Upload app.py

app.py

@@ -0,0 +1,271 @@
+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
+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")
+
+# -------------------------------------------------------
+# تحلیل علائم
+# -------------------------------------------------------
+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 برای UI
+# -------------------------------------------------------
+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
+
+# -------------------------------------------------------
+# نمایش جزئیات هنگام کلیک روی بیماری
+# -------------------------------------------------------
+def show_details(evt: gr.SelectData, symptom_results):
+    if evt is None or evt.index is None:
+        return "هیچ بیماری انتخاب نشده 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 style='color:#2E86C1;'>{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>"
+
+    if disease.get('second_line_treatment'):
+        html += "<b>درمان خط دوم:</b><ul>"
+        for t in disease['second_line_treatment']:
+            html += f"<li>{t}</li>"
+        html += "</ul>"
+
+    return html
+
+# -------------------------------------------------------
+# 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]
+    )
+
+    symptom_table_output.select(
+        fn=show_details,
+        inputs=[symptom_results_state],
+        outputs=detailed_output
+    )
+
+if __name__ == "__main__":
+    demo.launch()