after changes

app.py

@@ -2,7 +2,6 @@ import io
 import os
 import re
 from datetime import datetime, timedelta
-
 import numpy as np
 import pandas as pd
 import requests
@@ -13,10 +12,49 @@ from groq import Groq
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 
-# Import database and user management functions
-from database import get_user_history
-from user_management import show_user_management, save_history_if_logged_in
-from utils import extract_keyword  # Ensure utils.py has the extract_keyword function
+# Import database functions directly here.
+# These functions should NOT import anything from app.py or user_management.py
+# If you don't have database.py, you'll need to create it with these functions.
+# For demonstration, I'll assume they exist.
+try:
+    from database import get_user_history, register_user, user_exists, check_user_credentials, save_user_history
+    # Assuming get_user_history, register_user, user_exists, check_user_credentials, save_user_history are in database.py
+except ImportError:
+    st.error("Could not import database functions. Please ensure database.py exists and contains required functions.")
+    # Define dummy functions to allow the app to run without a real database.py for now
+    def get_user_history(user_id):
+        st.warning("Database function 'get_user_history' not implemented. History will not be persistent.")
+        return []
+    def register_user(user_id, full_name, dob, email, password):
+        st.warning("Database function 'register_user' not implemented. User registration will not be persistent.")
+        return True # Simulate success
+    def user_exists(user_id):
+        st.warning("Database function 'user_exists' not implemented. User existence check will not work.")
+        return False # Simulate user not existing
+    def check_user_credentials(user_id, password):
+        st.warning("Database function 'check_user_credentials' not implemented. Login will not work.")
+        return False # Simulate login failure
+    def save_user_history(user_id, symptoms, predicted_diseases):
+        st.warning("Database function 'save_user_history' not implemented. History saving will not be persistent.")
+        pass
+
+
+# Import user management functions from user_management.py
+# These functions will now receive st.session_state as an argument
+from user_management import render_user_management_sidebar, save_history_to_db_if_logged_in
+
+# Import utility functions
+try:
+    from utils import extract_keyword  # Ensure utils.py has the extract_keyword function
+except ImportError:
+    st.error("Could not import utility functions. Please ensure utils.py exists and contains required functions.")
+    def extract_keyword(text, keywords):
+        # Dummy implementation if utils.py is missing
+        for kw in keywords:
+            if kw.lower() in text.lower():
+                return kw
+        return "Unknown"
+
 
 # Load environment variables from .env.local for local development.
 # On Hugging Face Spaces, st.secrets will be used.
@@ -33,10 +71,6 @@ try:
     GROQ_API_KEY = st.secrets.get("GROQ_API_KEY")
     if not GROQ_API_KEY:
         # Fallback to environment variable for local development if not in secrets
-        # You might not want to write raw file content to the app in production
-        # f = open(".env", "r")
-        # st.write(f.read())
-        # f.close()
         GROQ_API_KEY = os.getenv("GROQ_API_KEY")
     if GROQ_API_KEY:
         groq_client = Groq(api_key=GROQ_API_KEY)
@@ -78,7 +112,7 @@ if "feedback_input" not in st.session_state:  # For clearing feedback text area
 # Ensure user_id is initialized for saving history
 if "user_id" not in st.session_state:
     st.session_state.user_id = None
-if "logged_in_user" not in st.session_state:
+if "logged_in_user" not in st.session_state: # This will hold the user_id after successful login
     st.session_state.logged_in_user = None
 
