Uploaded 3 files

app.py

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+import streamlit as st
+import numpy as np
+import cv2
+from PIL import Image
+from io import BytesIO
+from ultralytics import YOLO
+from datetime import datetime
+from gtts import gTTS
+import tempfile
+import os
+import base64
+import ollama
+import bcrypt
+import sqlite3
+import time
+from deep_translator import GoogleTranslator
+#from transformers import AutoTokenizer, AutoModelForCausalLM, AutoImageProcessor, pipeline
+#import torch
+#from huggingface_hub import from_pretrained_keras
+import requests
+
+# Database setup
+conn = sqlite3.connect('users.db')
+c = conn.cursor()
+c.execute('''CREATE TABLE IF NOT EXISTS users
+             (id INTEGER PRIMARY KEY AUTOINCREMENT,
+              username TEXT UNIQUE,
+              password_hash TEXT)''')
+conn.commit()
+
+# Password hashing and verification
+def hash_password(password):
+    return bcrypt.hashpw(password.encode(), bcrypt.gensalt())
+
+def verify_password(password, hashed_password):
+    return bcrypt.checkpw(password.encode(), hashed_password)
+
+# Add a user
+def add_user(username, password):
+    # Check if username already exists
+    c.execute("SELECT id FROM users WHERE username = ?", (username,))
+    result = c.fetchone()
+
+    if result:
+        return False  # Username already exists
+
+    # Hash the password and insert the new user
+    password_hash = hash_password(password)
+    c.execute("INSERT INTO users (username, password_hash) VALUES (?, ?)",
+              (username, password_hash))
+    conn.commit()
+
+    return True
+
+# Verify a user
+def verify_user(username, password):
+    c.execute("SELECT password_hash FROM users WHERE username = ?", (username,))
+    result = c.fetchone()
+    if result:
+        return verify_password(password, result[0])
+    return False
+
+# Login and logout
+def login(username, password):
+    if not username or not password:
+        st.error("Username and password are required.")
+        return False
+    if verify_user(username, password):
+        st.session_state['authenticated'] = True
+        st.session_state['username'] = username
+        st.session_state['last_activity'] = time.time()
+        return True
+    st.error("Invalid username or password.")
+    return False
+
+def logout():
+    st.session_state['authenticated'] = False
+    st.session_state['username'] = None
+
+# Add this at the top of your file
+def local_css():
+    st.markdown("""
+        <style>
+        .stButton>button {
+            width: 100%;
+            border-radius: 5px;
+            height: 3em;
+            margin-top: 10px;
+        }
+        
+        .auth-container {
+            max-width: 400px;
+            margin: auto;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+            background-color: white;
+        }
+        
+        .auth-title {
+            text-align: center;
+            font-size: 24px;
+            margin-bottom: 20px;
+            color: #1f1f1f;
+        }
+        
+        .auth-subtitle {
+            text-align: center;
+            font-size: 16px;
+            margin-bottom: 20px;
+            color: #666;
+        }
+        
+        .hero-section {
+            text-align: center;
+            padding: 40px 20px;
+            background: linear-gradient(to right, #4f46e5, #3b82f6);
+            color: white;
+            margin-bottom: 30px;
+        }
+        
+        .feature-container {
+            max-width: 1200px;
+            margin: auto;
+            padding: 20px;
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 20px;
+            margin-bottom: 40px;
+        }
+        
+        .feature-card {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            transition: transform 0.3s ease, box-shadow 0.3s ease;
+        }
+
+        .feature-card:hover {
+            transform: scale(1.05);
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+        }
+
+        .feature-title {
+            color: #1f1f1f;
+            font-size: 18px;
+            margin-bottom: 10px;
+            font-weight: bold;
+        }
+        
+        .feature-text {
+            color: #666;
+            font-size: 14px;
+        }
+        
+        .divider {
+            text-align: center;
+            margin: 20px 0;
+            position: relative;
+        }
+        
+        .divider:before {
+            content: "";
+            position: absolute;
+            top: 50%;
+            left: 0;
+            right: 0;
+            height: 1px;
+            background-color: #e0e0e0;
+            z-index: -1;
+        }
+        
+        .divider span {
+            background-color: white;
+            padding: 0 10px;
+            color: #666;
+            font-size: 14px;
+        }
+
+        @keyframes typing {
+            0% {
+                width: 0;
+            }
+            50% {
+                width: 100%;
+            }
+            60% {
+                width: 100%;
+            }
+            100% {
+                width: 0;
+            }
+        }
+
+        @keyframes blink {
+            50% {
+                border-color: transparent;
+            }
+        }
+
+        .hero-title{
+            display: inline-block;
+            font-size: 2.5em;
+            white-space: nowrap;
+            overflow: hidden;
+            border-right: 2px solid white;
+            width: 0;
+            animation: typing 6s steps(40, end) infinite, blink 0.5s step-end infinite;
+        }
+
+        .hero-section {
+            text-align: center;
+            padding: 40px 20px;
+            background: linear-gradient(45deg, #4f46e5, #3b82f6);
+            background-size: 300% 300%;
+            animation: gradientShift 8s ease infinite;
+            color: white;
+            margin-bottom: 30px;
+            opacity: 0;
+            animation: fadeIn 2s ease-in-out forwards;
+        }
+
+        @keyframes fadeIn {
+            from {
+                opacity: 0;
+            }
+            to {
+                opacity: 1;
+            }
+        }
+
+        @keyframes gradientShift {
+            0% {
+                background-position: 0% 50%;
+            }
+            50% {
+                background-position: 100% 50%;
+            }
+            100% {
+                background-position: 0% 50%;
+            }
+        }
+
+        /*.feature-container {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            gap: 20px;
+            position: relative;
+            width: 100%;
+            height: 300px;
+            animation: rotate 20s linear infinite; /* Rotate the container */
