyc  | Bin 0 -> 3010 bytes
 __pycache__/speech_handler.cpython-312.pyc    | Bin 0 -> 7308 bytes
 __pycache__/speech_to_text.cpython-312.pyc    | Bin 0 -> 14711 bytes
 app.py                                        | 363 +++++++++++-------
 requirements.txt                              |  21 +-
 speech_handler.py                             | 151 ++++++++
 speech_to_text.py                             | 313 +++++++++++++++
 types.py                                      |  53 +++
 11 files changed, 752 insertions(+), 149 deletions(-)
 create mode 100644 __pycache__/app.cpython-312.pyc
 create mode 100644 __pycache__/ensemble_classifier_method.cpython-312.pyc
 create mode 100644 __pycache__/export_feature.cpython-312.pyc
 create mode 100644 __pycache__/smart_preprocess.cpython-312.pyc
 create mode 100644 __pycache__/speech_handler.cpython-312.pyc
 create mode 100644 __pycache__/speech_to_text.cpython-312.pyc
 create mode 100644 speech_handler.py
 create mode 100644 speech_to_text.py
 create mode 100644 types.py

diff --git a/__pycache__/app.cpython-312.pyc b/__pycache__/app.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..307461f685488466787ebdb43809aa4b6fabc083
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diff --git a/app.py b/app.py
index 41f7417..e90ad6b 100644
--- a/app.py
+++ b/app.py
@@ -1,76 +1,48 @@
+from typing import Dict, Any, Optional, List, Union, TypedDict, Callable
 import streamlit as st
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
+import numpy as np
+import json
+from datetime import datetime
+import torch
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-from datetime import datetime, timedelta
-
-# Assuming these files exist in the same directory
-from export_feature import export_results_button 
-# from ensemble_classifier_method import EnsembleSpamClassifier, ModelPerformanceTracker, PredictionResult
-# Dummy classes if you don't have the actual files for testing
-try:
-    from ensemble_classifier_method import EnsembleSpamClassifier, ModelPerformanceTracker, PredictionResult
-except ImportError:
-    st.warning("`ensemble_classifier_method.py` not found. Using dummy classes. Please provide the actual file for full functionality.")
-    class PredictionResult:
-        def __init__(self, label, score, spam_probability=None):
-            self.label = label
-            self.score = score
-            self.spam_probability = spam_probability
-    class ModelPerformanceTracker:
-        def __init__(self):
-            self.stats = {}
-        def update_performance(self, model_name, correct): pass
-        def get_all_stats(self): return {}
-        def save_to_file(self, filename): pass
-    class EnsembleSpamClassifier:
-        def __init__(self, performance_tracker):
-            self.performance_tracker = performance_tracker
-            self.default_weights = {"DistilBERT": 0.25, "BERT": 0.25, "RoBERTa": 0.25, "ALBERT": 0.25}
-            self.model_weights = self.default_weights.copy()
-        def update_model_weights(self, weights): self.model_weights.update(weights)
-        def get_model_weights(self): return self.model_weights
-        def get_ensemble_prediction(self, predictions, method):
-            # Dummy implementation for ensemble prediction
-            if not predictions:
-                return {'label': 'UNKNOWN', 'confidence': 0.0, 'spam_probability': 0.0, 'method': method, 'details': 'No model predictions'}
-            
-            # Simple majority voting for dummy
-            spam_votes = sum(1 for p in predictions.values() if p['label'] == 'SPAM')
-            ham_votes = sum(1 for p in predictions.values() if p['label'] == 'HAM')
-            
-            if spam_votes > ham_votes:
-                label = 'SPAM'
-                score = sum(p['score'] for p in predictions.values() if p['label'] == 'SPAM') / spam_votes if spam_votes else 0
-                spam_prob = score # Simplified
-            elif ham_votes > spam_votes:
-                label = 'HAM'
-                score = sum(p['score'] for p in predictions.values() if p['label'] == 'HAM') / ham_votes if ham_votes else 0
-                spam_prob = 1 - score # Simplified
-            else: # Tie or no clear majority, default to HAM for safety or SPAM for caution
-                label = 'HAM' 
-                score = 0.5
-                spam_prob = 0.5
-            return {'label': label, 'confidence': score, 'spam_probability': spam_prob, 'method': method, 'details': f'Dummy {method} applied'}
-        
-        def get_all_predictions(self, predictions):
-            # Dummy method to return results for all ensemble methods
-            dummy_results = {}
-            for method_key in ["majority_voting", "weighted_average", "confidence_weighted", "adaptive_threshold", "meta_ensemble"]:
-                dummy_results[method_key] = self.get_ensemble_prediction(predictions, method_key)
-            return dummy_results
-        
-# Core Python imports
+from collections import defaultdict
+from io import StringIO
 import time
 import re
-from datetime import datetime
-from pathlib import Path
-import numpy as np
-from typing import Dict, List, Tuple, Any, Optional
-from io import StringIO
-import torch
-from collections import defaultdict # Added for easier analytics data aggregation
+
+# Custom type definitions
+class MethodStats(TypedDict):
+    count: int
+    spam: int
+    confidences: List[float]
+
+class ModelStats(TypedDict):
+    spam: int
+    ham: int
+    total: int
+
+# Session state type definition
+class SpamlyserSessionState:
+    """Type-safe session state container for Spamlyser"""
+    def __init__(self):
+        self.classification_history: List[Dict[str, Any]] = []
+        self.ensemble_history: List[Dict[str, Any]] = []
+        self.model_stats: Dict[str, ModelStats] = {}
+        self.ensemble_tracker = None
+        self.ensemble_classifier = None
+        self.loaded_models: Dict[str, Any] = {}
+
+# Import app core and ensemble functionality
+import ensemble_classifier_method as ecm
+from ensemble_classifier_method import ModelPerformanceTracker, EnsembleSpamClassifier
+
+# Import other local modules
+from export_feature import export_results_button
+from speech_handler import SpeechHandler
+from speech_to_text import SpeechToText
 