 # --- HARDCODED DATA LOADING FROM CSVs ---
@@ -90,6 +124,7 @@ def load_csv_data():
         description_df = pd.read_csv('symptom_Description.csv').fillna('')
         precaution_df = pd.read_csv('symptom_precaution.csv').fillna('')
         severity_df = pd.read_csv('Symptom-severity.csv').fillna('')  # Load symptom severity
+
         # Prepare data for quick lookup
         # Dataset mapping diseases to their symptoms
         disease_symptoms_map = {}
@@ -98,13 +133,17 @@ def load_csv_data():
             # Get all symptoms for this disease, filtering out empty strings and 'Disease' column itself
             symptoms = [s.strip().replace('_', ' ') for s in row.values[1:] if s.strip()]
             disease_symptoms_map[disease] = symptoms
+
         # Disease descriptions map
         disease_description_map = {row['Disease']: row['Description'] for index, row in description_df.iterrows()}
+
         # Disease precautions map
         disease_precaution_map = {row['Disease']: [p.strip() for p in row.values[1:] if p.strip()] for index, row in precaution_df.iterrows()}
+
         # Symptom severity map
         # Ensure symptom names are consistent (e.g., lowercase and spaces instead of underscores)
         symptom_severity_map = {row['Symptom'].strip().replace('_', ' ').lower(): row['weight'] for index, row in severity_df.iterrows()}
+
         # Extract all unique symptoms for the multiselect
         all_unique_symptoms = sorted(list(set(symptom for symptoms_list in disease_symptoms_map.values() for symptom in symptoms_list)))
         return disease_symptoms_map, disease_description_map, disease_precaution_map, all_unique_symptoms, symptom_severity_map
@@ -137,7 +176,6 @@ st.markdown("""
         margin-bottom: 2rem;
         background-color: #ffffff; /* White background */
     }
-
     /* Headers */
     h1, h2, h3, h4, h5, h6 {
         color: #004d99; /* A professional darker blue */
@@ -148,7 +186,6 @@ st.markdown("""
     h1 { font-size: 2.8em; text-align: center; color: #003366; } /* Larger, darker blue for main title */
     h2 { font-size: 2.2em; border-bottom: 2px solid #e0e7ee; padding-bottom: 0.5em; margin-bottom: 1em; } /* Subtle line under h2 */
     h3 { font-size: 1.6em; }
-
     /* Main Tabs Styling (Home, History, Feedback, About, Insights) */
     .stTabs [data-baseweb="tab-list"] {
         gap: 20px; /* More space between tabs */
@@ -191,7 +228,6 @@ st.markdown("""
         outline: none !important;
         box-shadow: 0 0 0 0.25rem rgba(0, 123, 255, 0.25) !important; /* Subtle focus glow if needed */
     }
-
     /* Nested Tabs Styling (Symptom Checker, Chat with MediBot) */
     /* This targets tabs that are children of other tabs, making them smaller */
     .stTabs [data-baseweb="tab-list"]:has(.stTabs [data-baseweb="tab-list"]) [data-baseweb="tab"],
@@ -224,17 +260,16 @@ st.markdown("""
         box-shadow: 0 2px 8px rgba(0,0,0,0.15); /* Subtle shadow for selected sub-tab */
         outline: none !important; /* Ensure no outline when selected */
     }
-
     /* General Button Styling */
     .stButton>button {
         background-color: #007bff; /* Primary blue button */
         color: white;
-        border-radius: 10px; /* More rounded */ /* Corrected: was border_radius */
+        border-radius: 10px; /* More rounded */
         padding: 12px 28px; /* More padding */
         font-weight: 600; /* Semi-bold */
         border: none;
         cursor: pointer;
-        box-shadow: 0 5px 15px rgba(0,0,0,0.1); /* Softer, more diffuse shadow */ /* Corrected: was box_shadow */
+        box-shadow: 0 5px 15px rgba(0,0,0,0.1); /* Softer, more diffuse shadow */
         transition: all 0.3s ease-in-out; /* Smooth transitions */
         font-size: 1.05em; /* Slightly larger text */
         outline: none; /* Remove outline on focus */
@@ -243,7 +278,7 @@ st.markdown("""
     }
     .stButton>button:hover {
         background-color: #0056b3; /* Darker blue on hover */
-        box-shadow: 0 8px 20px rgba(0,0,0,0.15); /* Larger shadow on hover */ /* Corrected: was box_shadow */
+        box-shadow: 0 8px 20px rgba(0,0,0,0.15); /* Larger shadow on hover */
         transform: translateY(-3px); /* More pronounced lift */
         outline: none; /* Ensure no outline on hover either */
     }
@@ -252,17 +287,15 @@ st.markdown("""
         width: 100%; /* Full width buttons in sidebar */
         margin-bottom: 10px;
     }
-
-
     /* Input Fields, Selects, etc. */
     .stTextInput>div>div>input,
     .stTextArea>div>div>textarea, /* Target textarea as well */
     .stDateInput>div>div>input,
     .stMultiSelect>div>div {
-        border-radius: 10px; /* More rounded */ /* Corrected: was border_radius */
+        border-radius: 10px; /* More rounded */
         border: 1px solid #ced4da; /* Light grey border */
         padding: 10px 15px; /* More padding */
-        box-shadow: inset 0 1px 4px rgba(0,0,0,0.05); /* Inner shadow */ /* Corrected: was box_shadow */
+        box-shadow: inset 0 1px 4px rgba(0,0,0,0.05); /* Inner shadow */
         transition: border-color 0.2s ease-in-out, box-shadow 0.2s ease-in-out;
         background-color: #fefefe; /* Slightly off-white background */
         outline: none; /* Remove default outline */
@@ -286,10 +319,10 @@ st.markdown("""
     /* Expander (for disease info) */
     .stExpander {
         border: 1px solid #e0e7ee; /* Lighter grey border */
-        border-radius: 12px; /* More rounded */ /* Corrected: was border_radius */
+        border-radius: 12px; /* More rounded */
         margin-bottom: 15px; /* More space below */
         background-color: #ffffff; /* White background */
-        box-shadow: 0 4px 12px rgba(0,0,0,0.05); /* Softer shadow */ /* Corrected: was box_shadow */
+        box-shadow: 0 4px 12px rgba(0,0,0,0.05); /* Softer shadow */
         overflow: hidden; /* Ensure rounded corners clip content */
     }
     .stExpander > div > div > .streamlit-expanderHeader {
@@ -341,7 +374,6 @@ st.markdown("""
     .stAlert.st-error { background-color: #f8d7da; color: #721c24; border-color: #f5c6cb; }
     /* Spinners */
     .stSpinner { color: #007bff; } /* Spinner color matching primary button */
-
     /* Disclaimer Box - using a more reliable target */
     div[data-testid="stVerticalBlock"] > div:first-child > div:has(p > strong:contains("Disclaimer")) {
         background-color: #fff8e1; /* Light yellow for warnings/disclaimers */
@@ -356,7 +388,6 @@ st.markdown("""
     .stAlert[data-testid="stAlert"] p strong:contains("Important Disclaimer") {
         color: #664d03;
     }
-
     /* General text improvements */
     p {
         line-height: 1.7;
@@ -373,7 +404,6 @@ st.markdown("""
     .stExpander > div > div > .streamlit-expanderHeader {
         font-size: 1.2em; /* Increased font size */
     }
-
     /* Media queries for specific mobile adjustments if needed (example) */
     @media (max-width: 768px) {
         h1 {
@@ -398,8 +428,7 @@ st.markdown("""
             padding: 3px 8px !important;
         }
     }
-    </style>
-""", unsafe_allow_html=True)
+    </style>""", unsafe_allow_html=True)
 