+        }
+
+        .feature-card {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            transition: transform 0.3s ease, box-shadow 0.3s ease;
+            flex-shrink: 0;
+            width: 250px;
+        }
+
+        .feature-card:hover {
+            transform: scale(1.1);
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+        }
+
+        @keyframes rotate {
+            from {
+                transform: rotate(0deg);
+            }
+            to {
+                transform: rotate(-360deg);
+            }
+        */}
+
+        /*.feature-container {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            overflow: hidden;
+            position: relative;
+            width: 100%;
+            height: 300px;
+        }
+
+        .feature-track {
+            display: flex;
+            animation: circularMove 15s linear infinite;
+        }
+
+        .feature-card {
+            flex: 0 0 300px; /* Fixed width for each card */
+            margin: 0 20px;
+            background: white;
+            color: #333; /* Text color */
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            text-align: center; /* Center-align the text */
+            overflow: hidden; /* Prevent overflow issues */
+        }
+
+        .feature-card h3 {
+            font-size: 1.2em;
+            margin-bottom: 10px;
+            text-align: center;
+        }
+
+        .feature-card p {
+            font-size: 0.9em;
+            line-height: 1.4;
+            text-align: center;
+            font-weight: bold;
+        }
+
+
+        .feature-card:hover {
+            transform: scale(1.1);
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+        }
+
+        @keyframes circularMove {
+            0% {
+                transform: translateX(0);
+            }
+            100% {
+                transform: translateX(-100%);
+            }
+        */}
+        .feature-container {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            height: 400px;
+            perspective: 1000px;
+            perspective-origin: 50% 50%;
+            background: linear-gradient(to bottom, #1e293b, #0f172a); /* Dark blue gradient background */
+            overflow: hidden;
+            position: relative;
+            padding: 40px 0;
+        }
+
+        .feature-track {
+            position: relative;
+            width: 100%;
+            height: 100%;
+            display: flex;
+            transform-style: preserve-3d;
+            animation: carousel 15s linear infinite;
+        }
+
+        .feature-card {
+            position: absolute;
+            width: 300px;
+            padding: 50px;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); /* Enhanced shadow for better contrast */
+            backface-visibility: hidden;
+            transform-origin: center center;
+            transition: all 0.5s ease;
+        }
+
+        .feature-card h3 {
+            color: #1e293b;
+            font-size: 1.5em;
+            margin-bottom: 1rem;
+            font-weight: bold;
+        }
+
+        .feature-card p {
+            color: #475569;
+            line-height: 1.6;
+        }
+
+        /* Position and animate cards */
+        .feature-card:nth-child(1) {
+            transform: rotateY(0deg) translateZ(400px) translateX(0px);
+        }
+
+        .feature-card:nth-child(2) {
+            transform: rotateY(60deg) translateZ(400px) translateX(0px);
+        }
+
+        .feature-card:nth-child(3) {
+            transform: rotateY(120deg) translateZ(400px) translateX(0px);
+        }
+
+        .feature-card:nth-child(4) {
+            transform: rotateY(180deg) translateZ(400px) translateX(0px);
+        }
+
+        .feature-card:nth-child(5) {
+            transform: rotateY(240deg) translateZ(400px) translateX(0px);
+        }
+
+        .feature-card:nth-child(6) {
+            transform: rotateY(300deg) translateZ(400px) translateX(0px);
+        }
+
+        @keyframes carousel {
+            0% {
+                transform: translateZ(-400px) rotateY(0deg);
+            }
+            100% {
+                transform: translateZ(-400px) rotateY(-360deg);
+            }
+        }
+
+        /* Enhanced hover effect with glow */
+        .feature-card:hover {
+            transform: scale(1.1) translateZ(450px);
+            box-shadow: 0 8px 30px rgba(255, 255, 255, 0.1); /* Glowing effect */
+            z-index: 1;
+        }
+
+        /* Gradient overlays for depth effect */
+        .feature-container::before,
+        .feature-container::after {
+            content: '';
+            position: absolute;
+            width: 100%;
+            height: 100px;
+            z-index: 2;
+            pointer-events: none;
+        }
+
+        .feature-container::before {
+            top: 0;
+            background: linear-gradient(to bottom, #1e293b, rgba(30, 41, 59, 0));
+        }
+
+        .feature-container::after {
+            bottom: 0;
+            background: linear-gradient(to top, #1e293b, rgba(30, 41, 59, 0));
+        </style>
+    """, unsafe_allow_html=True)
+
+# Check session expiry
+if 'authenticated' in st.session_state and st.session_state['authenticated']:
+    if time.time() - st.session_state['last_activity'] > 1800:  # 30 minutes
+        logout()
+        st.rerun()
+    st.session_state['last_activity'] = time.time()
+
+# Initialize session state for registration form visibility
+if 'show_register_form' not in st.session_state:
+    st.session_state['show_register_form'] = False
+
+# Replace your login/registration section with this:
+if 'authenticated' not in st.session_state or not st.session_state['authenticated']:
+    local_css()
+    
+    # Landing page hero section
+    st.markdown("""
+        <div class="hero-section">
+            <h1 class="hero-title" style="font-size: 2.5em; margin-bottom: 20px;">Crop Disease Detection System</h1>
+            <p style="font-size: 1.2em; max-width: 800px; margin: 0 auto;">
+                An advanced AI-powered system that helps farmers and agricultural experts identify and manage crop diseases effectively
+            </p>
+        </div>
+
+    """, unsafe_allow_html=True)
+    
+    # Features section using Streamlit columns
+    st.subheader("Key Features")
+    col1, col2, col3 = st.columns(3)
+
+    st.markdown("""
+        <div class="feature-container">
+            <div class="feature-track">
+                <div class="feature-card">
+                    <h3>🔍 Instant Detection</h3>
+                    <p>Upload images of your crops and get immediate disease detection results using state-of-the-art AI technology.</p>