 # --- Streamlit Page Configuration ---
 st.set_page_config(
@@ -193,20 +165,16 @@ def get_all_predictions(self, predictions):
         ]
     })
 
-# --- Session State Initialization ---
-if 'classification_history' not in st.session_state:
-    st.session_state.classification_history = []
-if 'model_stats' not in st.session_state:
-    st.session_state.model_stats = {model: {'spam': 0, 'ham': 0, 'total': 0} for model in ["DistilBERT", "BERT", "RoBERTa", "ALBERT"]}
-if 'ensemble_tracker' not in st.session_state:
-    st.session_state.ensemble_tracker = ModelPerformanceTracker()
-if 'ensemble_classifier' not in st.session_state:
-    st.session_state.ensemble_classifier = EnsembleSpamClassifier(performance_tracker=st.session_state.ensemble_tracker)
-if 'ensemble_history' not in st.session_state:
-    st.session_state.ensemble_history = []
-if 'loaded_models' not in st.session_state:
-    st.session_state.loaded_models = {model_name: None for model_name in ["DistilBERT", "BERT", "RoBERTa", "ALBERT"]}
-
+# Custom type for session state
+class SessionState:
+    def __init__(self):
+        self.classification_history: List[Dict[str, Any]] = []
+        self.model_stats: Dict[str, ModelStats] = {}
+        self.ensemble_tracker: ModelPerformanceTracker = ModelPerformanceTracker()
+        self.ensemble_classifier: EnsembleSpamClassifier = EnsembleSpamClassifier(performance_tracker=self.ensemble_tracker)
+        self.ensemble_history: List[Dict[str, Any]] = []
+        self.loaded_models: Dict[str, Any] = {}
+        self.speech_handler: SpeechHandler = SpeechHandler()
 
 # --- Model Configurations ---
 MODEL_OPTIONS = {
@@ -269,6 +237,33 @@ def get_all_predictions(self, predictions):
     }
 }
 
+# --- Session State Initialization ---
+def init_session_state():
+    """Initialize session state with proper typing"""
+    if 'classification_history' not in st.session_state:
+        st.session_state.classification_history = []
+    if 'ensemble_history' not in st.session_state:
+        st.session_state.ensemble_history = []
+    if 'model_stats' not in st.session_state:
+        st.session_state.model_stats = {
+            model: {'spam': 0, 'ham': 0, 'total': 0}
+            for model in MODEL_OPTIONS.keys()
+        }
+    if 'ensemble_tracker' not in st.session_state:
+        st.session_state.ensemble_tracker = ecm.ModelPerformanceTracker()
+    if 'ensemble_classifier' not in st.session_state:
+        st.session_state.ensemble_classifier = ecm.EnsembleSpamClassifier(
+            performance_tracker=st.session_state.ensemble_tracker
+        )
+    if 'loaded_models' not in st.session_state:
+        st.session_state.loaded_models = {
+            model_name: None for model_name in MODEL_OPTIONS.keys()
+        }
+    if 'speech_handler' not in st.session_state:
+        st.session_state.speech_handler = SpeechHandler()
+
+init_session_state()
+
 # --- Header ---
 st.markdown("""
 <div style="text-align: center; padding: 20px 0; background: linear-gradient(90deg, #1a1a1a, #2d2d2d); border-radius: 15px; margin-bottom: 30px; border: 1px solid #404040;">
@@ -284,9 +279,18 @@ def get_all_predictions(self, predictions):
 # --- Sidebar ---
 with st.sidebar:
 
-    # --- Dark Mode Toggle ---
+    # --- Initialize Session State ---
     if 'dark_mode' not in st.session_state:
         st.session_state.dark_mode = False
+        st.session_state.classification_history = []
+        st.session_state.ensemble_history = []
+        st.session_state.model_stats = {}
+        tracker = ecm.ModelPerformanceTracker()
+        st.session_state.ensemble_tracker = tracker
+        st.session_state.ensemble_classifier = ecm.EnsembleSpamClassifier(
+            performance_tracker=tracker
+        )
+        st.session_state.loaded_models = {}
     st.markdown("""
     <div style="text-align: center; padding: 20px; background: linear-gradient(145deg, #1e1e1e, #2a2a2a); border-radius: 15px; margin-bottom: 20px;">
         <h3 style="color: #00d4aa; margin: 0;">Analysis Mode</h3>
@@ -355,37 +359,79 @@ def get_all_predictions(self, predictions):
 