 # --- Groq API Response Function (updated system prompt) ---
 def get_groq_response(user_query, severity_label="Undetermined Severity", model="llama-3.3-70b-versatile"):
@@ -409,7 +438,6 @@ def get_groq_response(user_query, severity_label="Undetermined Severity", model=
     """
     if not GROQ_AVAILABLE or groq_client is None:
         return "AI assistant is not available."
-
     # Augment the system prompt with severity information from clinical diagnosis model
     # Note: 'severity_label' comes from the symptom checker. If asking a direct question,
     #       it might default to "Undetermined Severity" unless explicitly passed from a prior analysis.
@@ -455,7 +483,6 @@ def get_diagnosis_and_severity_from_model(symptoms_text):
     if not HF_MODEL_AVAILABLE or not HF_API_TOKEN:
         st.warning("Hugging Face API key not available. Cannot assess severity.")
         return "Severity Assessment Unavailable (API Key Missing)", []
-
     API_URL = "https://api-inference.huggingface.co/models/DATEXIS/CORe-clinical-diagnosis-prediction"
     headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
     payload = {"inputs": symptoms_text}
@@ -463,7 +490,6 @@ def get_diagnosis_and_severity_from_model(symptoms_text):
         response = requests.post(API_URL, headers=headers, json=payload)
         response.raise_for_status()
         result = response.json()
-
         raw_predicted_diagnoses = []
         threshold = 0.5
         if result and isinstance(result, list) and len(result) > 0 and isinstance(result[0], list):
@@ -488,7 +514,6 @@ def get_diagnosis_and_severity_from_model(symptoms_text):
             'unlikely', 'possible', 'likely',
             'symptom', 'sign', 'pain', 'fever', 'cough', 'vomiting', 'nausea', 'rash', 'headache', 'fatigue', 'diarrhea', 'sore throat', 'hemorrhage'
         ]
-
         for diagnosis_label in raw_predicted_diagnoses:
             lower_diag = diagnosis_label.lower()
             is_generic = False
@@ -500,7 +525,6 @@ def get_diagnosis_and_severity_from_model(symptoms_text):
                 is_generic = True
             if len(lower_diag.split()) <= 2 and lower_diag in ['pain', 'fever', 'cough', 'vomiting', 'nausea', 'rash', 'headache', 'fatigue', 'diarrhea', 'sore throat', 'hemorrhage']:
                 is_generic = True
-
             if not is_generic:
                 filtered_diagnoses.append(diagnosis_label)
 