+                </div>
+                <div class="feature-card">
+                    <h3>💡 Expert Analysis</h3>
+                    <p>Receive detailed analysis and recommendations from our plant pathology expert system.</p>
+                </div>
+                <div class="feature-card">
+                    <h3>📊 Detailed Reports</h3>
+                    <p>Generate comprehensive reports with treatment recommendations and preventive measures.</p>
+                </div>
+                <div class="feature-card">
+                    <h3>🔍 Instant Detection</h3>
+                    <p>Upload images of your crops and get immediate disease detection results using state-of-the-art AI technology.</p>
+                </div>
+                <div class="feature-card">
+                    <h3>💡 Expert Analysis</h3>
+                    <p>Receive detailed analysis and recommendations from our plant pathology expert system.</p>
+                </div>
+                <div class="feature-card">
+                    <h3>📊 Detailed Reports</h3>
+                    <p>Generate comprehensive reports with treatment recommendations and preventive measures.</p>
+                </div>
+            </div>
+        </div>
+    """, unsafe_allow_html=True)
+
+    # Crop carousel section
+    st.markdown("""
+        <div class="crop-carousel-container">
+            <div class="crop-carousel-track">
+                <div class="crop-card">
+                    <img src="https://github.com/ROBERT-ADDO-ASANTE-DARKO/AI-powered-crop-disease-detection/blob/main/images/b034333ddcc732299d45abf753f3fa71f6ff48ffa3338bfecd615bc2.jpg?raw=true" alt="Crop 1">
+                    <h4>Corn Leaf Blight</h4>
+                    <p>Corn leaf blight is a fungal disease caused primarily by Exserohilum turcicum (Northern corn leaf blight) and Bipolaris maydis (Southern corn leaf blight).</p>
+                </div>
+                <div class="crop-card">
+                    <img src="https://github.com/ROBERT-ADDO-ASANTE-DARKO/AI-powered-crop-disease-detection/blob/main/images/apple.jpg?raw=true" alt="Crop 2">
+                    <h4>Apple Scab Leaf</h4>
+                    <p>Apple scab is a fungal disease caused by Venturia inaequalis. It primarily affects apple and crabapple trees.</p>
+                </div>
+                <div class="crop-card">
+                    <img src="https://github.com/ROBERT-ADDO-ASANTE-DARKO/AI-powered-crop-disease-detection/blob/main/images/tomato.jpg?raw=true" alt="Crop 3">
+                    <h4>Tomato Leaf Late Blight</h4>
+                    <p>Late blight of tomato is caused by the oomycete pathogen Phytophthora infestans. It is characterized by dark, water-soaked lesions on leaves, stems, and fruit.</p>
+                </div>
+                <div class="crop-card">
+                    <img src="https://github.com/ROBERT-ADDO-ASANTE-DARKO/AI-powered-crop-disease-detection/blob/main/images/918d1d7a3dda5ce8fbdabf92e5bf38f104efd129ee09adcc6d1ad46c.jpg?raw=true" alt="Crop 4">
+                    <h4>Tomato Leaf Yellow Virus</h4>
+                    <p>Tomato leaf yellow virus (often referred to as Tomato yellow leaf curl virus, or TYLCV) is a viral disease transmitted by whiteflies. It causes yellowing and curling of tomato leaves.</p>
+                </div>
+            </div>
+        </div>
+    """, unsafe_allow_html=True)
+
+    st.markdown("""
+        <style>
+        .crop-carousel-container {
+            width: 100%;
+            max-width: 800px;
+            margin: auto;
+            overflow: hidden;
+            position: relative;
+        }
+
+        .crop-carousel-track {
+            display: flex;
+            animation: moveLeft 20s linear infinite; /* Move right to left */
+        }
+
+        .crop-card {
+            flex: 0 0 300px;
+            margin: 0 20px;
+            background: white;
+            color: #333;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            text-align: center;
+            overflow: hidden;
+        }
+
+        .crop-card img {
+            width: 100%;
+            height: 150px;
+            object-fit: cover;
+            border-radius: 10px;
+            margin-bottom: 10px;
+        }
+
+        .crop-card h4 {
+            font-size: 1.2em;
+            margin: 10px 0;
+        }
+
+        .crop-card p {
+            font-size: 0.9em;
+            line-height: 1.4;
+            color: #555;
+        }
+
+        @keyframes moveLeft {
+            0% {
+                transform: translateX(100%);
+            }
+            100% {
+                transform: translateX(-100%);
+            }
+        }
+        </style>
+        """, unsafe_allow_html=True)
+   
+    
+    # Add some spacing
+    st.markdown("<br>", unsafe_allow_html=True)
+    
+    # Authentication container
+    st.markdown('<div class="auth-container">', unsafe_allow_html=True)
+
+    # Initialize password reset state
+    if 'show_reset_form' not in st.session_state:
+        st.session_state['show_reset_form'] = False
+
+    # Update password function
+    def update_password(username, new_password):
+        conn = sqlite3.connect('users.db')
+        c = conn.cursor()
+        
+        # Check if username exists
+        c.execute("SELECT id FROM users WHERE username = ?", (username,))
+        if not c.fetchone():
+            return False
+        
+        # Update password
+        password_hash = bcrypt.hashpw(new_password.encode(), bcrypt.gensalt())
+        c.execute("UPDATE users SET password_hash = ? WHERE username = ?", 
+                (password_hash, username))
+        conn.commit()
+        conn.close()
+        return True
+
+
+    # Update the authentication container section
+if not st.session_state.get('authenticated', False):
+    st.markdown('<div class="auth-container">', unsafe_allow_html=True)
+    
+    # Reset Password Form
+    if st.session_state.get('show_reset_form', False):
+        st.markdown('<h1 class="auth-title">Reset Password</h1>', unsafe_allow_html=True)
+        st.markdown('<p class="auth-subtitle">Enter your username and new password</p>', unsafe_allow_html=True)
+        
+        with st.form("reset_form"):
+            username = st.text_input("Username")
+            new_password = st.text_input("New Password", type="password")
+            confirm_password = st.text_input("Confirm Password", type="password")
+            submit = st.form_submit_button("Reset Password")
+            
+            if submit:
+                if not username or not new_password or not confirm_password:
+                    st.error("All fields are required.")