     # Sidebar Overall Stats
     st.markdown("### 📊 Overall Statistics")
-    total_single_predictions = sum(st.session_state.model_stats[model]['total'] for model in MODEL_OPTIONS)
-    total_ensemble_predictions = len(st.session_state.ensemble_history)
-    total_predictions_overall = total_single_predictions + total_ensemble_predictions
 
-    st.markdown(f"""
-    <div class="metric-container">
-        <p style="color: #00d4aa; font-size: 1.2rem; margin-bottom: 5px;">Total Predictions</p>
-        <h3 style="color: #eee; margin-top: 0;">{total_predictions_overall}</h3>
-    </div>
-    """, unsafe_allow_html=True)
+    # Safely calculate statistics with proper error handling
+    try:
+        # Ensure session state is properly initialized
+        if not hasattr(st.session_state, 'model_stats'):
+            st.session_state.model_stats = {model: {'spam': 0, 'ham': 0, 'total': 0} for model in MODEL_OPTIONS.keys()}
+        if not hasattr(st.session_state, 'ensemble_history'):
+            st.session_state.ensemble_history = []
+
+        total_single_predictions = sum(
+            st.session_state.model_stats.get(model, {}).get('total', 0)
+            for model in MODEL_OPTIONS.keys()
+        )
+        total_ensemble_predictions = len(st.session_state.ensemble_history)
+        total_predictions_overall = total_single_predictions + total_ensemble_predictions
 
-    overall_spam_count = sum(st.session_state.model_stats[model]['spam'] for model in MODEL_OPTIONS) + \
-                         sum(1 for entry in st.session_state.ensemble_history if entry['prediction'] == 'SPAM')
-    overall_ham_count = sum(st.session_state.model_stats[model]['ham'] for model in MODEL_OPTIONS) + \
-                        sum(1 for entry in st.session_state.ensemble_history if entry['prediction'] == 'HAM')
-    col_spam, col_ham = st.columns(2)
-    with col_spam:
         st.markdown(f"""
-        <div class="metric-container spam-alert" style="padding: 15px;">
-            <p style="color: #ff6b6b; font-size: 1rem; margin-bottom: 5px;">Spam Count</p>
-            <h4 style="color: #ff6b6b; margin-top: 0;">{overall_spam_count}</h4>
+        <div class="metric-container">
+            <p style="color: #00d4aa; font-size: 1.2rem; margin-bottom: 5px;">Total Predictions</p>
+            <h3 style="color: #eee; margin-top: 0;">{total_predictions_overall}</h3>
         </div>
         """, unsafe_allow_html=True)
-    with col_ham:
-        st.markdown(f"""
-        <div class="metric-container ham-safe" style="padding: 15px;">
-            <p style="color: #6bff6b; font-size: 1rem; margin-bottom: 5px;">Ham Count</p>
-            <h4 style="color: #6bff6b; margin-top: 0;">{overall_ham_count}</h4>
+
+        overall_spam_count = sum(
+            st.session_state.model_stats.get(model, {}).get('spam', 0)
+            for model in MODEL_OPTIONS.keys()
+        ) + sum(1 for entry in st.session_state.ensemble_history if entry['prediction'] == 'SPAM')
+
+        overall_ham_count = sum(
+            st.session_state.model_stats.get(model, {}).get('ham', 0)
+            for model in MODEL_OPTIONS.keys()
+        ) + sum(1 for entry in st.session_state.ensemble_history if entry['prediction'] == 'HAM')
+
+        col_spam, col_ham = st.columns(2)
+        with col_spam:
+            st.markdown(f"""
+            <div class="metric-container spam-alert" style="padding: 15px;">
+                <p style="color: #ff6b6b; font-size: 1rem; margin-bottom: 5px;">Spam Count</p>
+                <h4 style="color: #ff6b6b; margin-top: 0;">{overall_spam_count}</h4>
+            </div>
+            """, unsafe_allow_html=True)
+        with col_ham:
+            st.markdown(f"""
+            <div class="metric-container ham-safe" style="padding: 15px;">
+                <p style="color: #6bff6b; font-size: 1rem; margin-bottom: 5px;">Ham Count</p>
+                <h4 style="color: #6bff6b; margin-top: 0;">{overall_ham_count}</h4>
+            </div>
+            """, unsafe_allow_html=True)
+    except (AttributeError, KeyError, TypeError):
+        # Handle case where session state is not properly initialized
+        st.markdown("""
+        <div class="metric-container">
+            <p style="color: #00d4aa; font-size: 1.2rem; margin-bottom: 5px;">Total Predictions</p>
+            <h3 style="color: #eee; margin-top: 0;">0</h3>
         </div>
         """, unsafe_allow_html=True)
 
