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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,607 @@
+import streamlit as st
+import numpy as np
+import cv2
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import pandas as pd
+from io import BytesIO
+import base64
+import matplotlib.pyplot as plt
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from concurrent.futures import ThreadPoolExecutor
+import urllib.parse
+import json
+import random
+import os
+
+# Set page configuration
+st.set_page_config(page_title="Indian Sign Language Classifier", page_icon="🤟", layout="wide")
+
+# Define paths
+MODEL_PATH = "C:/Users/Cherukuri Gowtham/OneDrive/project/model.keras"  # Update with your actual model path
+DATASET_PATH = "C:\\Users\\Cherukuri Gowtham\\OneDrive\\project\\isl dataset\\Indian"  # Verify this path exists
+
+# Load the trained model
+try:
+    model = load_model(MODEL_PATH)
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Define class labels
+class_labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
+
+# Translations for prediction output and learning mode
+translations = {
+    'en': {
+        'prediction_text': "The predicted sign is 🤟: {sign}",
+        'confidence_text': "Confidence: {confidence:.2%}",
+        'description_text': "Description: Sign {sign} represents the {type} {sign} in Indian Sign Language.",
+        'top_3_text': "Top 3 Suggestions:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "Practice Sign: {sign}",
+        'learning_description': "Sign {sign} is the {type} {sign} in Indian Sign Language.",
+    },
+    'hi': {
+        'prediction_text': "अनुमानित संकेत है 🤟: {sign}",
+        'confidence_text': "आत्मविश्वास: {confidence:.2%}",
+        'description_text': "विवरण: संकेत {sign} भारतीय सांकेतिक भाषा में {type} {sign} को दर्शाता है।",
+        'top_3_text': "शीर्ष 3 सुझाव:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "अभ्यास संकेत: {sign}",
+        'learning_description': "संकेत {sign} भारतीय सांकेतिक भाषा में {type} {sign} है।",
+    },
+    'ta': {
+        'prediction_text': "கணிக்கப்பட்ட குறியீடு 🤟: {sign}",
+        'confidence_text': "நம்பிக்கை: {confidence:.2%}",
+        'description_text': "விளக்கம்: குறியீடு {sign} இந்திய சைகை மொழியில் {type} {sign} ஐ பிரதிநிதித்துவப்படுத்துகிறது。",
+        'top_3_text': "முதல் 3 பரிந்துரைகள்:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "பயிற்சி குறியீடு: {sign}",
+        'learning_description': "குறியீடு {sign} இந்திய சைகை மொழியில் {type} {sign} ஆகும்。",
+    },
+    'te': {
+        'prediction_text': "అంచనా వేసిన సంజ్ఞ 🤟: {sign}",
+        'confidence_text': "విశ్వాసం: {confidence:.2%}",
+        'description_text': "వివరణ: సంజ్ఞ {sign} భారతీయ సంజ్ఞా భాషలో {type} {sign} ని సూచిస్తుంది。",
+        'top_3_text': "టాప్ 3 సూచనలు:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "అభ్యాస సంజ్ఞ: {sign}",
+        'learning_description': "సంజ్ఞ {sign} భారతీయ సంజ్ఞా భాషలో {type} {sign} గా ఉంది।",
+    },
+    'bn': {
+        'prediction_text': "পূর্বাভাসিত সংকেত 🤟: {sign}",
+        'confidence_text': "আত্মবিশ্বাস: {confidence:.2%}",
+        'description_text': "বর্ণনা: সংকেত {sign} ভারতীয় সংকেত ভাষায় {type} {sign} প্রতিনিধিত্ব করে।",
+        'top_3_text': "শীর্ষ 3 পরামর্শ:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "অভ্যাস সংকেত: {sign}",
+        'learning_description': "সংকেত {sign} ভারতীয় সংকেত ভাষায় {type} {sign} হিসেবে প্রতিনিধিত্ব করে।",
+    },
+    'mr': {
+        'prediction_text': "अंदाजित संकेत आहे 🤟: {sign}",
