Update app.py

app.py

@@ -1,601 +1,204 @@
-import os
-import gc
-import re
-import time
-import torch
 import gradio as gr
-import numpy as np
-from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
-from transformers import pipeline
-from collections import defaultdict
-from datetime import datetime, timedelta
-import tempfile
-
-# =============================
-# Configuration
-# =============================
-MODEL_PATH = r"Muhammadidrees/JayConverstionalModel"
-WHISPER_MODEL = "openai/whisper-small"  # Change to "openai/whisper-base" for faster, or "openai/whisper-medium" for better accuracy
-TTS_MODEL = "suno/bark-small"  # Alternative: "facebook/mms-tts-eng" for faster TTS
-
-MAX_NEW_TOKENS = 200
-TEMPERATURE = 0.5
-TOP_K = 50
-REPETITION_PENALTY = 1.1
-MAX_HISTORY_TURNS = 5
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"🚀 Loading models on {device}...")
-
-# =============================
-# Rate Limiting
-# =============================
-rate_limit_store = defaultdict(list)
-MAX_REQUESTS_PER_MINUTE = 10
+from groq import GroqClient
+
+# ==============================
+# Initialize Groq client
+# ==============================
+client = GroqClient(api_key="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")
+
+# ==============================
+# System Prompt for Doctor
+# ==============================
+SYSTEM_PROMPT = """
+You are Dr. HealBot, a calm, knowledgeable, and empathetic doctor talking to a patient.
+
+GOAL:
+Have a natural conversation — ask 3-4 short medical questions to understand the patient's condition, 
+then start giving practical advice including:
+- possible over-the-counter medicines (generic name only)
+- simple lifestyle or habit changes
+- nutrition or exercise guidance
+- when to see a real doctor
+
+TONE & STYLE:
+- Speak like a real doctor, short and direct sentences (1-2 lines max).
+- Be warm but professional.
+- Use plain language — no medical jargon unless necessary.
+- No bullet points or lists — just natural speech.
+- Only one question per response, until enough info is gathered.
+- After about 4 patient answers, switch to giving advice.
+
+CONVERSATION FLOW EXAMPLE:
+Doctor: How can I help you?
+Patient: I’ve had a cough for 2 weeks.
+Doctor: Is it dry or with phlegm?
+Patient: With phlegm.
+Doctor: Do you have fever or chest pain?
+Patient: Mild fever.
+Doctor: Do you smoke or have allergies?
+Patient: I smoke.
+Doctor: Sounds like a mild chest infection. You can try paracetamol for fever and warm fluids. 
+Cut down on smoking and rest. If symptoms persist beyond 5 days, see a doctor.
+
+ALWAYS END with a gentle reminder: 
+"Please consult a qualified doctor if it doesn’t improve or if symptoms worsen."
+"""
 
-def check_rate_limit(session_id):
-    """Simple rate limiting to prevent abuse"""
-    now = datetime.now()
-    rate_limit_store[session_id] = [
-        timestamp for timestamp in rate_limit_store[session_id]
-        if now - timestamp < timedelta(minutes=1)
-    ]
-    
-    if len(rate_limit_store[session_id]) >= MAX_REQUESTS_PER_MINUTE:
-        return False
-    
-    rate_limit_store[session_id].append(now)
-    return True
+# ==============================
+# Initial greeting
+# ==============================
+INITIAL_MESSAGE = "How can I help you today?"
+
+# ==============================
+# Chat logic
+# ==============================
+def chat_with_doctor(message, history):
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+
+    # Build chat history
+    for chat in history:
+        if isinstance(chat, dict):
+            messages.append(chat)
+        elif isinstance(chat, (list, tuple)) and len(chat) == 2:
+            if chat[0]:
+                messages.append({"role": "user", "content": chat[0]})
+            if chat[1]:
+                messages.append({"role": "assistant", "content": chat[1]})
+
+    # Add current patient message
+    messages.append({"role": "user", "content": message})
 
-# ==========================
-# Load Models
-# =============================
-try:
-    # Load ChatDoctor Model
-    print("Loading ChatDoctor model...")
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_PATH,
-        device_map="auto",
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        low_cpu_mem_usage=True
-    )
-    print("✅ ChatDoctor model loaded!")
-    
-    # Load Whisper (Speech-to-Text)
-    print("Loading Whisper ASR model...")
-    whisper_pipe = pipeline(
-        "automatic-speech-recognition",
-        model=WHISPER_MODEL,
-        device=0 if torch.cuda.is_available() else -1
-    )
-    print("✅ Whisper model loaded!")
-    
-    # Load TTS Model
-    print("Loading TTS model...")
     try:
-        tts_pipe = pipeline(
-            "text-to-speech",
-            model=TTS_MODEL,
-            device=0 if torch.cuda.is_available() else -1
+        # Count how many patient turns have occurred
+        patient_turns = sum(1 for chat in history if isinstance(chat, (list, tuple)) and chat[0])
+
+        # After 4 patient turns, guide the model to provide recommendations
+        if patient_turns >= 4:
+            messages.append({
+                "role": "system",
+                "content": (
+                    "Now begin giving specific recommendations based on the patient's symptoms. "
+                    "Include possible generic medicines (like paracetamol, ibuprofen, etc.), "
+                    "lifestyle and nutrition tips, and when to seek medical attention. "
+                    "Keep it short and empathetic, like a real doctor speaking naturally."
+                )
+            })
+
+        # Generate the response using Groq LLM
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model="llama-3.3-70b-versatile",
+            temperature=0.6,
+            max_tokens=120,
         )
-        print("✅ TTS model loaded!")
-        TTS_AVAILABLE = True
-    except Exception as e:
-        print(f"⚠️ TTS model not available: {e}")
-        TTS_AVAILABLE = False
-    
-except Exception as e:
-    print(f"❌ Error loading models: {e}")
-    raise
-
-# =============================
-# Stop Criteria
-# =============================
-class StopOnTokens(StoppingCriteria):
-    def __init__(self, stop_ids):
-        self.stop_ids = stop_ids
 