+        col_spam, col_ham = st.columns(2)
+        with col_spam:
+            st.markdown("""
+            <div class="metric-container spam-alert" style="padding: 15px;">
+                <p style="color: #ff6b6b; font-size: 1rem; margin-bottom: 5px;">Spam Count</p>
+                <h4 style="color: #ff6b6b; margin-top: 0;">0</h4>
+            </div>
+            """, unsafe_allow_html=True)
+        with col_ham:
+            st.markdown("""
+            <div class="metric-container ham-safe" style="padding: 15px;">
+                <p style="color: #6bff6b; font-size: 1rem; margin-bottom: 5px;">Ham Count</p>
+                <h4 style="color: #6bff6b; margin-top: 0;">0</h4>
+            </div>
+            """, unsafe_allow_html=True)
+
 
 # --- Model Loading Helpers ---
 @st.cache_resource
@@ -414,8 +460,8 @@ def _load_model_cached(model_id):
         if tokenizer is None or model is None:
             return None
         pipe = pipeline(
-            "text-classification", 
-            model=model, 
+            "text-classification",
+            model=model,
             tokenizer=tokenizer,
             device=0 if torch.cuda.is_available() else -1
         )
@@ -424,35 +470,23 @@ def _load_model_cached(model_id):
         st.error(f"❌ Error creating pipeline for {model_id}: {str(e)}")
         return None
 
+
 def load_model_if_needed(model_name, _progress_callback=None):
-    if st.session_state.loaded_models[model_name] is None:
+    """Load a model if not already loaded, with optional progress callback."""
+    if model_name not in st.session_state.loaded_models or st.session_state.loaded_models[model_name] is None:
+        if _progress_callback:
+            _progress_callback(f"Loading {model_name} model...")
+
         model_id = MODEL_OPTIONS[model_name]["id"]
-        status_container = st.empty()
-        def update_status(message):
-            if status_container:
-                status_container.info(message)
-            if _progress_callback:
-                _progress_callback(message)
-        try:
-            update_status(f"Starting to load {model_name}...")
-            update_status(f"🔄 Loading tokenizer for {model_name}...")
-            update_status(f"🤖 Loading {model_name} model... (This may take a few minutes)")
-            model = _load_model_cached(model_id)
-            if model is not None:
-                update_status(f"✅ Successfully loaded {model_name}")
-                st.session_state.loaded_models[model_name] = model
-            else:
-                update_status(f"❌ Failed to load {model_name}")
-                return None
-            time.sleep(1)
-        except Exception as e:
-            update_status(f"❌ Error loading {model_name}: {str(e)}")
-            return None
-        finally:
-            time.sleep(1)
-            status_container.empty()
+        st.session_state.loaded_models[model_name] = _load_model_cached(model_id)
+
+        if _progress_callback:
+            _progress_callback(f"✅ {model_name} model loaded successfully!")
+
     return st.session_state.loaded_models[model_name]
 
+
+
 def get_loaded_models():
     models = {}
     progress_bar = st.progress(0)
@@ -943,14 +977,21 @@ def render_ensemble_dashboard():
     st.markdown("### 🧠 Ensemble Method Analytics")
     
     # Analyze ensemble history
-    method_stats = defaultdict(lambda: {'count': 0, 'spam': 0, 'confidences': []})
+    method_stats: Dict[str, MethodStats] = {}
+    for method in ENSEMBLE_METHODS.keys():
+        method_stats[method] = MethodStats(
+            count=0,
+            spam=0,
+            confidences=[]
+        )
     
     for item in st.session_state.ensemble_history:
         method = item['method']
-        method_stats[method]['count'] += 1
-        method_stats[method]['confidences'].append(item['confidence'])
-        if item['prediction'] == 'SPAM':
-            method_stats[method]['spam'] += 1
+        if method in method_stats:
+            method_stats[method]['count'] += 1
+            method_stats[method]['confidences'].append(item['confidence'])
+            if item['prediction'] == 'SPAM':
+                method_stats[method]['spam'] += 1
     
     if method_stats:
         col1, col2 = st.columns(2)
@@ -1252,7 +1293,7 @@ def render_realtime_monitor():
                 'total_messages': len(st.session_state.classification_history) + len(st.session_state.ensemble_history)
             }
             
-            json_data = st.json.dumps(dashboard_data, indent=2, default=str)
+            json_data = json.dumps(dashboard_data, indent=2, default=str)
             
             st.download_button(
                 label="📥 Download Dashboard Data (JSON)",
@@ -1323,13 +1364,40 @@ def get_ensemble_predictions(message, models):
 
     # Set initial value of text_area based on sample_selector or previous user input
     user_sms_initial_value = selected_message if selected_message else st.session_state.get('user_sms_input_value', "")
-    user_sms = st.text_area(
-        "Enter SMS message to analyse",
-        value=user_sms_initial_value,
-        height=120,
-        placeholder="Type or paste your SMS message here...",
-        help="Enter the SMS message you want to classify as spam or ham (legitimate)"
-    )
+    col_text, col_record = st.columns([4, 1])
+
+    with col_text:
+        user_sms = st.text_area(
+            "Enter SMS message to analyse",
+            value=user_sms_initial_value,
+            height=120,
+            placeholder="Type or paste your SMS message here...",
+            help="Enter the SMS message you want to classify as spam or ham (legitimate)",
+            key="sms_input"
+        )
+    
+    with col_record:
+        if st.button("🎤 Record Voice", type="primary", key="record_btn"):
+            with st.spinner("🎤 Listening... Speak clearly"):
+                try:
+                    success, result = st.session_state.speech_handler.listen_and_transcribe()
+                    if success:
+                        # Get existing text and append new text
+                        current_text = user_sms  # Use the widget value directly
+                        if current_text and current_text.strip():
+                            new_text = f"{current_text.strip()} {result}"
+                        else:
+                            new_text = result
+                        # Update session state for persistence
+                        st.session_state.user_sms_input_value = new_text
+                        st.success("✅ Speech recognized!")
+                        st.rerun()
+                    else:
+                        st.error(f"❌ {result}")
+                except Exception as e:
+                    st.error(f"Error: {str(e)}")
+                    time.sleep(2)
+
     # Store current text_area value in session state for persistence
     st.session_state.user_sms_input_value = user_sms
     
