Update app.py

app.py

@@ -2,13 +2,6 @@ import streamlit as st
 import os
 from groq import Groq
 from datetime import datetime
-from transformers import pipeline
-import pandas as pd
-import re
-import torch
-
-# Force CPU usage
-device = torch.device("cpu")
 
 # Set page config FIRST
 st.set_page_config(page_title="AI Medical Consultancy", layout="wide")
@@ -16,80 +9,56 @@ st.set_page_config(page_title="AI Medical Consultancy", layout="wide")
 # Custom CSS for styling
 st.markdown("""
     <style>
-    /* Color Variables */
-    :root {
-        --primary: #3498db;       /* Blue */
-        --secondary: #2c3e50;     /* Dark accent */
-        --accent: #f1c40f;        /* Yellow */
-        --success: #2ecc71;       /* Positive actions */
-        --light: #ffffff;         /* White backgrounds */
-        --dark: #000000;          /* Black text/elements */
-    }
-
     /* Main container styling */
     .stApp {
-        background: linear-gradient(135deg, #3498db 0%, #e0e0e0 100%);
+        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
         font-family: 'Arial', sans-serif;
     }
 
     /* Headers styling */
     h1, h2, h3 {
-        color: var(--dark) !important;
-        border-bottom: 3px solid var(--primary);
+        color: #2c3e50 !important;
+        border-bottom: 2px solid #3498db;
         padding-bottom: 0.3em;
     }
 
     /* Form containers */
     .stForm {
-        background: #000000;
-        border: 1px solid rgba(44, 62, 80, 0.2);
+        background: rgba(255, 255, 255, 0.9);
         border-radius: 15px;
         padding: 2rem;
-        box-shadow: 0 8px 30px rgba(0, 0, 0, 0.12);
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
         margin: 1rem 0;
     }
 
     /* Input fields */
-    .stTextInput input, .stNumberInput input, 
-    .stSelectbox select, .stTextArea textarea {
-        border: 2px solid #00FFFF !important;
-        border-radius: 10px !important;
-        padding: 1rem !important;
-        background: #00FFFF !important;
-        transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-        color: var(--dark) !important;
+    .stTextInput input, .stNumberInput input, .stSelectbox select, .stTextArea textarea {
+        border: 2px solid #3498db !important;
+        border-radius: 8px !important;
+        padding: 0.8rem !important;
+        transition: all 0.3s ease;
     }
 
     .stTextInput input:focus, .stNumberInput input:focus, 
     .stSelectbox select:focus, .stTextArea textarea:focus {
-        border-color: var(--primary) !important;
-        box-shadow: 0 0 12px rgba(52, 152, 219, 0.2) !important;
-        background: white !important;
-        color: var(--dark) !important;
+        border-color: #2980b9 !important;
+        box-shadow: 0 0 8px rgba(52, 152, 219, 0.3) !important;
     }
 
     /* Buttons styling */
     .stButton>button {
-        background: linear-gradient(135deg, var(--primary) 0%, var(--accent) 100%) !important;
-        color: var(--dark) !important;
+        background: linear-gradient(135deg, #3498db 0%, #2980b9 100%) !important;
+        color: white !important;
         border: none !important;
-        border-radius: 10px !important;
-        padding: 1rem 2rem !important;
+        border-radius: 8px !important;
+        padding: 0.8rem 1.5rem !important;
         font-size: 1rem !important;
-        transition: all 0.3s ease;
-        position: relative;
-        overflow: hidden;
+        transition: transform 0.2s ease;
     }
 
     .stButton>button:hover {
         transform: translateY(-2px);
-        box-shadow: 0 8px 15px rgba(52, 152, 219, 0.3);
-        opacity: 0.95;
-    }
-
-    .stButton>button:active {
-        transform: translateY(0);
-        opacity: 1;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
     }
 
     /* Progress indicator */
@@ -100,60 +69,39 @@ st.markdown("""
         padding: 1rem;
         background: rgba(255, 255, 255, 0.9);
         border-radius: 10px;
-        color: var(--dark) !important;
     }
 
