refactor(app.py): remove commented-out code and unused sections for cleaner codebase

app.py

@@ -126,56 +126,6 @@ if selected == 'Home':
     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.
     """)
 
-    # 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 == 'Diabetes Prediction':
     st.title('🩸 Diabetes Prediction using ML (SVC)')
     st.image("https://cdn-icons-png.flaticon.com/512/2919/2919950.png", width=100)
@@ -492,182 +442,8 @@ if selected == 'Data Visualization':
             st.pyplot(fig)
 
 
-# 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.")
+
+
 
 
 import torch

discarded.txt

@@ -0,0 +1,228 @@
+    # 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.")