@@ -1364,6 +1432,10 @@ def get_ensemble_predictions(message, models):
                 result = classifier(cleaned_sms)[0]
                 label = result['label'].upper()
                 confidence = result['score']
+                # Safely update model statistics with proper error handling
+                if selected_model_name not in st.session_state.model_stats:
+                    st.session_state.model_stats[selected_model_name] = {'spam': 0, 'ham': 0, 'total': 0}
+
                 st.session_state.model_stats[selected_model_name][label.lower()] += 1
                 st.session_state.model_stats[selected_model_name]['total'] += 1
                 st.session_state.classification_history.append({
@@ -1515,7 +1587,7 @@ def get_ensemble_predictions(message, models):
         st.markdown("#### 📊 Single Model Performance")
         
         # Check if there's any data for any model
-        if any(st.session_state.model_stats[model]['total'] > 0 for model in MODEL_OPTIONS):
+        if any(st.session_state.model_stats.get(model, {}).get('total', 0) > 0 for model in MODEL_OPTIONS.keys()):
             # Pie Chart for Spam/Ham Distribution of the SELECTED model
             current_model_stats = st.session_state.model_stats[selected_model_name]
             if current_model_stats['total'] > 0:
@@ -1975,4 +2047,3 @@ def classify_csv(file, ensemble_mode, selected_models_for_bulk, selected_ensembl
     </div>
 </div>
 """, unsafe_allow_html=True)
-
diff --git a/requirements.txt b/requirements.txt
index 1e05383..827b476 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,6 +23,21 @@ python-dotenv>=1.0.0,<2.0.0
 pytest>=7.4.0,<8.0.0
 pytest-cov>=4.1.0,<5.0.0
 
