Update app.py

app.py

@@ -3,7 +3,7 @@ from huggingface_hub import InferenceClient
 import random
 import re
 
-# ✅ Allowed mental health keywords (EN + AR + transliterated Arabic)
+# ✅ Expanded mental health keywords to include common disorders (fixes the OCD bug)
 MENTAL_KEYWORDS = [
     # English
     "depression", "depressed", "anxiety", "anxious", "panic", "stress", "sad", "lonely",
@@ -12,6 +12,8 @@ MENTAL_KEYWORDS = [
     "relax", "calm", "sleep", "emotion", "feeling", "feel", "thoughts", "help", "life", "advice",
     "unmotivated", "lost", "hopeless", "tired", "burnout", "cry", "hurt", "love", "breakup",
     "friend", "family", "alone", "heartbroken", "scared", "fearful",
+    # Specific Disorders & Mental Health Terms
+    "ocd", "adhd", "ptsd", "bipolar", "disorder", "eating disorder", "schizophrenia", "borderline",
     # Transliterated Arabic
     "ana", "zahqan", "daye2", "ha2t", "mota3ab", "mota3eb", "za3lan", "malo", "khalni", "mash3or",
     "bakhaf", "w7ed", "msh 3aref", "mash fahem", "malish", "3ayez", "ayez", "7azeen", "mdaye2",
@@ -20,55 +22,38 @@ MENTAL_KEYWORDS = [
     "مشاعر", "مضغوط", "قلقان", "وحدي", "مش مبسوط", "زهقان", "ضايق", "تعب", "مش مرتاح",
 ]
 
-# ✅ Off-topic keywords (EN + AR)
+# ✅ Off-topic keywords
 OFF_TOPIC = [
-    # English
     "recipe", "song", "music", "lyrics", "joke", "funny", "laugh", "code", "python", "program",
     "game", "food", "cook", "movie", "film", "series", "sport", "football", "instagram",
     "tiktok", "money", "business", "crypto", "ai", "computer",
-    # Arabic
     "نكتة", "ضحك", "اغنية", "اغاني", "طبخ", "اكل", "فيلم", "مسلسل", "كورة", "رياضة",
     "بيزنس", "فلوس", "العاب", "لعبة", "كود", "برمجة", "ذكاء اصطناعي"
 ]
 
-# ✅ Random natural off-topic responses
 OFF_TOPIC_RESPONSES = [
-    "I'm here to help with emotional and mental well-being. Let's focus on how you're feeling, coping, or managing your emotions today.",
-    "I specialize in mental and emotional health conversations. Tell me what’s been on your mind lately.",
-    "Let’s bring it back to how you’ve been feeling — I’m here to help you talk through emotions, stress, or challenges.",
-    "My goal is to support your mental health. How have things been emotionally for you lately?",
-    "I’m here for emotional and mental support only. What’s been bothering you recently?",
-    "Let's focus on your thoughts and feelings — I can help you process or manage them better.",
-    "It sounds like you might be going off-topic. Can we talk about how you’ve been feeling instead?",
-    "Let’s keep this space focused on your emotions and well-being. What’s been heavy on your mind lately?",
+    "I'm here to help with emotional and mental well-being. Let's focus on how you're feeling today.",
+    "I specialize in mental and emotional health. What’s been on your mind lately?",
+    "Let’s bring it back to your well-being — I’m here to help you talk through emotions.",
+    "My goal is to support your mental health. How have things been emotionally for you?",
 ]
 
-# ✅ Detect Arabic characters
 def contains_arabic(text: str) -> bool:
     return bool(re.search(r"[\u0600-\u06FF]", text))
 
-# ✅ Function to check if input is related to mental health
 def is_mental_health_related(text: str) -> bool:
     text_lower = text.lower()
-    has_arabic = contains_arabic(text_lower)
-
-    # If message includes off-topic Arabic or English terms → block it
+    # Check off-topic first
     if any(word in text_lower for word in OFF_TOPIC):
         return False
-
-    # If it has mental-related Arabic/English → allow
+    # Check keywords
     if any(word in text_lower for word in MENTAL_KEYWORDS):
         return True
-
-    # If purely Arabic but not off-topic → assume emotional (allow)
-    if has_arabic:
+    # Default to true if Arabic characters are present to avoid blocking natural dialect
+    if contains_arabic(text_lower):
         return True
-
-    # Default fallback
     return False
 
-
-# ✅ Main response function
 def respond(
     message,
     history: list[dict[str, str]],
@@ -78,61 +63,57 @@ def respond(
     top_p,
     hf_token: gr.OAuthToken,
 ):
+    # 1. Check if input is blocked by local logic
     if not is_mental_health_related(message):
         yield random.choice(OFF_TOPIC_RESPONSES)
         return
 
-    locked_system_message = (
-        "You are a licensed mental health therapy assistant. "
-        "You respond with empathy, emotional intelligence, and a therapeutic tone. "
-        "Never answer questions unrelated to emotional or mental wellness, even if they are in another language."
-    )
+    # 2. Connect to model
+    if not hf_token:
+        yield "Please log in to use the chatbot."
+        return
 
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
 
-    messages = [{"role": "system", "content": locked_system_message}]
+    # 3. Build message list (Using the UI's system message)
+    messages = [{"role": "system", "content": system_message}]
     messages.extend(history)
     messages.append({"role": "user", "content": message})
 
+    # 4. Stream response
     response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-        response += token
-        yield response
-
-
-# ✅ Gradio interface setup
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
+    try:
+        for msg in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = msg.choices[0].delta.content or ""
+            response += token
+            yield response
+    except Exception as e:
+        yield f"An error occurred: {str(e)}"
+
+# ✅ Gradio Interface Setup
 with gr.Blocks() as demo:
     with gr.Sidebar():
         gr.LoginButton()
-    chatbot.render()
+    
+    chatbot = gr.ChatInterface(
+        respond,
+        type="messages",
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a compassionate mental health assistant. Prioritize the user's specific concern in your first sentence. Do not repeat a generic intro if the user shares a problem. Always validate feelings.",
+                label="System message"
+            ),
+            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p"),
+        ],
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()