discarded.txt

    # st.markdown("""
    # ## Welcome to the **Early Prediction of Health & Lifestyle Diseases**!  
    # This tool provides **early prediction and analysis** for various health conditions using **Machine Learning & NLP**.
    
    # ### 🏥 Available Features:
    # - **🩸 Disease Predictors**:
    #   - Diabetes Prediction   
    #   - Hypertension Prediction  
    #   - Cardiovascular Disease Prediction 
    #   - Stroke Prediction
    #   - Asthma Prediction 
    #   - Sleep Health Analysis
    # # - **✅ Checkbox-based Lifestyle Disease Predictor** using **BiomedNLP-PubMedBERT**  
    # - **🤖 Medical Consultant** (Ask health-related questions)  
    # # - **🧠 Mental Health Assessment**  
    # - **📊 Data Visualizer** (Analyze trends in health conditions)  
                
    # 👉 Select an option from the sidebar to proceed!  
    # """)

    # with st.expander("🚀 Quick Start Guide"):
    #     st.write("""
    #     1. Select a **health prediction model** from the sidebar.
    #     2. Enter your details in the input fields.
    #     3. Click **Predict** to get your result.
    #     4. View personalized **health insights & recommendations**.
    #     """)


    # # Disclaimer Section
    # st.markdown("---")
    # # st.markdown("""
    # # **⚠️ Disclaimer:** This application has been developed using **real-world healthcare datasets** sourced from Kaggle:  

    # # - [Stroke Prediction Dataset](http://kaggle.com/code/chanchal24/stroke-prediction-using-python/input?select=healthcare-dataset-stroke-data.csv)  
    # # - [Asthma Analysis & Prediction](https://www.kaggle.com/code/bryamblasrimac/asthma-eda-prediction-f2score-85/input)  
    # # - [Diabetes Dataset](https://www.kaggle.com/datasets/mathchi/diabetes-data-set)  
    # # - [Cardiovascular Disease Dataset](https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset)  
    # # - [Sentiment Analysis for Mental Health](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)  
    # # - [Sleep Health Analysis](https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset)
    # # \


    # st.markdown("""
    # The predictions are generated using **machine learning models** trained on these datasets, incorporating **evaluation metrics and graphical insights** to enhance interpretability.  

    # However, this tool has **not undergone clinical validation** and should be used **for informational and educational purposes only**. It is not intended to serve as a substitute for professional medical diagnosis or treatment. Always consult a qualified healthcare provider for medical advice.
    # """)



    # if selected == 'Medical Consultant':
#     st.title("🩺 Medical Consultant Chatbot")
#     st.markdown("### Discuss Your Health Concerns with Our AI-powered Chatbot")
#     st.write("Ask about **Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health.**")

#     genai.configure(api_key="AIzaSyAwyi9c5OdvLoWrv5lFi1jZDEYwuprQAKE")

#     # Custom Styling
#     st.markdown("""
#         <style>
#             .prompt-box { 
#                 background-color: #000000; 
#                 padding: 12px; 
#                 border-radius: 8px; 
#                 font-size: 14px; 
#                 font-family: sans-serif;
#                 margin-bottom: 10px;
#                 border: 1px solid #dee2e6;
#                 text-align: center;
#             }
#         </style>
#     """, unsafe_allow_html=True)

#     st.markdown("#### 💡 Common Health Queries")

#     prompt_options = [
#         ("Diabetes – Diet", "What foods should I eat if I have diabetes?"),
#         ("Diabetes – Exercise", "What type of workouts help control blood sugar levels?"),
#         ("Asthma – Triggers", "What are common asthma triggers?"),
#         ("Asthma – Treatment", "What are the best medications for asthma?"),
#         ("Stroke – Symptoms", "What are the early warning signs of a stroke?"),
#         ("Stroke – Prevention", "How can I reduce my risk of stroke?"),
#         ("Cardiovascular – Heart Health", "How can I reduce my risk of heart disease?"),
#         ("Cardiovascular – Blood Pressure", "What lifestyle changes can lower high blood pressure?"),
#         ("Mental Health – Stress Management", "How can I manage stress effectively?"),
#         ("Mental Health – Sleep Disorders", "What are the causes and treatments for sleep disorders?")
#     ]

#     # Display prompts in two columns (2 prompts per row)
#     cols = st.columns(2)
#     for i in range(0, len(prompt_options), 2):
#         with cols[0]: 
#             if i < len(prompt_options):
#                 label, prompt = prompt_options[i]
#                 st.markdown(f"""<div class="prompt-box"><strong>{label}</strong><br>{prompt}</div>""", unsafe_allow_html=True)

#         with cols[1]: 
#             if i+1 < len(prompt_options):
#                 label, prompt = prompt_options[i+1]
#                 st.markdown(f"""<div class="prompt-box"><strong>{label}</strong><br>{prompt}</div>""", unsafe_allow_html=True)

#     # Initialize chat history if not present
#     if "chat_history" not in st.session_state:
#         st.session_state.chat_history = []

#     # Display previous chat history
#     for message in st.session_state.chat_history:
#         with st.chat_message(message["role"]):
#             st.markdown(message["content"])

#     # User input field
#     user_prompt = st.chat_input("Ask about Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health...")