     .step {
         flex: 1;
         text-align: center;
-        padding: 1rem;
-        font-weight: 600;
-        color: #95a5a6;
-        position: relative;
+        padding: 0.5rem;
+        font-weight: bold;
+        color: #7f8c8d;
     }
 
     .step.active {
-        color: var(--primary);
-    }
-
-    .step.active:after {
-        content: '';
-        position: absolute;
-        bottom: -1px;
-        left: 50%;
-        transform: translateX(-50%);
-        width: 40%;
-        height: 3px;
-        background: var(--primary);
+        color: #3498db;
+        border-bottom: 3px solid #3498db;
     }
 
     /* Chat bubbles */
     .dr-message {
-        background: linear-gradient(135deg, var(--primary) 0%, #2980b9 100%);
+        background: #3498db;
         color: white;
-        border-radius: 20px 20px 20px 4px;
-        padding: 1.2rem 1.5rem;
+        border-radius: 15px;
+        padding: 1rem;
         margin: 1rem 0;
         max-width: 80%;
         width: fit-content;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-    }
-
-    .user-message {
-        background: linear-gradient(135deg, #f1c40f 0%, #e1b800 100%);
-        margin-left: auto;
-        border-radius: 20px 20px 4px 20px;
-        color: var(--dark) !important;
     }
 
     /* Emergency alert */
     .emergency-alert {
-        background: linear-gradient(135deg, var(--accent) 0%, #c0392b 100%);
+        background: #e74c3c;
         color: white;
         padding: 2rem;
         border-radius: 15px;
         animation: pulse 1.5s infinite;
-        text-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);
     }
 
     @keyframes pulse {
@@ -164,43 +112,11 @@ st.markdown("""
 
     /* Download button */
     .download-btn {
-        background: linear-gradient(135deg, var(--success) 0%, #27ae60 100%) !important;
-    }
-
-    /* Enhanced Data Visualization Contrast */
-    .stDataFrame {
-        border: 1px solid rgba(0, 0, 0, 0.1);
-        border-radius: 12px;
-        overflow: hidden;
-        background: #f0f0f0;
-        color: var(--dark) !important;
-    }
-
-    /* Tabbed Interface Styling */
-    .stTabs [role="tablist"] {
-        gap: 10px;
-        padding: 8px;
-        background: rgba(240, 240, 240, 0.9);
-        border-radius: 12px;
-        color: var(--dark) !important;
-    }
-
-    .stTabs [role="tab"] {
-        background: #ffffff !important;
-        border-radius: 8px !important;
-        transition: all 0.3s ease;
-        color: var(--dark) !important;
-    }
-
-    .stTabs [role="tab"][aria-selected="true"] {
-        background: var(--primary) !important;
-        color: white !important;
-        transform: scale(1.05);
+        background: linear-gradient(135deg, #2ecc71 0%, #27ae60 100%) !important;
     }
     </style>
 """, unsafe_allow_html=True)
 
-
 # Initialize session state variables
 if 'current_step' not in st.session_state:
     st.session_state.current_step = 0
@@ -209,96 +125,6 @@ if 'symptom_details' not in st.session_state:
 if 'patient_info' not in st.session_state:
     st.session_state.patient_info = {}
 