+                elif new_password != confirm_password:
+                    st.error("Passwords do not match.")
+                elif update_password(username, new_password):
+                    st.success("Password updated successfully!")
+                    st.session_state['show_reset_form'] = False
+                    time.sleep(1)
+                    st.rerun()
+                else:
+                    st.error("Username not found.")
+        
+        if st.button("Back to Login"):
+            st.session_state['show_reset_form'] = False
+            st.rerun()
+    
+    # Registration Form
+    elif st.session_state.get('show_register_form', False):
+        st.markdown('<h1 class="auth-title">Create Account</h1>', unsafe_allow_html=True)
+        st.markdown('<p class="auth-subtitle">Sign up to get started</p>', unsafe_allow_html=True)
+        
+        with st.form("register_form"):
+            new_username = st.text_input("Username")
+            new_password = st.text_input("Password", type="password")
+            submit_button = st.form_submit_button("Create Account")
+            
+            if submit_button:
+                if new_username and new_password:
+                    if add_user(new_username, new_password):
+                        st.success("Account created successfully!")
+                        st.session_state['show_register_form'] = False
+                        time.sleep(1)
+                        st.rerun()
+                    else:
+                        st.error("Username already exists.")
+                else:
+                    st.error("Username and password are required.")
+        
+        st.markdown('<div class="divider"><span>OR</span></div>', unsafe_allow_html=True)
+        if st.button("Back to Login"):
+            st.session_state['show_register_form'] = False
+            st.rerun()
+    
+    # Login Form (default)
+    else:
+        st.markdown('<h1 class="auth-title">Welcome Back</h1>', unsafe_allow_html=True)
+        st.markdown('<p class="auth-subtitle">Sign in to your account</p>', unsafe_allow_html=True)
+        
+        with st.form("login_form"):
+            username = st.text_input("Username")
+            password = st.text_input("Password", type="password")
+            cols = st.columns([1, 1])
+            submit_button = cols[0].form_submit_button("Sign In")
+            forgot_password = cols[1].form_submit_button("Forgot Password?")
+            
+            if submit_button:
+                if login(username, password):
+                    st.success("Logged in successfully!")
+                    time.sleep(1)
+                    st.rerun()
+            elif forgot_password:
+                st.session_state['show_reset_form'] = True
+                st.rerun()
+        
+        st.markdown('<div class="divider"><span>OR</span></div>', unsafe_allow_html=True)
+        if st.button("Create New Account"):
+            st.session_state['show_register_form'] = True
+            st.rerun()
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Update the footer section (replace the existing footer with this)
+    st.markdown("""
+    <div style="background: linear-gradient(to right, #1e293b, #334155); color: white; padding: 40px 0; margin-top: 40px;">
+        <div style="max-width: 1200px; margin: auto; padding: 0 20px;">
+            <div style="display: flex; flex-wrap: wrap; justify-content: space-between; gap: 40px;">
+                <!-- About Section -->
+                <div style="flex: 1; min-width: 250px;">
+                    <h3 style="color: #60a5fa; font-size: 1.5em; margin-bottom: 20px;">About Our Platform</h3>
+                    <p style="color: #e2e8f0; line-height: 1.6; margin-bottom: 20px;">
+                        Our AI-powered platform revolutionizes crop disease detection and management. 
+                        We combine cutting-edge technology with agricultural expertise to protect your crops 
+                        and maximize your yield.
