', 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('

', unsafe_allow_html=True) # Reset Password Form if st.session_state.get('show_reset_form', False): st.markdown('

Reset Password

', unsafe_allow_html=True) st.markdown('

Enter your username and new password

', 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('

Create Account

', unsafe_allow_html=True) st.markdown('

', 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('

', 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('

Welcome Back

', unsafe_allow_html=True) st.markdown('

', 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('

', unsafe_allow_html=True) if st.button("Create New Account"): st.session_state['show_register_form'] = True st.rerun() st.markdown('

', unsafe_allow_html=True) # Update the footer section (replace the existing footer with this) st.markdown("""

About Our Platform

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.

Key Features

✓ Real-time Disease Detection
✓ Multi-language Support
✓ Expert Analysis Reports
✓ Treatment Recommendations

Contact Us

Email: support@crophealth.ai

Phone: +1 (234) 567-8900

📱 💬 📨

""", 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 """)

Crop Disease Detection System

🔍 Instant Detection

💡 Expert Analysis

📊 Detailed Reports

🔍 Instant Detection

💡 Expert Analysis

📊 Detailed Reports

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