+        'confidence_text': "आत्मविश्वास: {confidence:.2%}",
+        'description_text': "वर्णन: स���केत {sign} भारतीय संकेत भाषेत {type} {sign} दर्शवितो।",
+        'top_3_text': "शीर्ष 3 सूचना:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "सराव संकेत: {sign}",
+        'learning_description': "संकेत {sign} भारतीय संकेत भाषेत {type} {sign} आहे।",
+    },
+    'gu': {
+        'prediction_text': "આગાહી કરેલ સંકેત છે 🤟: {sign}",
+        'confidence_text': "આત્મવિશ્વાસ: {confidence:.2%}",
+        'description_text': "વર્ણન: સંકેત {sign} ભારતીય સંકેત ભાષામાં {type} {sign} નું પ્રતિનિધિત્વ કરે છે।",
+        'top_3_text': "ટોચના 3 સૂચનો:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "અભ્યાસ સંકેત: {sign}",
+        'learning_description': "સંકેત {sign} ભારતીય સંકેત ભાષામાં {type} {sign} છે।",
+    },
+    'kn': {
+        'prediction_text': "ಊಹಿಸಲಾದ ಸಂಕೇತ 🤟: {sign}",
+        'confidence_text': "ವಿಶ್ವಾಸ: {confidence:.2%}",
+        'description_text': "ವಿವರಣೆ: ಸಂಕೇತ {sign} ಭಾರತೀಯ ಸಂಕೇತ ಭಾಷೆಯಲ್ಲಿ {type} {sign} ಗೆ ಪ್ರತಿನಿಧಿಯಾಗಿರುತ್ತದೆ।",
+        'top_3_text': "ಟಾಪ್ 3 ಸಲಹೆಗಳು:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "ಅಭ್ಯಾಸ ಸಂಕೇತ: {sign}",
+        'learning_description': "ಸಂಕೇತ {sign} ಭಾರತೀಯ ಸಂಕೇತ ಭಾಷೆಯಲ್ಲಿ {type} {sign} ಆಗಿದೆ।",
+    },
+    'ml': {
+        'prediction_text': "പ്രവചിച്ച ആംഗ്യം 🤟: {sign}",
+        'confidence_text': "ആത്മവിശ്വാസം: {confidence:.2%}",
+        'description_text': "വിവരണം: ആംഗ്യം {sign} ഇന്ത്യൻ ആംഗ്യഭാഷയിൽ {type} {sign} നെ പ്രതിനിധീകരിക്കുന്നു।",
+        'top_3_text': "മികച്ച 3 നിർദ്ദേശങ്ങൾ:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "പരിശീലന ആംഗ്യം: {sign}",
+        'learning_description': "ആംഗ്യം {sign} ഇന്ത്യൻ ആംഗ്യഭাষയിൽ {type} {sign} ആണ്。",
+    },
+    'pa': {
+        'prediction_text': "ਅੰਦਾਜ਼ਾ ਲਗਾਇਆ ਸੰਕੇਤ 🤟: {sign}",
+        'confidence_text': "ਵਿਸ਼ਵਾਸ: {confidence:.2%}",
+        'description_text': "ਵੇਰਵਾ: ਸੰਕੇਤ {sign} ਭਾਰਤੀ ਸੰਕੇਤ ਭਾਸ਼ਾ ਵਿੱਚ {type} {sign} ਨੂੰ ਦਰਸਾਉਂਦਾ ਹੈ।",
+        'top_3_text': "ਸਿਖਰ ਦੇ 3 ਸੁਝਾਅ:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "ਅਭਿਆਸ ਸੰਕੇਤ: {sign}",
+        'learning_description': "ਸੰਕੇਤ {sign} ਭਾਰਤੀ ਸੰਕੇਤ ਭਾਸ਼ਾ ਵਿੱਚ {type} {sign} ਹੈ।",
+    },
+    'or': {
+        'prediction_text': "ପୂର୍ବାନୁମାନିତ ଚିହ୍ନ 🤟: {sign}",
+        'confidence_text': "ଆତ୍ମବିଶ୍ୱାସ: {confidence:.2%}",
+        'description_text': "ବିବରଣୀ: ଚିହ୍ନ {sign} ଭାରତୀୟ ସଙ୍କେତ ଭାଷାରେ {type} {sign} କୁ ପ୍ରତିନିଧିତ୍ୱ କରେ।",
+        'top_3_text': "ଶୀର୍ଷ 3 ପରାମର୍ଶ:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "ଅଭ୍ୟାସ ଚିହ୍ନ: {sign}",
+        'learning_description': "ଚିହ୍ନ {sign} ଭାରତୀୟ ସଙ୍କେତ ଭାଷାରେ {type} {sign} ଅଟେ।",
+    },
+    'as': {
+        'prediction_text': "পূৰ্বাভাস কৰা সংকেত 🤟: {sign}",
+        'confidence_text': "আত্মবিশ্বাস: {confidence:.2%}",
+        'description_text': "বিৱৰণ: সংকেত {sign} ভাৰতীয় সংকেত ভাষাত {type} {sign} ক প্ৰতিনিধিত্ব কৰে।",
+        'top_3_text': "শীৰ্ষ ৩ পৰামৰ্শ:",
+        'top_3_item': "- {sign}: {confidence:.2%}",
+        'learning_text': "অভ্যাস সংকেত: {sign}",
+        'learning_description': "সংকেত {sign} ভাৰতীয় সংকেত ভাষাত {type} {sign} হয়।",
+    }
+}
+
+# Preprocess image with caching
+@st.cache_data
+def preprocess_image(image, target_size=(64, 62)):
+    image_resized = cv2.resize(image, target_size)
+    image_preprocessed = tf.keras.preprocessing.image.img_to_array(image_resized) / 255.0
+    return image_resized, image_preprocessed
+
+# Load sign image with caching
+@st.cache_data