+                    </p>
+                </div>
+    <div style="flex: 1; min-width: 250px;">
+                    <h3 style="color: #60a5fa; font-size: 1.5em; margin-bottom: 20px;">Key Features</h3>
+                    <ul style="list-style: none; padding: 0; color: #e2e8f0;">
+                        <li style="margin-bottom: 10px; display: flex; align-items: center;">
+                            <span style="color: #60a5fa; margin-right: 10px;">✓</span> Real-time Disease Detection
+                        </li>
+                        <li style="margin-bottom: 10px; display: flex; align-items: center;">
+                            <span style="color: #60a5fa; margin-right: 10px;">✓</span> Multi-language Support
+                        </li>
+                        <li style="margin-bottom: 10px; display: flex; align-items: center;">
+                            <span style="color: #60a5fa; margin-right: 10px;">✓</span> Expert Analysis Reports
+                        </li>
+                        <li style="margin-bottom: 10px; display: flex; align-items: center;">
+                            <span style="color: #60a5fa; margin-right: 10px;">✓</span> Treatment Recommendations
+                        </li>
+                    </ul>
+                </div>
+    <div style="flex: 1; min-width: 250px;">
+                    <h3 style="color: #60a5fa; font-size: 1.5em; margin-bottom: 20px;">Contact Us</h3>
+                    <p style="color: #e2e8f0; line-height: 1.6; margin-bottom: 10px;">
+                        <span style="color: #60a5fa;">Email:</span> [email protected]
+                    </p>
+                    <p style="color: #e2e8f0; line-height: 1.6; margin-bottom: 20px;">
+                        <span style="color: #60a5fa;">Phone:</span> +1 (234) 567-8900
+                    </p>
+                    <div style="display: flex; gap: 15px; margin-top: 20px;">
+                        <a href="#" style="color: #60a5fa; text-decoration: none; font-size: 1.2em;">
+                            <span>📱</span>
+                        </a>
+                        <a href="#" style="color: #60a5fa; text-decoration: none; font-size: 1.2em;">
+                            <span>💬</span>
+                        </a>
+                        <a href="#" style="color: #60a5fa; text-decoration: none; font-size: 1.2em;">
+                            <span>📨</span>
+                        </a>
+                    </div>
+                </div>
+            </div>
+            <div style="border-top: 1px solid #4b5563; margin-top: 40px; padding-top: 20px; text-align: center;">
+                <p style="color: #e2e8f0; font-size: 0.9em;">
+                    © 2025 Crop Disease Detection System. All rights reserved.
+                </p>
+                <div style="margin-top: 10px;">
+                    <a href="#" style="color: #e2e8f0; text-decoration: none; margin: 0 10px; font-size: 0.9em;">Privacy Policy</a>
+                    <a href="#" style="color: #e2e8f0; text-decoration: none; margin: 0 10px; font-size: 0.9em;">Terms of Service</a>
+                    <a href="#" style="color: #e2e8f0; text-decoration: none; margin: 0 10px; font-size: 0.9em;">FAQ</a>
+                </div>
+            </div>
+        </div>
+    </div>
+""", unsafe_allow_html=True)
+    
+    st.stop()
+
+# Update database schema to include comments
+def setup_feedback_db():
+    conn = sqlite3.connect('customer_feedback.db')
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS customer_feedback
+                 (id INTEGER PRIMARY KEY AUTOINCREMENT,
+                  question TEXT,
+                  response TEXT,
+                  feedback_type TEXT,
+                  comment_type TEXT,
+                  custom_comment TEXT,
+                  timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
+    conn.commit()
+    return conn, c
+
+def save_feedback(question, response, feedback_type, comment_type=None, custom_comment=None):
+    conn, c = setup_feedback_db()
+    try:
+        c.execute("""INSERT INTO customer_feedback 
+                    (question, response, feedback_type, comment_type, custom_comment) 
+                    VALUES (?, ?, ?, ?, ?)""",
+                  (question, response, feedback_type, comment_type, custom_comment))
+        conn.commit()
+        return True
+    except Exception as e:
+        st.error(f"Error saving feedback: {e}")
+        return False
+    finally:
+        conn.close()
+
+# Update the conversation display section
+def display_feedback_buttons(file_id, index, question, response):
+    # Suggested comments
+    SUGGESTED_COMMENTS = [
+        "Inaccurate information",
+        "Unclear explanation",
+        "Missing details",
+        "Not relevant to question",
+        "Technical error",
+        "Other"
+    ]
+    
+    # Initialize session state for feedback if it doesn't exist
+    if f"feedback_{file_id}_{index}" not in st.session_state:
+        st.session_state[f"feedback_{file_id}_{index}"] = {
+            "feedback_type": None,  # Stores "👍" or "👎"
+            "comment": None,        # Stores the user's comment
+            "submitted": False      # Tracks whether feedback has been submitted
+        }
+    
+    col1, col2 = st.columns([1, 4])
+    with col1:
+        if st.button("👍", key=f"helpful_{file_id}_{index}"):
+            # Save positive feedback immediately
+            save_feedback(question, response, "👍")
+            st.success("Feedback saved!")
+            # Update session state to indicate feedback has been submitted
+            st.session_state[f"feedback_{file_id}_{index}"]["submitted"] = True
+            return
+        
+    with col2:
+        if st.button("👎", key=f"not_helpful_{file_id}_{index}"):
+            # Store the feedback type in session state
+            st.session_state[f"feedback_{file_id}_{index}"]["feedback_type"] = "👎"
+        
+        # Check if feedback_type is "👎" before showing the comment input field
+        if st.session_state[f"feedback_{file_id}_{index}"].get("feedback_type") == "👎":
+            # Display suggested comments in a dropdown menu
+            selected_comment = st.selectbox(
+                "What was the issue?",
+                options=SUGGESTED_COMMENTS,
+                key=f"suggested_comment_{file_id}_{index}"
+            )
+            
+            # If the user selects "Other", allow them to provide a custom comment
+            custom_comment = None
+            if selected_comment == "Other":
+                custom_comment = st.text_area(
+                    "Please describe the issue:",
+                    key=f"custom_comment_{file_id}_{index}"
+                )
+            
+            # Submit Feedback button
+            if st.button("Submit Feedback", key=f"submit_{file_id}_{index}"):
+                # Save feedback to the database
+                save_feedback(
+                    question,
+                    response,
+                    st.session_state[f"feedback_{file_id}_{index}"]["feedback_type"],
+                    custom_comment if selected_comment == "Other" else selected_comment
+                )
+                st.success("Thank you for your feedback!")
