Update app.py

app.py

@@ -4,6 +4,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 from sentence_transformers import SentenceTransformer
 from sklearn.decomposition import PCA
 import plotly.graph_objects as go
+import re
 
 # مدل زبانی
 model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
@@ -39,50 +40,66 @@ school_profiles = {
     "Empiricism": {"timeline": "17th – 18th century", "profile": "Practical, Observational, Experimental"}
 }
 
+psychological_categories = [
+    {
+        "name": "Moral guilt",
+        "keywords": [
+            "guilt", "remorse", "regret", "پشیمانی", "عذاب وجدان", "ندامت"
+        ],
+        "followed": "Helps in personal growth, emotional release, and reconciliation.",
+        "ignored": "Can lead to chronic anxiety, regret, or depression."
+    },
+    {
+        "name": "Freedom & Will",
+        "keywords": [
+            "freedom", "free will", "liberty", "independence", "آزادی", "اراده", "خودمختاری"
+        ],
+        "followed": "Encourages autonomy and a sense of responsibility.",
+        "ignored": "May result in feelings of helplessness or existential crisis."
+    },
+    {
+        "name": "Altruism",
+        "keywords": [
+            "altruism", "help", "helping", "helped", "assist", "support", "others",
+            "نوع‌دوستی", "کمک", "یاری", "حمایت"
+        ],
+        "followed": "Improves social bonding and personal satisfaction.",
+        "ignored": "May cause loneliness or self-centered thinking."
+    },
+    {
+        "name": "Anxiety & Fear",
+        "keywords": [
+            "anxiety", "fear", "worry", "stress", "ترس", "اضطراب", "استرس", "نگرانی"
+        ],
+        "followed": "Leads to caution and preparedness.",
+        "ignored": "Might cause mental blocks, phobia, or stress disorders."
+    },
+    {
+        "name": "Rationality",
+        "keywords": [
+            "reason", "logic", "rational", "عقل", "منطق", "استدلال", "عقلانی"
+        ],
+        "followed": "Supports wise decision-making and clarity.",
+        "ignored": "Can result in poor judgments and emotional impulses."
+    }
+]
+
 def psychological_analysis(text):
-    rules = [
-        {
-            "keywords": ["guilt", "remorse"],
-            "aspect": "Moral guilt",
-            "followed": "Helps in personal growth, emotional release, and reconciliation.",
-            "ignored": "Can lead to chronic anxiety, regret, or depression."
-        },
-        {
-            "keywords": ["freedom", "free will"],
-            "aspect": "Freedom & Will",
-            "followed": "Encourages autonomy and a sense of responsibility.",
-            "ignored": "May result in feelings of helplessness or existential crisis."
-        },
-        {
-            "keywords": ["altruism", "help", "others"],
-            "aspect": "Altruism",
-            "followed": "Improves social bonding and personal satisfaction.",
-            "ignored": "May cause loneliness or self-centered thinking."
-        },
-        {
-            "keywords": ["anxiety", "fear"],
-            "aspect": "Anxiety & Fear",
-            "followed": "Leads to caution and preparedness.",
-            "ignored": "Might cause mental blocks, phobia, or stress disorders."
-        },
-        {
-            "keywords": ["reason", "logic"],
-            "aspect": "Rationality",
-            "followed": "Supports wise decision-making and clarity.",
-            "ignored": "Can result in poor judgments and emotional impulses."
-        }
-    ]
-    
+    text_lower = text.lower()
     results = []
-    lower_text = text.lower()
-    for rule in rules:
-        if any(keyword in lower_text for keyword in rule["keywords"]):
-            results.append({
-                "aspect": rule["aspect"],
-                "followed": rule["followed"],
-                "ignored": rule["ignored"]
-            })
-    return results
+    for category in psychological_categories:
+        for keyword in category["keywords"]:
+            if re.search(rf"\b{re.escape(keyword.lower())}\b", text_lower):
+                results.append(category)
+                break
+
+    if not results:
+        return "No clear psychological indicators found."
+
+    output = ""
+    for item in results:
+        output += f"🧠 {item['name']}\n✅ If Followed: {item['followed']}\n❌ If Ignored: {item['ignored']}\n\n"
+    return output
 
 def create_semantic_plot(user_vec, best_school):
     ref_quotes = school_data[best_school]
@@ -140,13 +157,7 @@ def analyze_text(text):
     school_profile = school_profiles[best_school]["profile"]
     timeline = school_profiles[best_school]["timeline"]
 
-    psych_results = psychological_analysis(text)
-    psych_output = ""
-    if psych_results:
-        for item in psych_results:
-            psych_output += f"🧠 {item['aspect']}\n✅ If Followed: {item['followed']}\n❌ If Ignored: {item['ignored']}\n\n"
-    else:
-        psych_output = "No clear psychological indicators found."
+    psych_output = psychological_analysis(text)
 
     return best_school, f"{best_score:.2f}", school_profile, timeline, best_match, semantic_plot, psych_output
 
@@ -184,13 +195,13 @@ with gr.Blocks(title="Philosophical Analyzer") as demo:
         fn=analyze_text,
         inputs=input_text,
         outputs=[
-            school,        # best_school
-            score,         # similarity score
-            profile_box,   # school_profile
-            timeline,      # timeline
-            best_quote,    # best_match
-            conceptual_map,# semantic_plot
-            psych_box      # psych_output
+            school,
+            score,
+            profile_box,
+            timeline,
+            best_quote,
+            conceptual_map,
+            psych_box
         ]
     )