@@ -527,9 +551,7 @@ def get_diagnosis_and_severity_from_model(symptoms_text):
                     overall_severity = "Moderate Severity"
                 # Keep looping to see if a High Severity diagnosis is found later in the filtered list
                 # Don't return here if a Moderate is found, continue to check for High
-
         return f"Overall Symptom Severity (AI Assessment): {overall_severity}", filtered_diagnoses
-
     except requests.exceptions.RequestException as e:
         st.error(f"Network or API Error during clinical diagnosis model call: {e}. Check API key and internet connection.")
         return "Severity Assessment Unavailable (Network Error)", []
@@ -547,11 +569,9 @@ def generate_pdf_report(history_data):
     c.setFont("Helvetica", 10)
     c.drawString(50, height - 70, f"Report Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     y = height - 100
-
     for entry in history_data:
         # history_data comes as list of tuples: (history_id, symptoms, predicted_diseases, query_timestamp)
         timestamp, symptoms_text, predicted_diseases = entry[3], entry[1], entry[2]
-
         if y < 100:
             c.showPage()
             c.setFont("Helvetica-Bold", 16)
@@ -563,7 +583,6 @@ def generate_pdf_report(history_data):
         c.drawString(50, y, f"Timestamp: {timestamp.strftime('%Y-%m-%d %H:%M:%S')}")
         y -= 15
         c.setFont("Helvetica", 10)
-
         symptoms_line = f"Symptoms/Question: {symptoms_text}"
         insight_line = f"Insight/Response: {predicted_diseases}"
 
@@ -600,7 +619,6 @@ def generate_pdf_report(history_data):
         draw_wrapped_text(textobject, insight_line, width - 100)
         c.drawText(textobject)
         y -= 20
-
     c.save()
     buffer.seek(0)
     return buffer
@@ -670,7 +688,6 @@ def parse_insight_text_for_conditions(insight_text: str) -> list[str]:
 if "feedback_input" not in st.session_state:
     st.session_state.feedback_input = ""
 
-
 if st.session_state.show_welcome:
     # A more visually appealing welcome page
     st.markdown("<h1 style='color: #0056b3; font-size: 3.5em; margin-top: 50px; text-shadow: 2px 2px 4px rgba(0,0,0,0.1);'>MediBot - Your Personal Health Assistant 🏥</h1>", unsafe_allow_html=True)
@@ -711,10 +728,12 @@ else:
                 "</p>"
                 "</div>", unsafe_allow_html=True)
 
-    # Sidebar for User Management (remains in sidebar)
+    # Sidebar for User Management
     with st.sidebar:
         st.header("User Management")
-        show_user_management()
+        # Call the refactored user management function, passing st.session_state
+        render_user_management_sidebar(st.session_state)
+
         st.markdown("---")
         st.header("Navigation")
         # Add a button to clear chat history
@@ -723,9 +742,10 @@ else:
             st.session_state.last_chat_response = ""
             st.session_state.chat_input_value = ""
             st.success("Chat history cleared!")
+
         # PDF Download Button - available if user is logged in and has history
-        if st.session_state.get("logged_in_user"):
-            user_history = get_user_history(st.session_state["logged_in_user"])
+        if st.session_state.get("user_id"):
+            user_history = get_user_history(st.session_state["user_id"]) # Directly call get_user_history
             if user_history:
                 pdf_buffer = generate_pdf_report(user_history)
                 st.download_button(
@@ -737,9 +757,10 @@ else:
                 )
             else:
                 st.info("No history to download yet. Interact with MediBot to generate a report.")
+        else:
+            st.info("Log in to download your health report.")
 