+                # Update session state to indicate feedback has been submitted
+                st.session_state[f"feedback_{file_id}_{index}"]["submitted"] = True
+                return
+
+# Model configuration
+SUPPORTED_MODELS = {
+    "llama3.2": {
+        "name": "llama3.2",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": False
+    },
+    "llama3.1": {
+        "name": "llama3.1",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": False
+    },
+    "llama2": {
+        "name": "llama2",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": False
+    },
+    "llava": {
+        "name": "llava",
+        "system_prompt": "You are a helpful plant pathology expert assistant with vision capabilities.",
+        "supports_vision": True,
+        "vision_prompt": "Analyze the image and describe the diseases present."
+    },
+    "mistral": {
+        "name": "mistral",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": False
+    },
+    "gemma": {
+        "name": "gemma",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": False
+    },
+    "jyan1/paligemma-mix-224": {
+        "name": "jyan1/paligemma-mix-224",
+        "system_prompt": "You are a helpful plant pathology expert assistant.",
+        "supports_vision": True
+    }
+}
+
+# Initialize session state for conversation history if it doesn't exist
+if 'conversation_history' not in st.session_state:
+    st.session_state.conversation_history = {}
+
+# Load YOLOv8 model
+yolo_model = YOLO("models/best.pt")
+
+def preprocess_image(image, target_size=(224, 224)):
+    """
+    Preprocess the image for vision-capable models.
+    """
+    image = Image.fromarray(image)
+    image = image.resize(target_size)
+    return image
+
+def text_to_speech(text, language='en'):
+    """Convert text to speech using gTTS"""
+    try:
+        # Create temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            # Generate audio file
+            tts = gTTS(text=text, lang=language, slow=False)
+            tts.save(temp_audio.name)
+            
+            # Read the audio file
+            with open(temp_audio.name, 'rb') as audio_file:
+                audio_bytes = audio_file.read()
+            
+            # Clean up
+            os.unlink(temp_audio.name)
+            
+            return audio_bytes
+    except Exception as e:
+        st.error(f"Error generating speech: {str(e)}")
+        return None
+    
+def check_ollama_connection():
+    try:
+        response = requests.get("http://localhost:11434")
+        return response.status_code == 200
+    except Exception as e:
+        return False
+
+def generate_ollama_response(prompt, model_name="llama2", conversation_history=None, image_data=None):
+    try:
+        if model_name not in SUPPORTED_MODELS:
+            return f"Error: Model {model_name} is not supported."
+
+        model_config = SUPPORTED_MODELS[model_name]
+
+        # Build the messages array
+        messages = [
+            {
+                "role": "system",
+                "content": model_config["system_prompt"]
+            }
+        ]
+
+        # Add conversation history
+        if conversation_history:
+            for entry in conversation_history:
+                if len(entry) >= 2:  # Handle tuples with 2 or 3 values
+                    question, response = entry[:2]
+                    messages.extend([
+                        {"role": "user", "content": question},
+                        {"role": "assistant", "content": response}
+                    ])
+
+        # Handle vision models differently
+        if model_config["supports_vision"] and image_data is not None:
+            if isinstance(image_data, np.ndarray):
+                image = Image.fromarray(image_data)
+                buffered = BytesIO()
+                image.save(buffered, format="JPEG")
+                img_str = base64.b64encode(buffered.getvalue()).decode()
+
+                messages.append({
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {"type": "image", "image": img_str}
+                    ]
+                })
+        else:
+            messages.append({
+                "role": "user",
+                "content": prompt
+            })
+
+        # Make an API call to Ollama
+        api_url = "http://localhost:11434/api/generate"  # Ollama API endpoint
+        payload = {
+            "model": model_config["name"],
+            "prompt": prompt,  # Use the prompt directly
+            "stream": False  # Set to True if you want streaming responses
+        }
+
+        # Send the request
+        response = requests.post(api_url, json=payload)
+
+        # Check for errors
+        if response.status_code != 200:
+            return f"Error: API request failed with status code {response.status_code}. Response: {response.text}"
+
+        # Parse the response
+        response_data = response.json()
+        
+        # Check if the response contains the expected key
+        if "response" in response_data:
+            return response_data["response"]
+        else:
+            return f"Error: Unexpected response format: {response_data}"
+
+    except Exception as e:
+        return f"Error connecting to Ollama API: {str(e)}"
+
+def generate_improved_description(detected_classes, class_names, user_text, image_details=None, conversation_history=None):
+    """
+    Generate a more detailed and contextual description using Ollama
+    """
+    detected_objects = [class_names[cls] for cls in detected_classes]
+    
+    # Create base context about detected diseases
+    disease_context = f"Detected diseases: {', '.join(detected_objects)}"
+    
+    # Different prompt structure for initial vs. follow-up questions
+    if not conversation_history:
+        base_prompt = f"""As an expert plant pathologist, analyze the following crop diseases detected in the image: {', '.join(detected_objects)}.