-# Initialize models and dataset with caching
-@st.cache_resource
-def load_ner_model():
-    return pipeline("token-classification", 
-                    model="d4data/biomedical-ner-all",
-                    aggregation_strategy='first',
-                    device=-1)
-
-@st.cache_resource
-def load_dataset():
-    try:
-        data = pd.read_csv('DATASET.csv', encoding='latin1')
-        data['Symptom_lower'] = data['Symptom'].str.strip().str.lower()
-        return data
-    except FileNotFoundError:
-        st.error("DATASET.csv not found. Please ensure the file is in the correct directory.")
-        return None
-
-pipe = load_ner_model()
-data = load_dataset()
-if data is None:
-    st.stop()
-
-SEVERITY_KEYWORDS = {
-    'normal': ['mild', 'occasional', 'controlled', 'temporary', 'fleeting'],
-    'moderate': ['moderate', 'persistent', 'frequent', 'prolonged', 'bloating'],
-    'severe': ['severe', 'extreme', 'crushing', 'radiating', 'blood', 'inability', 
-              'sweating', 'vomiting', 'fever', 'swelling', 'radiates', 'persistent >3 days']
-}
-
-def merge_entities(entities, text):
-    """Merge entities using dataset symptom names for multi-word matching"""
-    merged = []
-    detected = set()
-    text_lower = text.lower()
-    
-    symptoms_sorted = data['Symptom'].str.lower().sort_values(
-        key=lambda x: x.str.len(), ascending=False).tolist()
-    
-    for symptom in symptoms_sorted:
-        if symptom in text_lower and symptom not in detected:
-            start = text_lower.find(symptom)
-            end = start + len(symptom)
-            merged.append({
-                'word': text[start:end],
-                'start': start,
-                'end': end,
-                'entity_group': 'Sign_symptom'
-            })
-            detected.add(symptom)
-    return merged
-
-def determine_condition(text, entity):
-    context_window = re.findall(r'\w+', text[max(0, entity['start']-20):entity['end']+20])
-    context = ' '.join(context_window).lower()
-    
-    for level, keywords in SEVERITY_KEYWORDS.items():
-        if any(k in context for k in keywords):
-            return level.capitalize()
-    return 'Normal'
-
-def get_risk_score(symptom, condition):
-    symptom_clean = symptom.strip().lower()
-    matches = data[
-        data['Symptom_lower'].str.contains(rf'\b{symptom_clean}\b', regex=True, flags=re.IGNORECASE)
-    ]
-    if not matches.empty:
-        matches = matches[matches['Condition'] == condition]
-        return matches['Risk Score'].values[0] if not matches.empty else 0
-    return 0
-
-def calculate_risk_score(text):
-    entities = pipe(text)
-    merged_entities = merge_entities(entities, text)
-
-    score = 0
-    breakdown = []
-    for ent in merged_entities:
-        if ent['entity_group'] == "Sign_symptom":
-            symptom = ent['word'].strip().lower()
-            condition = determine_condition(text, ent)
-            risk = get_risk_score(symptom, condition)
-            score += risk
-            breakdown.append({
-                'symptom': symptom.capitalize(),
-                'condition': condition,
-                'risk': risk
-            })
-    return score, breakdown
-
 def initialize_groq_client():
     try:
         # Try to get the API key from Streamlit secrets
@@ -306,7 +132,7 @@ def initialize_groq_client():
         try:
             api_key = st.secrets.get("GROQ_API_KEY", os.getenv("GROQ_API_KEY"))
         except FileNotFoundError:
-            st.warning("No secrets.toml file found. Please create one in the .streamlit folder.")
+            st.warning("No `secrets.toml` file found. Please create one in the `.streamlit` folder.")
         
         # If not found, prompt the user to enter it
         if not api_key:
@@ -343,9 +169,7 @@ def symptom_interrogation_step():
         focusing on {focus} to differentiate serious causes. Your task is to have a polite and simple conversation with a patient. 
         Start by asking ONE specific follow-up question about their initial symptom: {main_symptom}.
         Ask only one question at a time to avoid overwhelming the patient.
-        Keep your language clear, professional, and easy to understand. 
-        
-        Dont display possibe symptoms or why you are asking questions."""
+        Keep your language clear, professional, and easy to understand."""
         
         messages = [
             {"role": "system", "content": "Ask focused clinical questions. One at a time."},
@@ -382,7 +206,7 @@ def handle_symptom_interrogation():
     
     if 'current_question' in st.session_state:
         with st.form("symptom_qna"):
-            st.markdown(f'<div class="dr-message">👨‍⚕ {st.session_state.current_question}</div>', unsafe_allow_html=True)
+            st.markdown(f'<div class="dr-message">👨‍⚕️ {st.session_state.current_question}</div>', unsafe_allow_html=True)
             answer = st.text_input("Your answer:", key=f"answer_{len(st.session_state.symptom_details)}")
             
             if st.form_submit_button("Next"):
@@ -458,41 +282,40 @@ def generate_risk_assessment():
         )
 
         patient_profile = f"""
-*Patient Profile*
+**Patient Profile**
 Name: {st.session_state.patient_info['name']}
 Age: {st.session_state.patient_info['age']}
 Gender: {st.session_state.patient_info['gender']}
 