+def load_sign_image(sign):
+    # Debugging: Show attempted paths
+    st.write(f"Attempting to load image for sign '{sign}' from {DATASET_PATH}")
+    
+    # Try subfolder structure (e.g., DATASET_PATH/A/image.png)
+    subfolder_path = os.path.join(DATASET_PATH, sign)
+    if os.path.isdir(subfolder_path):
+        st.write(f"Found subfolder: {subfolder_path}")
+        for ext in ['png', 'jpg', 'jpeg']:
+            images = [f for f in os.listdir(subfolder_path) if f.lower().endswith(f'.{ext}')]
+            if images:
+                image_path = os.path.join(subfolder_path, images[0])
+                st.write(f"Selected image: {image_path}")
+                return image_path
+        st.write(f"No images found in {subfolder_path}")
+    
+    # Try alternative subfolder names (e.g., letter_A, 0 for numbers)
+    alt_subfolder = f"letter_{sign}" if sign.isalpha() else str(int(sign) - 1) if sign.isdigit() else sign
+    alt_subfolder_path = os.path.join(DATASET_PATH, alt_subfolder)
+    if os.path.isdir(alt_subfolder_path):
+        st.write(f"Found alternative subfolder: {alt_subfolder_path}")
+        for ext in ['png', 'jpg', 'jpeg']:
+            images = [f for f in os.listdir(alt_subfolder_path) if f.lower().endswith(f'.{ext}')]
+            if images:
+                image_path = os.path.join(alt_subfolder_path, images[0])
+                st.write(f"Selected image: {image_path}")
+                return image_path
+        st.write(f"No images found in {alt_subfolder_path}")
+    
+    # Try single image (e.g., DATASET_PATH/A.png)
+    for ext in ['png', 'jpg', 'jpeg']:
+        image_path = os.path.join(DATASET_PATH, f"{sign}.{ext}")
+        if os.path.exists(image_path):
+            st.write(f"Found single image: {image_path}")
+            return image_path
+    
+    # Try case-insensitive subfolder
+    for folder in os.listdir(DATASET_PATH):
+        if folder.lower() == sign.lower() and os.path.isdir(os.path.join(DATASET_PATH, folder)):
+            subfolder_path = os.path.join(DATASET_PATH, folder)
+            st.write(f"Found case-insensitive subfolder: {subfolder_path}")
+            for ext in ['png', 'jpg', 'jpeg']:
+                images = [f for f in os.listdir(subfolder_path) if f.lower().endswith(f'.{ext}')]
+                if images:
+                    image_path = os.path.join(subfolder_path, images[0])
+                    st.write(f"Selected image: {image_path}")
+                    return image_path
+    
+    st.write(f"No image found for sign '{sign}'")
+    return None
+
+# Convert image to base64
+def image_to_base64(image):
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    _, buffer = cv2.imencode('.png', image_bgr)
+    return base64.b64encode(buffer).decode('utf-8')
+
+# Generate PDF report (English for simplicity)
+def generate_pdf_report(df):
+    buffer = BytesIO()
+    c = canvas.Canvas(buffer, pagesize=letter)
+    c.setFont("Helvetica", 12)
+    c.drawString(100, 750, "Indian Sign Language Prediction Report")
+    y = 700
+    for _, row in df.iterrows():
+        c.drawString(100, y, f"Image: {row['Image']}")
+        c.drawString(100, y-20, f"Predicted Sign: {row['Predicted Sign']}")
+        c.drawString(100, y-40, f"Confidence: {row['Confidence']:.2%}")
+        y -= 60
+    c.save()
+    buffer.seek(0)
+    return buffer
+
+# Visualization function
+def generate_visualization(df):
+    if df.empty:
+        st.warning("No predictions to visualize. Please upload images first.")