+
+For each detected disease, provide a structured analysis following this format:
+
+1. Disease Name: [Name]
+   - Pathogen: [Causative organism]
+   - Severity Level: [Based on visual symptoms]
+   - Key Symptoms:
+     * [Symptom 1]
+     * [Symptom 2]
+   - Economic Impact:
+     * [Brief description of potential crop losses]
+   - Treatment Options:
+     * Immediate actions: [Short-term solutions]
+     * Long-term management: [Preventive measures]
+   - Environmental Conditions:
+     * Favorable conditions for disease development
+     * Risk factors
+
+2. Recommendations:
+   - Immediate Steps:
+     * [Action items for immediate control]
+   - Prevention Strategy:
+     * [Long-term prevention measures]
+   - Monitoring Protocol:
+     * [What to watch for]
+
+Initial Question/Context: {user_text if user_text else "Provide a general analysis"}
+"""
+    else:
+        base_prompt = f"""Context: {disease_context}
+
+Previous conversation context has been provided above. Please address the following follow-up question while maintaining consistency with previous responses:
+
+{user_text}
+
+Provide a detailed response that builds upon the previous context and specifically addresses this question."""
+
+    # Get the selected model from session state or default to llama2
+    selected_model = st.session_state.get('selected_model', 'llama2')
+    
+    return generate_ollama_response(
+        base_prompt, 
+        model_name=selected_model,
+        conversation_history=conversation_history,
+        image_data=image_details.get("image_data") if image_details else None
+    )
+
+def inference(image):
+    """
+    Enhanced inference function with confidence scores and bounding box information
+    """
+    results = yolo_model(image, conf=0.4)
+    infer = np.zeros(image.shape, dtype=np.uint8)
+    classes = dict()
+    names_infer = []
+    confidence_scores = []
+    bounding_boxes = []
+
+    for r in results:
+        infer = r.plot()
+        classes = r.names
+        names_infer = r.boxes.cls.tolist()
+        confidence_scores = r.boxes.conf.tolist()
+        bounding_boxes = r.boxes.xyxy.tolist()
+    
+    return infer, names_infer, classes, confidence_scores, bounding_boxes
+
+# Streamlit application
+st.title("Interactive Crop Disease Detection and Analysis🌾🌿🥬☘️")
+st.write(f"Welcome, {st.session_state['username']}!😊")
+
+# Logout button
+if st.button("Logout"):
+    logout()
+    st.rerun()
+
+# Add sidebar for configuration
+with st.sidebar:
+    st.header("Settings")
+    selected_model = st.selectbox(
+        "Select LLM Model",
+        list(SUPPORTED_MODELS.keys()),
+        index=0,  # Default to first model (bart-large-cnn)
+        help="Choose the Ollama model to use for analysis"
+    )
+    # Store the selected model in session state
+    st.session_state['selected_model'] = selected_model
+    
+    if SUPPORTED_MODELS[selected_model]["supports_vision"]:
+        st.info("This model supports vision capabilities and can analyze images directly.")
+    
+    confidence_threshold = st.slider("Detection Confidence Threshold", 0.0, 1.0, 0.4)
+    show_confidence = st.checkbox("Show Confidence Scores", value=True)
+    show_bbox = st.checkbox("Show Bounding Boxes", value=True)
+    
+    # TTS Settings
+    st.header("Text-to-Speech Settings")
+    tts_enabled = st.checkbox("Enable Text-to-Speech", value=True)
+    if tts_enabled:
+        language = st.selectbox("Speech Language", 
+                              options=['en', 'es', 'fr', 'de'],
+                              format_func=lambda x: {
+                                  'en': 'English',
+                                  'es': 'Spanish',
+                                  'fr': 'French',
+                                  'de': 'German'
+                              }[x],
+                              help="Select speech language")
+    
+    # Add option to clear conversation history
+    if st.button("Clear All Conversations"):
+        st.session_state.conversation_history = {}
+        st.success("Conversation history cleared!")
