Upload app.py

app.py

@@ -2,136 +2,137 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import random
 import re
-import torch
-from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
-import librosa
-from gtts import gTTS
-import tempfile
-import os
-
-# ========== MENTAL HEALTH FILTERS ==========
 
+# ✅ Allowed mental health keywords (EN + AR + transliterated Arabic)
 MENTAL_KEYWORDS = [
+    # English
     "depression", "depressed", "anxiety", "anxious", "panic", "stress", "sad", "lonely",
-    "trauma", "mental", "therapy", "therapist", "mood", "overwhelmed", "anger", "fear",
-    "worry", "self-esteem", "confidence", "motivation", "relationship", "cope", "coping",
-    "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",
+    "trauma", "mental", "therapy", "therapist", "counselor", "mood", "overwhelmed", "anger",
+    "fear", "worry", "self-esteem", "confidence", "motivation", "relationship", "cope", "coping",
+    "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",
     # Transliterated Arabic
-    "ana", "zahqan", "daye2", "ha2t", "mota3ab", "mota3eb", "za3lan", "malo", "khalni",
-    "mash3or", "bakhaf", "w7ed", "msh 3aref", "mash fahem", "malish", "3ayez", "ayez",
-    "7azeen", "mdaye2",
+    "ana", "zahqan", "daye2", "ha2t", "mota3ab", "mota3eb", "za3lan", "malo", "khalni", "mash3or",
+    "bakhaf", "w7ed", "msh 3aref", "mash fahem", "malish", "3ayez", "ayez", "7azeen", "mdaye2",
     # Arabic
     "حزين", "تعبان", "قلق", "خايف", "وحدة", "ضيق", "توتر", "زعلان", "اكتئاب", "علاج",
     "مشاعر", "مضغوط", "قلقان", "وحدي", "مش مبسوط", "زهقان", "ضايق", "تعب", "مش مرتاح",
 ]
 
+# ✅ Off-topic keywords (EN + AR)
 OFF_TOPIC = [
-    "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",
+    # 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 your feelings.",
-    "I specialize in mental and emotional health conversations. How have you been feeling?",
-    "Let's bring it back to your emotions — I'm here to help process stress or challenges.",
+    "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?",
 ]
 
-
+# ✅ 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
     if any(word in text_lower for word in OFF_TOPIC):
         return False
+
+    # If it has mental-related Arabic/English → allow
     if any(word in text_lower for word in MENTAL_KEYWORDS):
         return True
-    if contains_arabic(text_lower):
-        return True
-    return False
-
-
-# ========== EMOTION DETECTION MODEL ==========
-
-voice_model_name = "Hatman/audio-emotion-detection"
-voice_model = Wav2Vec2ForSequenceClassification.from_pretrained(voice_model_name)
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(voice_model_name)
-
-
-def detect_voice_emotion(audio_file):
-    audio, sr = librosa.load(audio_file, sr=16000)
-    inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = voice_model(**inputs).logits
-    predicted_id = torch.argmax(logits, dim=-1).item()
-    return voice_model.config.id2label[predicted_id]
 
+    # If purely Arabic but not off-topic → assume emotional (allow)
+    if has_arabic:
+        return True
 
-# ========== RESPONSE LOGIC ==========
-
-def respond(message, history, system_message, max_tokens, temperature, top_p, hf_token: gr.OAuthToken, audio=None):
-    transcript = []
-    response_text = ""
+    # Default fallback
+    return False
 
-    # Mood detection from voice
-    if audio:
-        mood = detect_voice_emotion(audio)
-        response_text += f"[Detected mood: {mood}] "
 
-    # Mental health filtering
+# ✅ Main response function
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
     if not is_mental_health_related(message):
-        bot_reply = random.choice(OFF_TOPIC_RESPONSES)
-        transcript.extend([("User", message), ("Bot", bot_reply)])
-        tts_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
-        gTTS(bot_reply).save(tts_file)
-        return bot_reply, tts_file, transcript
+        yield random.choice(OFF_TOPIC_RESPONSES)
+        return
 
-    # GPT-based mental health conversation
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
     locked_system_message = (
         "You are a licensed mental health therapy assistant. "
         "You respond with empathy, emotional intelligence, and a therapeutic tone. "
-        "Never answer unrelated questions."
+        "Never answer questions unrelated to emotional or mental wellness, even if they are in another language."
     )
 
+    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+
     messages = [{"role": "system", "content": locked_system_message}]
     messages.extend(history)
     messages.append({"role": "user", "content": message})
 
-    for msg in client.chat_completion(messages, max_tokens=max_tokens, stream=True,
-                                      temperature=temperature, top_p=top_p):
-        if msg.choices and msg.choices[0].delta.content:
-            response_text += msg.choices[0].delta.content
-
-    transcript.extend([("User", message), ("Bot", response_text)])
-
-    tts_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
-    gTTS(response_text).save(tts_file)
-
-    return response_text, tts_file, transcript
-
-
-# ========== GRADIO UI ==========
+    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)",
+        ),
+    ],
+)
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Mental Health Chatbot with Voice Mood Detection & TTS")
-    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"),
-            gr.Audio(label="🎙️ Speak (optional)", type="filepath"),
-            gr.OAuthToken(label="Hugging Face Token"),
-        ],
-    )
+    with gr.Sidebar():
+        gr.LoginButton()
+    chatbot.render()
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()