+        return
+    chart_type = st.selectbox("Select Chart Type", ["Bar Chart", "Pie Chart", "Confidence Trend"], key="chart_type")
+    if chart_type == "Bar Chart":
+        sign_counts = df["Predicted Sign"].value_counts()
+        fig, ax = plt.subplots()
+        ax.bar(sign_counts.index, sign_counts.values)
+        ax.set_title("Prediction Distribution (Bar Chart)")
+        ax.set_xlabel("Signs")
+        ax.set_ylabel("Count")
+        plt.xticks(rotation=45)
+        st.pyplot(fig)
+    elif chart_type == "Pie Chart":
+        sign_counts = df["Predicted Sign"].value_counts()
+        fig, ax = plt.subplots()
+        ax.pie(sign_counts.values, labels=sign_counts.index, autopct='%1.1f%%', startangle=90)
+        ax.axis('equal')
+        ax.set_title("Prediction Distribution (Pie Chart)")
+        st.pyplot(fig)
+    else:  # Confidence Trend
+        selected_sign = st.selectbox("Select Sign for Confidence Trend", options=class_labels, key="trend_sign")
+        trend_df = df[df["Predicted Sign"] == selected_sign][["Confidence"]].reset_index(drop=True)
+        if trend_df.empty:
+            st.warning(f"No predictions for sign {selected_sign}.")
+            return
+        fig, ax = plt.subplots()
+        ax.plot(trend_df.index, trend_df["Confidence"], marker='o')
+        ax.set_title(f"Confidence Trend for Sign {selected_sign}")
+        ax.set_xlabel("Prediction Instance")
+        ax.set_ylabel("Confidence")
+        ax.grid(True)
+        st.pyplot(fig)
+
+# CSS for accessible, visual-first styling
+st.markdown("""
+<style>
+@keyframes slideIn {
+    0% { transform: translateX(-100%); opacity: 0; }
+    100% { transform: translateX(0); opacity: 1; }
+}
+@keyframes flash {
+    0% { border-color: #ff6b6b; }
+    50% { border-color: #4ecdc4; }
+    100% { border-color: #ff6b6b; }
+}
+.stApp {
+    transition: all 0.3s ease;
+    font-size: 18px;
+}
+.prediction-card {
+    animation: slideIn 0.5s ease-out;
+    background: linear-gradient(45deg, #ff6b6b, #4ecdc4, #45e994);
+    color: white;
+    padding: 15px;
+    border-radius: 10px;
+    margin-bottom: 10px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+    border: 3px solid transparent;
+}
+.prediction-card.flash {
+    animation: flash 0.5s;
+}
+.stButton>button {
+    background: linear-gradient(45deg, #ff6b6b, #4ecdc4);
+    color: white;
+    border: none;
+    border-radius: 25px;
+    padding: 15px 30px;
+    font-size: 16px;
+    transition: transform 0.2s;
+}
+.stButton>button:hover {
+    transform: scale(1.05);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+}
+.loader {
+    border: 4px solid #f3f3f3;
+    border-top: 4px solid #ff6b6b;
+    border-radius: 50%;
+    width: 40px;
+    height: 40px;
+    animation: spin 1s linear infinite;
+    margin: auto;
+}
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+.header {
+    background: linear-gradient(to right, #ff7e5f, #feb47b);
+    padding: 20px;
+    border-radius: 10px;
+    text-align: center;
+    color: white;
+    margin-bottom: 20px;
+}
+.footer {
+    background: linear-gradient(to right, #6b7280, #4b5563);
+    padding: 10px;
+    border-radius: 10px;
+    text-align: center;
+    color: white;
+    margin-top: 20px;
+}
+.search-bar {
+    padding: 10px;
+    border-radius: 5px;
+    border: 1px solid #ccc;
+    width: 100%;
+    font-size: 16px;
+}
+.learning-card {
+    background: linear-gradient(45deg, #4ecdc4, #45e994);
+    color: white;
+    padding: 20px;
+    border-radius: 10px;
+    text-align: center;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+}
+.learning-image {
+    max-width: 200px;
+    margin: 0 auto;
+    display: block;
+}
+@media (max-width: 600px) {
+    .stSidebar {
+        width: 100%;
+        height: auto;
+    }
+    .stApp {
+        font-size: 16px;
+    }
+    .learning-image {
+        max-width: 150px;
+    }
+}
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown("<div class='header'><h1>Indian Sign Language Classifier</h1></div>", unsafe_allow_html=True)
+
+# Sidebar
+st.sidebar.header("Settings & Instructions")
+st.sidebar.markdown("""
+1. Select the language for prediction output and learning content (English by default).