-*Primary Complaint*
+**Primary Complaint**
 {st.session_state.patient_info['main_symptom']}
 
-*Symptom Interrogation*
+**Symptom Interrogation**
 {symptom_log}
 
-*Medical History*
+**Medical History**
 {st.session_state.patient_info.get('medical_history', 'None reported')}
 
-*Current Medications*
+**Current Medications**
 {st.session_state.patient_info.get('medications', 'None')}
 
-*Allergies*
+**Allergies**
 {st.session_state.patient_info.get('allergies', 'None reported')}
 
-*Recent Context*
+**Recent Context**
 Last Meal: {st.session_state.patient_info.get('last_meal', 'Unknown')}
 Recent Travel: {st.session_state.patient_info.get('recent_travel', 'None')}
         """
 
         analysis_prompt = f"""STRICTLY follow these instructions:
 1. Analyze this case: {patient_profile}
-2. *Include ONLY symptoms the patient is actively experiencing*. Exclude all negated symptoms (e.g., "no fever," "denies breathlessness").
-3. Output *EXCLUSIVELY* in this format with NO additional text or explanations:
-[Age]-year-old [gender] with [specific, present symptoms].
+2. Output ONLY this exact format WITHOUT ANY additional text:
+[Age]-year-old [gender] with [symptom details]
 
 Example Output:
 "45-year-old man with severe chest pain radiating to the jaw"
 
-Your Output:"""
+Your Output MUST BE:"""
 
         response = st.session_state.client.chat.completions.create(
             messages=[
@@ -507,42 +330,11 @@ Your Output:"""
         risk_prompt = response.choices[0].message.content.strip('"')
         
         st.subheader("Clinical Summary")
-        st.markdown(f"\n{risk_prompt}\n")
+        st.markdown(f"```\n{risk_prompt}\n```")
         
-        # Add risk scoring
-        risk_score, breakdown = calculate_risk_score(risk_prompt)
-        
-        # Display risk score with color coding
-        score_color = "#2ecc71" if risk_score < 40 else "#f1c40f" if risk_score < 70 else "#e74c3c"
-        st.markdown(f"""
-            <div style="background: {score_color}; 
-                        color: white; 
-                        padding: 1.5rem; 
-                        border-radius: 10px;
-                        text-align: center;
-                        margin: 2rem 0;">
-                <h2 style="color: white; margin: 0;">Risk Assessment Score</h2>
-                <h1 style="font-size: 3.5rem; margin: 0.5rem 0;">{risk_score}/100</h1>
-                <p>{'Low Risk' if risk_score < 40 else 'Moderate Risk' if risk_score < 70 else 'High Risk'}</p>
-            </div>
-        """, unsafe_allow_html=True)
-
-        # Display breakdown
-        with st.expander("View Detailed Risk Breakdown", expanded=True):
-            for item in breakdown:
-                st.markdown(f"""
-                    <div style="background: rgba(236, 240, 241, 0.5);
-                                padding: 1rem;
-                                border-radius: 8px;
-                                margin: 0.5rem 0;">
-                        <strong>{item['symptom']}</strong> ({item['condition']})
-                        <div style="float: right;">+{item['risk']} pts</div>
-                    </div>
-                """, unsafe_allow_html=True)
-
         # Create download button
         timestamp = datetime.now().strftime('%Y%m%d%H%M')
-        filename = f"{st.session_state.patient_info['name'].replace(' ', '')}_assessment{timestamp}.txt"
+        filename = f"{st.session_state.patient_info['name'].replace(' ', '_')}_assessment_{timestamp}.txt"
         st.download_button(
             label="Download Assessment",
             data=risk_prompt,
@@ -592,5 +384,5 @@ def main():
     if current_step in steps:
         steps[current_step]()
 
-if __name__ == "_main_":
+if __name__ == "__main__":
     main()