#     # List of allowed topics
#     allowed_keywords = ["diabetes", "asthma", "stroke", "cardiovascular", "heart", "blood pressure", 
#                         "mental health", "depression", "stress", "cholesterol", "sleep disorders"]

#     if user_prompt:
#         # Display user message
#         st.chat_message("user").markdown(user_prompt)
#         st.session_state.chat_history.append({"role": "user", "content": user_prompt})

#         # Restriction: Only process if related to health topics
#         if any(keyword in user_prompt.lower() for keyword in allowed_keywords):
#             model = genai.GenerativeModel("gemini-2.0-flash")
#             response = model.generate_content(user_prompt)

#             if response and hasattr(response, "text"):
#                 assistant_response = response.text
#             else:
#                 assistant_response = "I'm sorry, I couldn't generate a response."

#             st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})

#             # Display assistant's response
#             with st.chat_message("assistant"):
#                 st.markdown(assistant_response)
#         else:
#             # Restriction message
#             restriction_msg = "**⚠️ This chatbot only responds to health-related topics.**\nPlease ask about Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health."
#             st.session_state.chat_history.append({"role": "assistant", "content": restriction_msg})
            
#             with st.chat_message("assistant"):
#                 st.markdown(restriction_msg)



# if selected == 'Checkbox-to-disease-predictor':
# # Load transformer model
#     classifier = pipeline("zero-shot-classification", model="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
#     # Use a pipeline as a high-level helper

#     # from transformers import pipeline

#     # pipe = pipeline("fill-mask", model="microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext")


#     # Define symptoms for each disease
#     diseases = {
#         "Diabetes": ["Frequent urination", "Increased thirst", "Unexplained weight loss", "Fatigue", "Blurred vision"],
#         "Hypertension": ["Headache", "Dizziness", "Chest pain", "Shortness of breath", "Nosebleeds"],
#         "Obesity": ["Excess body fat", "Breathlessness", "Joint pain", "Increased sweating", "Low energy levels"],
#         "Cardiovascular Disease": ["Chest pain", "Shortness of breath", "Dizziness", "Irregular heartbeat", "Fatigue"],
#         "COPD": ["Chronic cough", "Shortness of breath", "Wheezing", "Chest tightness", "Frequent respiratory infections"],
#         "Liver Disease": ["Jaundice", "Abdominal pain", "Swelling in legs", "Chronic fatigue", "Nausea"],
#         "Kidney Disease": ["Swelling in legs", "Fatigue", "Loss of appetite", "Changes in urination", "Muscle cramps"],
#         "Metabolic Syndrome": ["High blood sugar", "High blood pressure", "Increased waist size", "High cholesterol", "Fatigue"],
#         "Osteoarthritis": ["Joint pain", "Stiffness", "Swelling", "Reduced flexibility", "Bone spurs"],
#         "Gastroesophageal Reflux Disease": ["Heartburn", "Acid reflux", "Difficulty swallowing", "Chronic cough", "Sore throat"],
#         "Depression": ["Persistent sadness", "Loss of interest", "Sleep disturbances", "Fatigue", "Difficulty concentrating"],
#         "Sleep Apnea": ["Loud snoring", "Pauses in breathing", "Daytime drowsiness", "Morning headaches", "Irritability"],
#     }

#     # Streamlit UI
#     st.title("🩺 Hybrid Symptom Checker")
#     st.write("Select your symptoms and get AI-powered predictions!")

#     selected_symptoms = []

#     # Create symptom selection with markdown separation and three columns
#     disease_keys = list(diseases.keys())

#     for i in range(0, len(disease_keys), 3):
#         cols = st.columns(3)
#         for j in range(3):
#             if i + j < len(disease_keys):
#                 disease = disease_keys[i + j]
#                 with cols[j]:
#                     st.markdown(f"### {disease}")
#                     for symptom in diseases[disease]:
#                         if st.checkbox(symptom, key=f"{disease}_{symptom}"):
#                             selected_symptoms.append(symptom)

#     if st.button("🔍 Predict Disease"):
#         if selected_symptoms:
#             user_input = ", ".join(selected_symptoms)  # Convert symptoms to text

#             # 1️⃣ Custom Symptom Matching Approach
#             disease_scores = {disease: 0 for disease in diseases.keys()}
#             for disease, symptoms in diseases.items():
#                 matches = sum(symptom in selected_symptoms for symptom in symptoms)
#                 disease_scores[disease] = matches / len(symptoms)  # Normalize by symptom count

#             # Normalize to percentage
#             symptom_match_scores = {d: round(score * 100, 2) for d, score in disease_scores.items()}

#             # 2️⃣ AI Model Prediction
#             ai_results = classifier(user_input, list(diseases.keys()))
#             ai_scores = {ai_results["labels"][i]: round(ai_results["scores"][i] * 100, 2) for i in range(len(ai_results["labels"]))}

#             # 3️⃣ Hybrid Score Calculation (Average of Both Scores)
#             final_scores = {}
#             for disease in diseases.keys():
#                 symptom_score = symptom_match_scores.get(disease, 0)
#                 ai_score = ai_scores.get(disease, 0)
#                 final_scores[disease] = round((symptom_score + ai_score) / 2, 2)  # Averaging

#             # Sort by final score
#             sorted_final_scores = sorted(final_scores.items(), key=lambda x: x[1], reverse=True)

#             # Display results
#             st.write("### 🔬 Possible Conditions (Hybrid Model Prediction):")
#             for disease, score in sorted_final_scores:
#                 if score > 0:
#                     st.write(f"🩺 {disease}: {score}% match")
#         else:
#             st.write("⚠️ Please select at least one symptom.")