-#UI
-streamlit
-fpdf
\ No newline at end of file
+# UI Components
+streamlit>=1.30.0
+fpdf2>=2.7.0
+
+# Speech Recognition
+SpeechRecognition>=3.10.0
+PyAudio>=0.2.13
+pocketsphinx>=5.0.0
+
+# Type Checking
+mypy>=1.5.0
+types-requests>=2.31.0
+types-setuptools>=68.0.0
+types-protobuf>=4.24.0
+typing-extensions>=4.7.0
+pandas-stubs>=2.0.0
+types-tqdm>=4.65.0
+types-PyYAML>=6.0.0
\ No newline at end of file
diff --git a/speech_handler.py b/speech_handler.py
new file mode 100644
index 0000000..19ff284
--- /dev/null
+++ b/speech_handler.py
@@ -0,0 +1,151 @@
+"""
+Speech Handler Module
+
+A friendly and robust speech recognition handler that converts speech to text.
+Handles microphone input, noise adjustment, and multiple recognition engines.
+"""
+
+import speech_recognition as sr
+from typing import Tuple, Any
+
+
+class SpeechHandler:
+    """
+    A user-friendly speech recognition handler.
+
+    This class provides an easy-to-use interface for converting speech to text
+    with automatic error handling and fallback mechanisms.
+    """
+
+    def __init__(self):
+        """Initialize the speech handler with optimized settings."""
+        self.recognizer: Any = sr.Recognizer()
+        self._setup_recognizer()
+
+    def _setup_recognizer(self) -> None:
+        """Configure the recognizer with optimal settings for better accuracy."""
+        # Adjust energy threshold for better speech detection
+        if hasattr(self.recognizer, "energy_threshold"):
+            self.recognizer.energy_threshold = 300
+
+        # Enable dynamic energy threshold for adaptive listening
+        if hasattr(self.recognizer, "dynamic_energy_threshold"):
+            self.recognizer.dynamic_energy_threshold = True
+
+        # Set pause threshold for natural speech breaks
+        if hasattr(self.recognizer, "pause_threshold"):
+            self.recognizer.pause_threshold = 0.8
+
+    def _validate_audio(self, audio) -> bool:
+        """Validate that the captured audio is suitable for processing."""
+        if not audio or not hasattr(audio, 'sample_rate'):
+            return False
+
+        # Check if audio is too short (less than 0.5 seconds)
+        if len(audio.frame_data) / audio.sample_rate < 0.5:
+            return False
+
+        return True
+
+    def listen_and_transcribe(self, timeout: int = 5, max_duration: int = 10) -> Tuple[bool, str]:
+        """
+        Listen for speech and convert it to text.
+
+        Args:
+            timeout: Maximum time to wait for speech (seconds)
+            max_duration: Maximum duration of speech to record (seconds)
+
+        Returns:
+            Tuple of (success: bool, result: str)
+        """
+        try:
+            # Create a new microphone instance for fresh capture
+            microphone = sr.Microphone()
+
+            with microphone as source:
+                print("🎤 Adjusting for ambient noise... Please wait.")
+                # Adjust for ambient noise to improve recognition
+                self.recognizer.adjust_for_ambient_noise(source, duration=1)
+
+                try:
+                    print("🎤 Listening... Please speak clearly.")
+                    audio = self.recognizer.listen(
+                        source,
+                        timeout=timeout,
+                        phrase_time_limit=max_duration
+                    )
+
+                    # Validate the captured audio
+                    if not self._validate_audio(audio):
+                        return False, "❌ Audio too short or invalid. Please try again."
+
+                except sr.WaitTimeoutError:
+                    return False, "⏰ No speech detected. Please try speaking again."
+                except Exception as e:
+                    return False, f"❌ Error recording audio: {str(e)}"
+
+            # Convert speech to text with fallback mechanisms
+            return self._transcribe_audio(audio)
+
+        except Exception as e:
+            return False, f"💥 Critical error: {str(e)}"
+
+    def _transcribe_audio(self, audio) -> Tuple[bool, str]:
+        """Transcribe the captured audio to text using multiple engines."""
+        try:
+            # Primary: Try Google's speech recognition (most accurate)
+            try:
+                print("🔄 Processing with Google Speech Recognition...")
+                result = self.recognizer.recognize_google(audio)
+                if result and result.strip():
+                    print(f"✅ Recognized: '{result}'")
+                    return True, result
+            except (sr.UnknownValueError, sr.RequestError) as e:
+                print(f"⚠️ Google recognition failed: {str(e)}")
+
+            # Fallback: Try local Sphinx recognizer if available
+            try:
+                print("🔄 Trying offline recognition...")
+                result = self.recognizer.recognize_sphinx(audio)
+                if result and result.strip():
+                    print(f"✅ Offline recognition: '{result}'")
+                    return True, result
+            except ImportError:
+                print("ℹ️ Offline recognizer not available")
+            except Exception as e:
+                print(f"⚠️ Offline recognition failed: {str(e)}")
+
+            return False, "❓ Could not understand the audio. Please speak more clearly."
+
+        except Exception as e:
+            return False, f"❌ Speech recognition error: {str(e)}"
+
+    def cleanup(self) -> None:
+        """Clean up resources when done."""
+        try:
+            if hasattr(self, 'recognizer'):
+                del self.recognizer
+                print("🧹 Speech handler cleaned up successfully")
+        except Exception as e:
+            print(f"⚠️ Cleanup warning: {str(e)}")
+
+
+# Example usage
+if __name__ == "__main__":
+    print("🎤 Speech Handler Demo")
+    print("=" * 50)
+
+    handler = SpeechHandler()
+
+    try:
+        success, text = handler.listen_and_transcribe(timeout=5, max_duration=10)
+
+        if success:
+            print(f"\n🎉 Success! Transcribed text: '{text}'")
+        else:
+            print(f"\n❌ Failed: {text}")
+
+    except KeyboardInterrupt:
+        print("\n⏹️ Stopped by user")
+    finally:
+        handler.cleanup()
diff --git a/speech_to_text.py b/speech_to_text.py
new file mode 100644
index 0000000..5b9399a
--- /dev/null
+++ b/speech_to_text.py
@@ -0,0 +1,313 @@
+"""
+🎤 Speech to Text Module
+
+A friendly and robust speech recognition system with real-time processing capabilities.