+
+# Language selection
+language = st.selectbox(
+    "Select Language",
+    options=['en', 'es', 'fr', 'de'],  # Add more languages as needed
+    format_func=lambda x: {
+        'en': 'English',
+        'es': 'Spanish',
+        'fr': 'French',
+        'de': 'German'
+    }[x],
+    help="Select your preferred language"
+)
+
+# Main content
+uploaded_files = st.file_uploader("Upload images for disease detection", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+
+if uploaded_files:
+    for uploaded_file in uploaded_files:
+        file_id = uploaded_file.name
+        
+        # Initialize conversation history for this image if it doesn't exist
+        if file_id not in st.session_state.conversation_history:
+            st.session_state.conversation_history[file_id] = []
+        
+        st.header(f"Analysis for {file_id}")
+        
+        # Create columns for side-by-side display
+        col1, col2 = st.columns(2)
+        
+        # Process image
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, 1)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        
+        # Display original image
+        with col1:
+            st.subheader("Original Image")
+            st.image(image, use_container_width=True)
+        
+        # Process and display results
+        with st.spinner("Processing image..."):
+            infer_image, classes_in_image, classes_in_dataset, confidences, boxes = inference(image)
+        
+        with col2:
+            st.subheader("Detected Diseases")
+            st.image(infer_image, use_container_width=True)
+        
+        # Display detection details
+        if show_confidence:
+            st.subheader("Detection Details")
+            for cls, conf in zip(classes_in_image, confidences):
+                st.write(f"- {classes_in_dataset[cls]}: {conf:.2%} confidence")
+
+       # Display conversation history
+        if st.session_state.conversation_history[file_id]:
+            st.subheader("Conversation History")
+            for i, entry in enumerate(st.session_state.conversation_history[file_id]):
+                question, response = entry[:2]
+                
+                with st.expander(f"Q{i+1}: {question[:50]}...", expanded=False):
+                    st.write("**Question:**", question)
+                    st.write("**Response:**", response)
+                    
+                    # Display feedback buttons and handle comment collection
+                    display_feedback_buttons(file_id, i, question, response)
+                    
+                    # Audio playback option
+                    if tts_enabled:
+                        if st.button("🔊 Listen", key=f"listen_history_{file_id}_{i}"):
+                            with st.spinner("Generating audio..."):
+                                audio_bytes = text_to_speech(response, language)
+                                if audio_bytes:
+                                    st.audio(audio_bytes, format='audio/mp3')
+                            
+        
+        # User input for questions
+        st.subheader("Ask Questions")
+        user_text = st.text_area(
+            "Enter your question about the detected diseases:",
+            placeholder="Example: What are the best treatment options for these diseases? What preventive measures should I take?",
+            key=f"question_{file_id}"
+        )
+        
+        def translate_text(text, target_lang='en'):
+            translator = GoogleTranslator(source='auto', target=target_lang)
+            return translator.translate(text)
+
+        # Use the async function in your Streamlit app
+        if st.button("Get Analysis", key=f"analyze_{file_id}"):
+            with st.spinner(f"Generating analysis using {selected_model}..."):
+                # Perform translation
+                translated_input = translate_text(user_text, target_lang='en')
+                st.write(f"Translated Input (to English): {translated_input}")
+
+                # Create detailed image information dictionary
+                image_details = {
+                    "confidence_scores": confidences,
+                    "bounding_boxes": boxes,
+                    "image_dimensions": image.shape,
+                    "image_data": image  # Add the image data for vision models
+                }
+
+                # Generate response
+                response = generate_improved_description(
+                    classes_in_image,
+                    classes_in_dataset,
+                    translated_input,
+                    image_details,
+                    st.session_state.conversation_history[file_id]
+                )
+
+                # Translate LLM response
+                translated_response = translate_text(response, target_lang=language)
+                
+                # Add to conversation history and display the response
+                st.session_state.conversation_history[file_id].append((user_text, translated_response, None))
+                st.markdown("### Latest Response")
+                st.markdown(translated_response)
+                
+                # Add audio playback option for the latest response
+                if tts_enabled:
+                    col1, col2 = st.columns([1, 4])
+                    with col1:
+                        if st.button("🔊 Listen", key=f"listen_latest_{file_id}"):
+                            with st.spinner("Generating audio..."):
+                                audio_bytes = text_to_speech(translated_response, language)
+                                if audio_bytes:
+                                    st.audio(audio_bytes, format='audio/mp3')
+        
+        # Export conversation
+        if st.button("Export Conversation", key=f"export_{file_id}"):
+            conversation_text = f"""
+            # Crop Disease Analysis Report
+
+            ## Image Information
+            - Filename: {file_id}
+            - Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+            
+            ## Detected Diseases
+            {', '.join([classes_in_dataset[cls] for cls in classes_in_image])}
+            
+            ## Conversation History
+            """
+            
+            for i, entry in enumerate(st.session_state.conversation_history[file_id]):
+                if len(entry) == 2:  # Handle legacy entries
+                    question, response = entry
+                    feedback = "No feedback"
+                else:
+                    question, response, feedback = entry
+                
+                conversation_text += f"\n### Question {i+1}:\n{question}\n\n### Answer {i+1}:\n{response}\n\n### Feedback {i+1}:\n{feedback}\n"
+            
+            st.download_button(
+                label="Download Conversation",
+                data=conversation_text,
+                file_name=f"disease_analysis_{file_id}.md",
+                mime="text/markdown"
+            )
+
+# Add a footer with clear instructions
+st.markdown("""
+---
+### How to Use
+1. Upload one or more images of crops with potential diseases
+2. View the detected diseases and their confidence scores
+3. Ask questions about the diseases, treatments, or prevention
+4. Use the 🔊 Listen button to hear the responses
+5. View previous questions and answers in the conversation history
+6. Export the entire conversation for future reference
+7. Use the sidebar to adjust settings or clear conversation history
+""")

startup.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+# Start Ollama in the background
+ollama serve &
+
+# Wait for Ollama to initialize
+echo "Waiting for Ollama to start..."
+sleep 5
+
+# Pull required models
+echo "Pulling Ollama models..."
+ollama pull llama2
+#ollama pull llama3.1
+#ollama pull llama3.2
+#ollama pull mistral
+ollama list
+
+curl -X POST http://localhost:11434/api/generate \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "llama2",
+  "prompt": "Hello, world!"
+}'
+
+# Start supervisord to manage processes
+echo "Starting supervisord..."
+exec /usr/bin/supervisord -c /etc/supervisor/conf.d/supervisord.conf

supervisord.conf

@@ -0,0 +1,16 @@
+[supervisord]
+nodaemon=true
+
+[program:ollama]
+command=ollama serve
+autostart=true
+autorestart=true
+stderr_logfile=/dev/stderr
+stdout_logfile=/dev/stdout
+
+[program:streamlit]
+command=streamlit run app.py --server.port=8501 --server.address=0.0.0.0
+autostart=true
+autorestart=true
+stderr_logfile=/dev/stderr
+stdout_logfile=/dev/stdout