+2. Use the Image Upload tab to classify signs.
+3. Adjust settings for image processing.
+4. Set confidence threshold for predictions.
+5. Search prediction history by sign, image name, or confidence in the Image Upload tab.
+6. Use the Visualization tab for prediction distribution or confidence trends.
+7. Export results as CSV or PDF in the Image Upload tab.
+8. Practice signs with images from the dataset in the Learning tab.
+9. Provide feedback in the Feedback tab.
+""")
+language_options = {
+    'en': 'English',
+    'hi': 'Hindi',
+    'ta': 'Tamil',
+    'te': 'Telugu',
+    'bn': 'Bengali',
+    'mr': 'Marathi',
+    'gu': 'Gujarati',
+    'kn': 'Kannada',
+    'ml': 'Malayalam',
+    'pa': 'Punjabi',
+    'or': 'Odia',
+    'as': 'Assamese'
+}
+if 'selected_language' not in st.session_state:
+    st.session_state.selected_language = 'en'
+selected_language = st.sidebar.selectbox(
+    "Prediction Language",
+    options=list(language_options.values()),
+    index=list(language_options.keys()).index(st.session_state.selected_language),
+    help="Choose the language for prediction output and learning content"
+)
+st.session_state.selected_language = list(language_options.keys())[list(language_options.values()).index(selected_language)]
+theme = st.sidebar.selectbox("Theme", ["Light", "Dark", "High Contrast"], help="Choose a theme for better visibility")
+if theme == "Dark":
+    st.markdown("<style>.stApp { background-color: #1E1E1E; color: white; }</style>", unsafe_allow_html=True)
+elif theme == "High Contrast":
+    st.markdown("<style>.stApp { background-color: #000; color: #FFF; }</style>", unsafe_allow_html=True)
+target_size = st.sidebar.slider("Target Image Height (px)", 16, 64, 64, step=8, help="Set image height (width fixed at 62)")
+target_size = (target_size, 62)
+confidence_threshold = st.sidebar.slider("Minimum Confidence Threshold", 0.0, 1.0, 0.5, 0.05, help="Filter low-confidence predictions")
+
+# Initialize session state
+if 'predictions_df' not in st.session_state:
+    st.session_state.predictions_df = pd.DataFrame(columns=["Image", "Predicted Sign", "Confidence", "Image Base64"])
+if 'current_sign' not in st.session_state:
+    st.session_state.current_sign = random.choice(class_labels)
+
+# Process single image
+def process_single_image(uploaded_file, target_size, confidence_threshold):
+    try:
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image_resized, image_preprocessed = preprocess_image(image, target_size)
+        prediction = model.predict(np.expand_dims(image_preprocessed, axis=0), verbose=0)
+        predicted_class = class_labels[np.argmax(prediction)]
+        confidence = np.max(prediction)
+        top_3 = np.argsort(prediction[0])[-3:][::-1]
+        top_3_signs = [(class_labels[i], prediction[0][i]) for i in top_3]
+        if confidence >= confidence_threshold:
+            image_base64 = image_to_base64(image_resized)
+            return {
+                "Image": uploaded_file.name,
+                "Predicted Sign": predicted_class,
+                "Confidence": confidence,
+                "Image Base64": image_base64,
+                "Top 3 Signs": top_3_signs
+            }
+        else:
+            return {"error": f"Prediction for {uploaded_file.name} below confidence threshold ({confidence:.2%} < {confidence_threshold:.2%})"}
+    except Exception as e:
+        return {"error": f"Error processing {uploaded_file.name}: {e}"}
+
+# Tabs
+tab1, tab2, tab3, tab4 = st.tabs(["Image Upload", "Visualization", "Feedback", "Learning"])
+
+# Tab 1: Image Upload
+with tab1:
+    st.subheader("Classify Signs")
+    uploaded_files = st.file_uploader("Upload image(s)", type=["jpg", "png", "jpeg"], accept_multiple_files=True, help="Upload images of signs")
+    
+    if st.button("Reset History", help="Clear all predictions"):
+        st.session_state.predictions_df = pd.DataFrame(columns=["Image", "Predicted Sign", "Confidence", "Image Base64"])
+        st.success("Prediction history reset.")