+Supports both continuous listening and one-time transcription with callback support.
+
+Features:
+- 🎯 Real-time continuous listening
+- 🔄 One-time transcription
+- 🛡️ Comprehensive error handling
+- 🎨 User-friendly interface
+- ⚡ Multi-threaded processing
+"""
+
+import speech_recognition as sr
+import threading
+import queue
+import time
+from typing import Optional, Callable, Dict, Any
+
+
+class SpeechToText:
+    """
+    🎤 Friendly Speech-to-Text Converter
+
+    This class provides an easy-to-use interface for converting speech to text
+    with both real-time continuous listening and one-time transcription capabilities.
+
+    Features:
+    - Real-time speech recognition with callbacks
+    - Single utterance transcription
+    - Robust error handling with user-friendly messages
+    - Multi-threaded processing for smooth performance
+    - Automatic microphone calibration
+    """
+
+    def __init__(self):
+        """Initialize the speech-to-text system with optimal settings."""
+        print("🚀 Initializing Speech-to-Text system...")
+
+        self.recognizer = sr.Recognizer()
+        self.microphone = sr.Microphone()
+        self.is_listening = False
+        self.audio_queue = queue.Queue()
+        self.error_callback: Optional[Callable] = None
+        self.text_callback: Optional[Callable] = None
+        self.processing_thread: Optional[threading.Thread] = None
+
+        # Configure optimal settings
+        self._setup_recognizer()
+        print("✅ Speech-to-Text system ready!")
+
+    def _setup_recognizer(self) -> None:
+        """Configure the recognizer with optimal settings for better accuracy."""
+        try:
+            print("🎤 Calibrating microphone for ambient noise...")
+            with self.microphone as source:
+                self.recognizer.adjust_for_ambient_noise(source, duration=1)
+                print("✅ Microphone calibrated successfully!")
+
+                # Set optimal recognition parameters
+                self.recognizer.energy_threshold = 300
+                self.recognizer.dynamic_energy_threshold = True
+                self.recognizer.pause_threshold = 0.8
+
+        except OSError as e:
+            print(f"⚠️ Microphone access error: {str(e)}")
+            print("💡 Please check your microphone permissions and try again.")
+        except Exception as e:
+            print(f"⚠️ Microphone setup warning: {str(e)}")
+            print("🔧 Using default microphone settings.")
+
+    def set_callbacks(self, text_callback: Callable, error_callback: Callable) -> None:
+        """
+        Set callback functions for handling transcription results and errors.
+
+        Args:
+            text_callback: Function to call when text is recognized
+            error_callback: Function to call when an error occurs
+        """
+        self.text_callback = text_callback
+        self.error_callback = error_callback
+        print("📞 Callbacks configured successfully!")
+
+    def start_listening(self) -> None:
+        """Start continuous listening in a background thread."""
+        if not self.is_listening:
+            self.is_listening = True
+            print("🎤 Starting continuous listening...")
+            threading.Thread(target=self._listen_loop, daemon=True).start()
+        else:
+            print("ℹ️ Already listening!")
+
+    def stop_listening(self) -> None:
+        """Stop the continuous listening loop."""
+        if self.is_listening:
+            self.is_listening = False
+            print("⏹️ Stopped listening.")
+        else:
+            print("ℹ️ Not currently listening.")
+
+    def _listen_loop(self) -> None:
+        """Main listening loop that runs in a separate thread."""
+        while self.is_listening:
+            try:
+                with self.microphone as source:
+                    audio = self.recognizer.listen(
+                        source,
+                        timeout=5,
+                        phrase_time_limit=10
+                    )
+                    self.audio_queue.put(audio)
+
+                    # Process audio in a separate thread to avoid blocking
+                    threading.Thread(
+                        target=self._process_audio,
+                        args=(audio,),
+                        daemon=True
+                    ).start()
+
+            except sr.WaitTimeoutError:
+                continue  # No speech detected, continue listening
+            except Exception as e:
+                if self.error_callback:
+                    self.error_callback(f"❌ Error capturing audio: {str(e)}")
+                time.sleep(1)  # Prevent tight loop on error
+
+    def _process_audio(self, audio) -> None:
+        """Process captured audio and convert to text with fallback options."""
+        try:
+            print("🔄 Processing audio...")
+            # Try Google Speech Recognition first (most accurate)
+            text = self.recognizer.recognize_google(audio)  # type: ignore
+
+            if text and text.strip():
+                print(f"✅ Recognized: '{text}'")
+                if self.text_callback:
+                    self.text_callback(text)
+            else:
+                print("⚠️ Empty recognition result")
+
+        except sr.UnknownValueError:
+            error_msg = "❓ Could not understand the audio. Please speak more clearly."
+            print(error_msg)
+            if self.error_callback:
+                self.error_callback(error_msg)
+
+        except sr.RequestError as e:
+            error_msg = f"❌ Could not connect to Google Speech Recognition: {str(e)}"
+            print(error_msg)
+            print("💡 Trying offline recognition...")
+
+            # Fallback to offline recognition if available
+            try:
+                text = self.recognizer.recognize_sphinx(audio)  # type: ignore
+                if text and text.strip():
+                    print(f"✅ Offline recognition: '{text}'")
+                    if self.text_callback:
+                        self.text_callback(text)
+                else:
+                    error_msg = "❌ Offline recognition also failed"
+                    print(error_msg)
+                    if self.error_callback:
+                        self.error_callback(error_msg)
+            except ImportError:
+                error_msg = "❌ Offline recognizer not available. Please install PocketSphinx."
+                print(error_msg)
+                if self.error_callback:
+                    self.error_callback(error_msg)
+            except Exception as offline_error:
