Update app.py

app.py

@@ -3,27 +3,18 @@ from huggingface_hub import InferenceClient
 import random
 import re
 
-# ✅ Keywords & Logic
-MENTAL_KEYWORDS = [
-    "ocd", "adhd", "ptsd", "bipolar", "disorder", "anxiety", "depressed", "suicide",
-    "panic", "stress", "lonely", "trauma", "mental", "therapy", "mood", "overwhelmed",
-    "ana", "zahqan", "daye2", "mota3ab", "za3lan", "حزين", "تعبان", "قلق", "خايف"
-]
+# ✅ Smart Detection: Only blocks obvious garbage (ads/links/spam), everything else goes to AI
+OFF_TOPIC_REGEX = r"(http|www|buy now|discount|subscribe|follow me|click here)"
 
-OFF_TOPIC = ["recipe", "code", "python", "crypto", "football", "نكتة", "طبخ", "كورة"]
+def is_safe_to_process(text: str) -> bool:
+    # If it's a very short message with no meaning, we use a friendly nudge
+    if len(text.strip()) < 2:
+        return False
+    # If it matches spam patterns
+    if re.search(OFF_TOPIC_REGEX, text.lower()):
+        return False
+    return True
 
-OFF_TOPIC_RESPONSES = [
-    "I'm here to focus on your emotional well-being. How are you feeling today?",
-    "Let’s talk about what’s on your mind emotionally.",
-]
-
-def is_mental_health_related(text: str) -> bool:
-    text_lower = text.lower()
-    if any(word in text_lower for word in OFF_TOPIC): return False
-    if any(word in text_lower for word in MENTAL_KEYWORDS): return True
-    return bool(re.search(r"[\u0600-\u06FF]", text_lower))
-
-# ✅ The "Perfect" Respond Function
 def respond(
     message,
     history: list[dict[str, str]],
@@ -33,35 +24,34 @@ def respond(
     top_p,
     hf_token: gr.OAuthToken,
 ):
-    if not is_mental_health_related(message):
-        yield random.choice(OFF_TOPIC_RESPONSES)
+    # 1. Basic Safety Check
+    if not is_safe_to_process(message):
+        yield "I'm here to listen and support your emotional well-being. How can I help you today?"
         return
 
     if not hf_token:
-        yield "Please log in via the Sidebar."
+        yield "Please log in via the Sidebar to start our session."
         return
 
+    # 2. Using Zephyr-7B: Faster, smarter, and doesn't 'hang' on free tier
     client = InferenceClient(token=hf_token.token, model="HuggingFaceH4/zephyr-7b-beta")
 
-    # 1. SLIDING WINDOW MEMORY: Only take last 6 messages to prevent "infinite loops"
-    trimmed_history = history[-6:] 
-    
+    # 3. Memory Management: Last 8 messages to keep the context sharp but fast
     messages = [{"role": "system", "content": system_message}]
-    messages.extend(trimmed_history)
+    messages.extend(history[-8:])
     messages.append({"role": "user", "content": message})
 
     response = ""
     try:
-        # 2. ADVANCED PARAMETERS: Adding repetition penalty to the extra_body
+        # 4. Perfect Parameters for ChatGPT-like flow
         for msg in client.chat_completion(
             messages,
             max_tokens=max_tokens,
             stream=True,
-            temperature=max(temperature, 0.1), # Avoid 0
-            top_p=top_p,
-            stop=["User:", "Assistant:"], # Prevents model from talking to itself
+            temperature=0.7, # The "Sweet Spot"
+            top_p=0.9,
             extra_body={
-                "repetition_penalty": 1.15,
+                "repetition_penalty": 1.15, # Stops the "I specialize in..." loop
                 "presence_penalty": 0.3
             }
         ):
@@ -69,31 +59,37 @@ def respond(
             response += token
             yield response
     except Exception as e:
-        yield f"API Error: {str(e)}. Try refreshing or checking your HF token."
+        yield f"Connection error: {str(e)}. Please try refreshing the page."
 
-# ✅ Optimized Interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+# ✅ Professional UI Setup
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
     with gr.Sidebar():
-        gr.Markdown("### 🛠 Settings")
+        gr.Markdown("## 🌿 Therapy Assistant Settings")
         gr.LoginButton()
         gr.Markdown("---")
-        st_msg = gr.Textbox(
-            value="You are a compassionate mental health assistant. Respond to the user's specific problem IMMEDIATELY. No generic intros. Tone: Empathetic & Short.",
-            label="System Prompt",
-            lines=4
+        sys_msg = gr.Textbox(
+            value=(
+                "You are a professional mental health assistant. "
+                "1. Respond directly to the user's specific problem in the first sentence. "
+                "2. Be empathetic but professional. "
+                "3. If the user mentions symptoms (like washing hands), explain them gently as a supportive peer. "
+                "4. Never use a repetitive introductory phrase."
+            ),
+            label="System Persona",
+            lines=6
         )
-        tokens = gr.Slider(128, 1024, value=512, label="Max Tokens")
-        temp = gr.Slider(0.1, 1.5, value=0.7, label="Temperature (Randomness)")
-        top_p_slider = gr.Slider(0.1, 1.0, value=0.9, label="Top-p (Focus)")
+        tokens = gr.Slider(128, 1024, value=512, label="Response Length")
+        temp = gr.Slider(0.1, 1.5, value=0.7, label="Empathy Level (Temperature)")
+        top_p_val = gr.Slider(0.1, 1.0, value=0.9, label="Focus (Top-p)")
 
     chatbot_ui = gr.ChatInterface(
         respond,
         type="messages",
-        additional_inputs=[st_msg, tokens, temp, top_p_slider],
+        additional_inputs=[sys_msg, tokens, temp, top_p_val],
         examples=[
-            ["I've been feeling very anxious lately.", st_msg.value, 512, 0.7, 0.9],
-            ["How do I deal with OCD thoughts?", st_msg.value, 512, 0.7, 0.9],
-            ["Ana ta3ban awee.", st_msg.value, 512, 0.7, 0.9]
+            ["I think my sister has OCD, she washes her hands constantly.", sys_msg.value, 512, 0.7, 0.9],
+            ["Ana 7ases b de2 fashkh w msh 3aref anam.", sys_msg.value, 512, 0.7, 0.9],
+            ["I've been feeling very lonely lately.", sys_msg.value, 512, 0.7, 0.9]
         ],
         cache_examples=False,
     )