+        st.rerun()
+    
+    if uploaded_files:
+        st.subheader("Prediction Results")
+        progress_bar = st.progress(0)
+        total_files = len(uploaded_files)
+        with ThreadPoolExecutor() as executor:
+            results = list(executor.map(lambda f: process_single_image(f, target_size, confidence_threshold), uploaded_files))
+        for i, result in enumerate(results):
+            if "error" not in result:
+                new_row = pd.DataFrame([{k: v for k, v in result.items() if k != "Top 3 Signs"}])
+                st.session_state.predictions_df = pd.concat([st.session_state.predictions_df, new_row], ignore_index=True)
+                
+                # Display prediction in the selected language
+                lang = st.session_state.selected_language
+                sign_type = "number" if result['Predicted Sign'].isdigit() else "letter"
+                st.markdown(f"""
+                <div class='prediction-card flash'>
+                    <h3>{translations[lang]['prediction_text'].format(sign=result['Predicted Sign'])}</h3>
+                    <p>{translations[lang]['confidence_text'].format(confidence=result['Confidence'])}</p>
+                </div>
+                """, unsafe_allow_html=True)
+                st.markdown(translations[lang]['description_text'].format(sign=result['Predicted Sign'], type=sign_type))
+                st.markdown(translations[lang]['top_3_text'])
+                for s, c in result["Top 3 Signs"]:
+                    st.markdown(translations[lang]['top_3_item'].format(sign=s, confidence=c))
+            else:
+                st.error(result["error"])
+            progress_bar.progress((i + 1) / total_files)
+    
+    if not st.session_state.predictions_df.empty:
+        st.subheader("Prediction Summary")
+        st.markdown("**Search Prediction History**")
+        search_query = st.text_input("Search by sign, image name, or confidence (e.g., 'A', 'image1.jpg', '0.9')", "", help="Enter a sign, image name, or confidence value")
+        filter_sign = st.multiselect("Filter by Predicted Sign", options=class_labels, default=[], help="Filter predictions by sign")
+        filtered_df = st.session_state.predictions_df
+        if filter_sign:
+            filtered_df = filtered_df[filtered_df["Predicted Sign"].isin(filter_sign)]
+        if search_query:
+            try:
+                confidence_search = float(search_query) if search_query.replace('.', '', 1).isdigit() else None
+                filtered_df = filtered_df[
+                    (filtered_df["Predicted Sign"].str.contains(search_query, case=False)) |
+                    (filtered_df["Image"].str.contains(search_query, case=False)) |
+                    (filtered_df["Confidence"].apply(lambda x: abs(x - confidence_search) < 0.05) if confidence_search is not None else False)
+                ]
+            except ValueError:
+                filtered_df = filtered_df[
+                    (filtered_df["Predicted Sign"].str.contains(search_query, case=False)) |
+                    (filtered_df["Image"].str.contains(search_query, case=False))
+                ]
+        selected_row = st.dataframe(
+            filtered_df[["Image", "Predicted Sign", "Confidence"]].style.format({"Confidence": "{:.2%}"}),
+            on_select="rerun",
+            selection_mode="single-row",
+            use_container_width=True
+        )
+        if selected_row["selection"]["rows"]:
+            idx = selected_row["selection"]["rows"][0]
+            row = filtered_df.iloc[idx]
+            st.image(base64.b64decode(row["Image Base64"]), caption="Processed Image", width=200)
+            st.markdown(f"**Sign**: {row['Predicted Sign']}")
+            st.markdown(f"**Confidence**: {row['Confidence']:.2%}")
+            st.markdown(f"**Description**: Sign {row['Predicted Sign']} represents the {'number' if row['Predicted Sign'].isdigit() else 'letter'} {row['Predicted Sign']} in Indian Sign Language.")