     st.markdown("<h1 style='text-align: center; color: #0056b3;'>MediBot - Your Health Assistant 🩺</h1>", unsafe_allow_html=True)
-
     # Top-level tabs: Home, History, Feedback, About, Insights
     # Re-ordered tabs slightly to put Insights closer to History
     main_tab_home, main_tab_history, main_tab_insights, main_tab_feedback, main_tab_about = st.tabs(["Home", "History", "Insights", "Feedback", "About"])
@@ -832,9 +853,8 @@ else:
                             predicted_diseases_str_for_history = f"{severity_assessment_text.replace('Overall Symptom Severity (AI Assessment): ', '')}. No specific insights found."
 
                         if st.session_state.get("user_id"):
-                            # This needs to be called correctly. The original line had a missing argument.
-                            # Assuming save_history_if_logged_in takes (user_object, query, response)
-                            save_history_if_logged_in(st.session_state.get("logged_in_user"), ", ".join(selected_symptoms), predicted_diseases_str_for_history)
+                            # Call the refactored save history function
+                            save_history_to_db_if_logged_in(st.session_state.user_id, ", ".join(selected_symptoms), predicted_diseases_str_for_history)
                             st.success("Analysis saved to your history.")
                         else:
                             st.info("Log in to save this analysis to your history.")
@@ -857,23 +877,18 @@ else:
                         # For the chatbot, if it's a direct question, severity is undetermined
                         # unless passed from a preceding symptom check.
                         # For simplicity, if not from symptom checker, pass "Undetermined Severity".
-                        # You could enhance this to try to infer severity from the question itself if needed.
                         severity_for_groq_prompt = "Undetermined Severity"
-                        # If you want to use the severity from a recent symptom check,
-                        # you'd need to store it in session_state and pass it here.
-                        # For now, we're keeping the Q&A separate.
                         with st.spinner("MediBot is thinking..."):
                             groq_answer = get_groq_response(user_question, severity_label=severity_for_groq_prompt)
                             st.markdown("**MediBot's Answer:**")
                             st.write(groq_answer)
-
                             # Save Q&A to chat history (if desired, not necessarily DB)
                             st.session_state.chat_history.append({"user": user_question, "bot": groq_answer})
 
                             # Save to database history if logged in
                             if st.session_state.get("user_id"):
                                 # Prepend "Question: " to the input for history tracking
-                                save_history_if_logged_in(st.session_state.get("logged_in_user"), f"Question: {user_question}", groq_answer)
+                                save_history_to_db_if_logged_in(st.session_state.user_id, f"Question: {user_question}", groq_answer)
                                 st.success("Your question and answer have been saved to history.")
                             else:
                                 st.info("Log in to save this Q&A to your history.")
@@ -886,13 +901,11 @@ else:
         if user_id:
             st.info("This section shows your saved symptom analyses and health questions.")
             history_data = get_user_history(user_id)  # Fetch history from database
-
             if history_data:
                 st.subheader("Past Interactions:")
                 # Display history in reverse chronological order using expanders
                 for entry in reversed(history_data):
                     timestamp, symptoms_text, insight_text = entry[3], entry[1], entry[2]
-
                     # Determine summary title based on the nature of the input
                     summary_title_prefix = ""
                     expander_icon = ""  # Initialize icon here
@@ -924,7 +937,6 @@ else:
                         expander_icon = "🩺"
 
                     # MODIFIED LINE: Use Markdown heading (e.g., ###) for larger font and bolding
-                    # Experiment with ## or ### to find the desired size.
                     expander_label = f"### **{timestamp.strftime('%Y-%m-%d %H:%M')}** - {expander_icon} *{summary_title_prefix}*"
                     with st.expander(expander_label):
                         st.write(f"**Your Input:** {symptoms_text}")
@@ -956,7 +968,6 @@ else:
                     entry for entry in history_data
                     if entry[3] and entry[3] > thirty_days_ago  # entry[3] is query_timestamp
                 ]
-
                 if recent_history:
                     all_conditions = []
                     for entry in recent_history:
@@ -1024,4 +1035,4 @@ else:
         st.markdown("[Learn more about Hugging Face](https://huggingface.co/)")
 
 st.markdown("---")
-st.markdown("For professional medical advice, always consult a qualified healthcare provider.")
+st.markdown("For professional medical advice, always consult a qualified healthcare provider.")