+                error_msg = f"❌ Offline recognition failed: {str(offline_error)}"
+                print(error_msg)
+                if self.error_callback:
+                    self.error_callback(error_msg)
+
+        except Exception as e:
+            error_msg = f"💥 Unexpected error processing audio: {str(e)}"
+            print(error_msg)
+            if self.error_callback:
+                self.error_callback(error_msg)
+
+    def transcribe_once(self, timeout: int = 5, max_duration: int = 10) -> Optional[str]:
+        """
+        Capture and transcribe a single utterance with user-friendly feedback.
+
+        Args:
+            timeout: Maximum time to wait for speech (seconds)
+            max_duration: Maximum duration of speech to record (seconds)
+
+        Returns:
+            Transcribed text or None if failed
+        """
+        try:
+            print("🎤 Listening for single utterance... Speak now!")
+            print("(Press Ctrl+C to cancel)")
+
+            with self.microphone as source:
+                audio = self.recognizer.listen(
+                    source,
+                    timeout=timeout,
+                    phrase_time_limit=max_duration
+                )
+
+            print("🔄 Processing your speech...")
+            # Try Google Speech Recognition first
+            text = self.recognizer.recognize_google(audio)  # type: ignore
+
+            if text and text.strip():
+                print(f"🎉 Success! Transcribed: '{text}'")
+                return text
+            else:
+                print("⚠️ No speech content detected")
+                return None
+
+        except sr.UnknownValueError:
+            print("❓ Could not understand the audio. Please speak more clearly.")
+        except sr.RequestError as e:
+            print(f"❌ Could not connect to Google Speech Recognition: {str(e)}")
+            print("💡 Trying offline recognition...")
+
+            # Fallback to offline recognition
+            try:
+                text = self.recognizer.recognize_sphinx(audio)  # type: ignore
+                if text and text.strip():
+                    print(f"🎉 Offline recognition successful: '{text}'")
+                    return text
+                else:
+                    print("❌ Offline recognition also failed")
+            except ImportError:
+                print("❌ Offline recognizer not available. Please install PocketSphinx for offline support.")
+            except Exception as offline_error:
+                print(f"❌ Offline recognition failed: {str(offline_error)}")
+        except sr.WaitTimeoutError:
+            print("⏰ No speech detected within timeout period. Please try again.")
+        except KeyboardInterrupt:
+            print("\n⏹️ Cancelled by user")
+        except Exception as e:
+            print(f"💥 Unexpected error: {str(e)}")
+
+        return None
+
+    def get_status(self) -> dict:
+        """Get the current status of the speech recognition system."""
+        return {
+            "is_listening": self.is_listening,
+            "queue_size": self.audio_queue.qsize(),
+            "has_text_callback": self.text_callback is not None,
+            "has_error_callback": self.error_callback is not None
+        }
+
+
+# Example usage and demo
+def demo_text_callback(text: str) -> None:
+    """Demo callback for successful text recognition."""
+    print(f"📝 Text callback: {text}")
+
+
+def demo_error_callback(error: str) -> None:
+    """Demo callback for errors."""
+    print(f"🚨 Error callback: {error}")
+
+
+if __name__ == "__main__":
+    print("🎤 Speech to Text Demo")
+    print("=" * 50)
+
+    # Create instance
+    stt = SpeechToText()
+
+    print("\nChoose an option:")
+    print("1. Single transcription")
+    print("2. Continuous listening demo")
+    print("3. Status check")
+
+    try:
+        choice = input("\nEnter your choice (1-3): ").strip()
+
+        if choice == "1":
+            print("\n--- Single Transcription Mode ---")
+            result = stt.transcribe_once()
+            if result:
+                print(f"\nFinal result: {result}")
+            else:
+                print("\nNo result obtained.")
+
+        elif choice == "2":
+            print("\n--- Continuous Listening Mode ---")
+            stt.set_callbacks(demo_text_callback, demo_error_callback)
+            stt.start_listening()
+
+            print("Listening... Say something! (Press Ctrl+C to stop)")
+            try:
+                while True:
+                    time.sleep(1)
+            except KeyboardInterrupt:
+                stt.stop_listening()
+                print("\nStopped listening.")
+
+        elif choice == "3":
+            print("\n--- Status Check ---")
+            status = stt.get_status()
+            for key, value in status.items():
+                print(f"{key}: {value}")
+
+        else:
+            print("❌ Invalid choice. Please run again.")
+
+    except KeyboardInterrupt:
+        print("\n⏹️ Demo interrupted by user")
+    except Exception as e:
+        print(f"💥 Demo error: {str(e)}")
+
+    print("\n👋 Demo completed!")
diff --git a/types.py b/types.py
new file mode 100644
index 0000000..4ab188b
--- /dev/null
+++ b/types.py
@@ -0,0 +1,53 @@
+"""Type definitions for the Spamlyser application."""
+from typing import TypeVar, Dict, Any, List, Union, Optional
+from typing_extensions import TypedDict, Protocol
+
+# Type for model predictions
+class PredictionDict(TypedDict):
+    label: str
+    score: float
+    spam_probability: Optional[float]
+
+# Type for model statistics
+class ModelStats(TypedDict):
+    spam: int
+    ham: int
+    total: int
+
+# Type for method statistics
+class MethodStats(TypedDict):
+    count: int
+    spam: int
+    confidences: List[float]
+
+# Type for ensemble predictions
+class EnsemblePrediction(TypedDict):
+    label: str
+    confidence: float
+    method: str
+    spam_probability: float
+    details: str
+
+# Type for model options
+class ModelOption(TypedDict):
+    id: str
+    description: str
+    icon: str
+    color: str
+
+# Protocol for models
+class Model(Protocol):
+    def __call__(self, text: Union[str, List[str]]) -> List[PredictionDict]: ...
+
+# Type variables
+T = TypeVar('T')
+ModelName = str
+ModelDict = Dict[ModelName, Model]
+OptionsDict = Dict[ModelName, ModelOption]
+StatsDict = Dict[ModelName, ModelStats]
+MethodStatsDict = Dict[str, MethodStats]
+
+# Type aliases
+JsonDict = Dict[str, Any]
+Confidence = float
+Label = str