+        
+        st.subheader("Export Results")
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.download_button(
+                label="Download Predictions as CSV",
+                data=st.session_state.predictions_df[["Image", "Predicted Sign", "Confidence"]].to_csv(index=False).encode('utf-8'),
+                file_name="predictions.csv",
+                mime="text/csv",
+                help="Download predictions as CSV"
+            )
+        with col2:
+            st.download_button(
+                label="Download PDF Report",
+                data=generate_pdf_report(st.session_state.predictions_df),
+                file_name="isl_report.pdf",
+                mime="application/pdf",
+                help="Download predictions as PDF"
+            )
+        with col3:
+            if st.button("Share Prediction", help="Share the latest prediction"):
+                latest_prediction = st.session_state.predictions_df.iloc[-1].to_dict()
+                prediction_json = json.dumps(latest_prediction)
+                encoded = urllib.parse.quote(prediction_json)
+                share_url = f"{st.get_option('server.baseUrlPath')}?prediction={encoded}"
+                st.markdown(f"Share this prediction: [Link]({share_url})")
+
+# Tab 2: Visualization
+with tab2:
+    st.subheader("Prediction History Visualization")
+    generate_visualization(st.session_state.predictions_df)
+
+# Tab 3: Feedback
+with tab3:
+    st.subheader("Feedback")
+    with st.form("feedback_form"):
+        st.markdown("Help us improve the app!")
+        rating = st.slider("Rate this app (1-5) ⭐", 1, 5, help="Rate your experience")
+        comments = st.text_area("Comments 💬", help="Share your thoughts (processed in English)")
+        submitted = st.form_submit_button("Submit Feedback")
+        if submitted:
+            st.success("Thank you for your feedback!")
+            with open("feedback.txt", "a") as f:
+                f.write(f"Rating: {rating}, Comments: {comments}\n")
+
+# Tab 4: Learning
+with tab4:
+    st.subheader("Sign Learning Mode")
+    st.markdown("Practice Indian Sign Language signs by viewing images and descriptions from the dataset.")
+    lang = st.session_state.selected_language
+    sign = st.session_state.current_sign
+    sign_type = "number" if sign.isdigit() else "letter"
+    
+    # Load and display sign image
+    image_path = load_sign_image(sign)
+    if image_path:
+        st.image(
+            image_path,
+            caption=f"Indian Sign Language Sign: {sign}",
+            width=200,
+            use_column_width=False,
+            output_format="auto",
+            clamp=True,
+            channels="RGB"
+        )
+    else:
+        st.warning(f"Image for sign {sign} not found in {DATASET_PATH}. Ensure a subfolder '{sign}' or file '{sign}.png/jpg' exists.")
+    
+    # Display text description
+    st.markdown(f"""
+    <div class='learning-card'>
+        <h3>{translations[lang]['learning_text'].format(sign=sign)}</h3>
+        <p>{translations[lang]['learning_description'].format(sign=sign, type=sign_type)}</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    if st.button("Show New Sign", help="Display a new random sign"):
+        st.session_state.current_sign = random.choice(class_labels)
+        st.rerun()
+
+# Footer
+st.markdown("<div class='footer'>Powered by Streamlit, TensorFlow, OpenCV.</div>", unsafe_allow_html=True)

feedback.txt

@@ -0,0 +1 @@
+Rating: 3, Comments: 

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62080bdc993096063431743337ac555cfefc473bc346a8ca1af42f450e295a49
+size 9924263

report.html

@@ -0,0 +1,8 @@
+
+    <h1>Indian Sign Language Classification Report</h1>
+    <p>Generated on: 2025-04-15 13:M:45</p>
+    <table border='1'>
+        <tr><th>Image</th><th>Predicted Sign</th><th>Confidence</th></tr>
+        <tr><td>0.jpg</td><td>R</td><td>100.00%</td></tr>
+    </table>
+    

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+numpy
+opencv-python
+tensorflow

sign_language_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5045bf665f989babd4ee5abd87b84a9e30adfaf15dbf3b62c8166eb2eb1f4350
+size 29700184