Delete app.py

app.py

@@ -1,275 +0,0 @@
-import gradio as gr
-import json
-from collections import defaultdict
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
-from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-from reportlab.lib import colors
-from reportlab.lib.enums import TA_RIGHT
-from reportlab.pdfbase import pdfmetrics
-from reportlab.pdfbase.ttfonts import TTFont
-from reportlab.lib.units import inch
-import difflib
-
-# -------------------------------------------------------
-# بارگذاری داده‌ها
-# -------------------------------------------------------
-def load_guidelines(json_file_path):
-    try:
-        with open(json_file_path, 'r', encoding='utf-8') as file:
-            data = json.load(file)
-        return data['guidelines']
-    except Exception as e:
-        print(f"Error loading JSON file: {e}")
-        return []
-
-guidelines = load_guidelines("enriched_guidelines_with_lab_findings_completed.json")
-
-# -------------------------------------------------------
-# توابع تحلیل
-# -------------------------------------------------------
-def analyze_symptoms(symptoms_input, guidelines):
-    symptoms_input = [s.lower().strip() for s in symptoms_input.split(',') if s.strip()]
-    matched_diseases = defaultdict(lambda: {'score': 0, 'matched_symptoms': [], 'alarm_matched': False, 'full_guideline_data': None})
-
-    key_symptoms_weights = {
-        'signs of liver decompensation': 2.0,
-        'bloody diarrhea': 1.5,
-        'fever (≥38°c)': 1.3,
-        'abdominal pain (sudden or progressive)': 1.2,
-        'abdominal distension (ascites)': 1.2,
-        'back pain': 1.1,
-        'dyspepsia': 1.0,
-        'asymptomatic (incidental finding)': 0.8
-    }
-
-    for guideline in guidelines:
-        disease_name = guideline.get('condition_name', 'Unknown')
-        icd_code = guideline.get('icd_code', 'N/A')
-        guideline_symptoms = [s.lower().strip() for s in guideline.get('symptoms', [])]
-        alarm_features = guideline.get('alarm_features', {})
-
-        matched_symptoms = [s for s in symptoms_input if s in guideline_symptoms]
-        if matched_symptoms:
-            score = sum([key_symptoms_weights.get(symptom, 1.0) for symptom in matched_symptoms])
-            is_alarm_matched = any(
-                symptom.lower() in str(alarm_features.values()).lower()
-                for symptom in matched_symptoms
-            )
-            matched_diseases[disease_name] = {
-                'score': score,
-                'icd_code': icd_code,
-                'matched_symptoms': matched_symptoms,
-                'alarm_matched': is_alarm_matched,
-                'full_guideline_data': guideline
-            }
-
-    results = []
-    total_score = sum(d['score'] for d in matched_diseases.values())
-
-    if total_score == 0:
-        return []
-
-    for disease_name, data in matched_diseases.items():
-        probability = (data['score'] / total_score) * 100
-        if data['alarm_matched']:
-            probability *= 1.5
-        probability = max(min(probability, 100), 5)
-
-        results.append({
-            'disease_name': disease_name,
-            'icd_code': data['icd_code'],
-            'probability': round(probability, 2),
-            'matched_symptoms': ', '.join(data['matched_symptoms']),
-            'full_data': data['full_guideline_data']
-        })
-
-    results.sort(key=lambda x: x['probability'], reverse=True)
-    return results
-
-
-# -------------------------------------------------------
-# تحلیل آزمایش‌ها
-# -------------------------------------------------------
-synonyms = {
-    "ast": ["ast", "sgot", "aspartate aminotransferase"],
-    "alt": ["alt", "sgpt", "alanine aminotransferase"],
-    "serum glucose": ["serum glucose", "glucose", "blood sugar"],
-    "bilirubin": ["bilirubin", "total bilirubin"],
-    "alkaline phosphatase": ["alkaline phosphatase", "alp"],
-    "albumin": ["albumin", "serum albumin"],
-    "prothrombin time": ["prothrombin time", "pt", "protime"],
-    "creatinine": ["creatinine", "serum creatinine"],
-    "platelets": ["platelets", "plts"],
-    "hemoglobin": ["hemoglobin", "hgb"],
-    "wbc": ["wbc", "white blood cell count"]
-}
-
-def normalize_test_name(name):
-    name = name.lower().strip()
-    for key, syns in synonyms.items():
-        if name in syns:
-            return key
-    for key in synonyms.keys():
-        if difflib.get_close_matches(name, synonyms[key], cutoff=0.7):
-            return key
-    return name
-
-def check_condition(user_value, expected):
-    try:
-        user_val = float(user_value)
-        if expected.startswith("<="):
-            return user_val <= float(expected[2:])
-        elif expected.startswith(">="):
-            return user_val >= float(expected[2:])
-        elif expected.startswith("<"):
-            return user_val < float(expected[1:])
-        elif expected.startswith(">"):
-            return user_val > float(expected[1:])
-        else:
-            return str(user_val) == expected
-    except:
-        return expected.lower() in user_value.lower() or user_value.lower() in expected.lower()
-
-def analyze_lab_results(lab_results_text, guidelines):
-    lab_results_input = {}
-    for line in lab_results_text.split('\n'):
-        if ':' in line:
-            key, value = line.split(':', 1)
-            norm_key = normalize_test_name(key)
-            lab_results_input[norm_key] = value.strip()
-
-    matched_diseases = []
-    for guideline in guidelines:
-        disease_name = guideline.get('condition_name', 'Unknown')
-        icd_code = guideline.get('icd_code', 'N/A')
-        lab_findings = guideline.get('lab_findings', {})
-
-        matches = []
-        for lab_test, criteria in lab_findings.items():
-            norm_test = normalize_test_name(lab_test)
-            if norm_test in lab_results_input:
-                user_value = lab_results_input[norm_test]
-                expected = str(criteria.get('expected', '')).strip()
-                meaning = criteria.get('meaning', '')
-                if check_condition(user_value, expected):
-                    matches.append({
-                        'test': lab_test,
-                        'user_value': user_value,
-                        'expected': expected,
-                        'meaning': meaning
-                    })
-
-        if matches:
-            matched_diseases.append({
-                'disease_name': disease_name,
-                'icd_code': icd_code,
-                'lab_matches': matches,
-                'full_data': guideline
-            })
-
-    return matched_diseases
-
-
-# -------------------------------------------------------
-# ترکیب نتایج و رابط کاربری Gradio
-# -------------------------------------------------------
-def combined_analysis_for_ui(symptoms, lab_results_text):
-    symptom_results = analyze_symptoms(symptoms, guidelines)
-    lab_results = analyze_lab_results(lab_results_text, guidelines)
-
-    if not symptom_results and not lab_results:
-        return [], "❌ هیچ بیماری مرتبطی یافت نشد.", [], []
-
-    symptom_table_data = []
-    for r in symptom_results:
-        symptom_table_data.append([r['disease_name'], r['icd_code'], f"{r['probability']}%", r['matched_symptoms']])
-
-    lab_results_html = ""
-    if lab_results:
-        lab_results_html += "<h4>🧪 تحلیل بر اساس آزمایش‌ها:</h4>"
-        for lab in lab_results:
-            lab_results_html += f"<b>{lab['disease_name']}</b> (ICD: {lab['icd_code']})<br>"
-            for match in lab['lab_matches']:
-                lab_results_html += f" - <b>{match['test']}</b>: {match['user_value']} (انتظار: {match['expected']}) → {match['meaning']}<br>"
-            lab_results_html += "<br>"
-
-    return symptom_table_data, lab_results_html, symptom_results, lab_results
-
-
-# -------------------------------------------------------
-# ساخت رابط Gradio
-# -------------------------------------------------------
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("<h2 style='text-align:center;color:#2E86C1;'>💡 سیستم هوشمند تحلیل علائم و آزمایش‌ها</h2>")
-
-    with gr.Row():
-        symptoms_input = gr.Textbox(label="✅ علائم (با کاما جدا کنید)")
-        lab_results_input = gr.Textbox(label="🧪 نتایج آزمایش (هر خط: نام: مقدار)")
-
-    analyze_button = gr.Button("🔍 تحلیل کن")
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            symptom_table_output = gr.Dataframe(headers=["بیماری", "ICD", "احتمال", "علائم منطبق"], interactive=False)
-            lab_results_output = gr.HTML()
-
-        with gr.Column(scale=3):
-            detailed_output = gr.HTML()
-
-    symptom_results_state = gr.State()
-    lab_results_state = gr.State()
-
-    analyze_button.click(
-        fn=combined_analysis_for_ui,
-        inputs=[symptoms_input, lab_results_input],
-        outputs=[symptom_table_output, lab_results_output, symptom_results_state, lab_results_state]
-    )
-
-# -------------------------------------------------------
-# تابع نمایش جزئیات هر بیماری
-# -------------------------------------------------------
-def show_details(evt: gr.SelectData, symptom_results):
-    if evt is None or evt.index is None:
-        return "هیچ بیماری انتخاب نشده است."
-
-    # evt.index معمولاً اندیس ردیف است
-    selected_row = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index
-    if selected_row < 0 or selected_row >= len(symptom_results):
-        return "بیماری انتخاب شده معتبر نیست."
-
-    disease = symptom_results[selected_row]["full_data"]
-    html = f"<h4>{disease.get('condition_name','Unknown')}</h4>"
-
-    if disease.get('diagnosis_criteria'):
-        html += "<b>معیارهای تشخیصی:</b><ul>"
-        for item in disease['diagnosis_criteria']:
-            html += f"<li>{item}</li>"
-        html += "</ul>"
-
-    if disease.get('alarm_features'):
-        html += "<b>علائم هشدار:</b><ul>"
-        for k, v in disease['alarm_features'].items():
-            html += f"<li><b>{k}</b>: {v}</li>"
-        html += "</ul>"
-
-    if disease.get('first_line_treatment'):
-        html += "<b>درمان خط اول:</b><ul>"
-        for t in disease['first_line_treatment']:
-            html += f"<li>{t}</li>"
-        html += "</ul>"
-
-    return html
-
-
-# -------------------------------------------------------
-# اتصال رویداد انتخاب جدول به تابع جزئیات
-# -------------------------------------------------------
-symptom_table_output.select(
-    fn=show_details,
-    inputs=[symptom_results_state],
-    outputs=detailed_output
-)
-
-
-if __name__ == "__main__":
-    demo.launch()