-    def __call__(self, input_ids, scores, **kwargs):
-        for stop_id_seq in self.stop_ids:
-            if len(stop_id_seq) == 1:
-                if input_ids[0][-1] == stop_id_seq[0]:
-                    return True
-            else:
-                if len(input_ids[0]) >= len(stop_id_seq):
-                    if input_ids[0][-len(stop_id_seq):].tolist() == stop_id_seq:
-                        return True
-        return False
+        response = chat_completion.choices[0].message.content.strip()
 
-# =============================
-# Medical Keywords and Validation
-# =============================
-MEDICAL_KEYWORDS = [
-    "pain", "ache", "symptom", "hurt", "sore", "discomfort", "fever", "cough", "flu",
-    "infection", "allergy", "diabetes", "pressure", "asthma", "migraine", "vomit",
-    "stomach", "head", "chest", "throat", "heart", "lung", "liver", "kidney", "brain",
-    "doctor", "hospital", "medicine", "treatment", "therapy", "surgery", "disease",
-    "illness", "blood", "test", "scan", "health", "diet", "nutrition", "stress", "sleep",
-    "weight", "vitamin", "fatigue", "anxiety", "depression", "nausea", "dizziness",
-    "rash", "swelling", "injury", "bruise", "cold", "sneeze", "tired", "weak"
-]
-
-EMERGENCY_KEYWORDS = [
-    "suicide", "kill myself", "end my life", "chest pain", "can't breathe",
-    "severe bleeding", "overdose", "poisoning", "unconscious", "seizure",
-    "stroke", "heart attack", "choking"
-]
-
-CASUAL_PATTERNS = [
-    r"^(hey|hi|hello|sup|yo|wassup|hiya)\s*[\?\!\.]*$",
-    r"^good\s+(morning|evening|afternoon|night)\s*[\?\!\.]*$",
-    r"^how\s+are\s+you\s*[\?\!\.]*$",
-    r"^what'?s\s+up\s*[\?\!\.]*$",
-]
-
-DANGEROUS_PATTERNS = [
-    r"take\s+\d+\s+(pills|tablets|capsules)",
-    r"inject\s+(yourself|myself)",
-    r"(don't|do not)\s+go\s+to\s+(hospital|doctor|emergency)",
-    r"ignore\s+(doctor|medical|professional)",
-]
-
-def is_emergency_query(message):
-    message_lower = message.lower()
-    return any(keyword in message_lower for keyword in EMERGENCY_KEYWORDS)
-
-def is_medical_query(message):
-    message_lower = message.lower()
-    for keyword in MEDICAL_KEYWORDS:
-        if keyword in message_lower:
-            return True
-    
-    question_words = ["what", "how", "why", "when", "where", "can", "should", "is", "are", "do", "does", "could", "would"]
-    words = message_lower.split()
-    has_question = any(q in words[:4] for q in question_words)
-    
-    if has_question and len(words) > 5:
-        return True
-    
-    return False
-
-def is_only_greeting(message):
-    message_clean = message.lower().strip()
-    message_clean = re.sub(r'[!?.]+$', '', message_clean)
-    
-    for pattern in CASUAL_PATTERNS:
-        if re.match(pattern, message_clean):
-            return True
-    
-    return False
-
-def contains_dangerous_advice(response):
-    response_lower = response.lower()
-    for pattern in DANGEROUS_PATTERNS:
-        if re.search(pattern, response_lower):
-            return True
-    return False
-
-# =============================
-# Speech Processing Functions
-# =============================
-def transcribe_audio(audio):
-    """Convert speech to text using Whisper"""
-    if audio is None:
-        return ""
-    
-    try:
-        # Handle different audio input formats
-        if isinstance(audio, tuple):
-            sample_rate, audio_data = audio
-        else:
-            audio_data = audio
-        
-        # Ensure audio is in the right format
-        if isinstance(audio_data, np.ndarray):
-            if audio_data.dtype != np.float32:
-                audio_data = audio_data.astype(np.float32) / np.iinfo(audio_data.dtype).max
-        
-        # Transcribe
-        result = whisper_pipe(audio_data)
-        transcription = result["text"].strip()
-        
-        return transcription
-        
-    except Exception as e:
-        print(f"Error in transcription: {e}")
-        return ""
+        # Append to history
+        history.append([message, response])
+        return history
 
-def text_to_speech(text):
-    """Convert text to speech"""
-    if not TTS_AVAILABLE or not text:
-        return None
-    
-    try:
-        # Limit text length for TTS (to avoid timeout)
-        if len(text) > 500:
-            text = text[:500] + "..."
-        
-        # Generate speech
-        speech = tts_pipe(text)
-        
-        # Extract audio data
-        audio_data = speech["audio"]
-        sampling_rate = speech["sampling_rate"]
-        
-        return (sampling_rate, audio_data)
-        
     except Exception as e:
-        print(f"Error in TTS: {e}")
-        return None
-
-# =============================
-# Get Response
-# =============================
-def get_response(user_input, history_context, session_id="default"):
-    """Generate response with enhanced safety and quality checks"""
-    
-    if not check_rate_limit(session_id):
-        return "⏰ You've made too many requests. Please wait a minute before trying again."
-    
-    if is_emergency_query(user_input):
-        return (
-            "🚨 **EMERGENCY DETECTED** 🚨\n\n"
-            "If you are experiencing a medical emergency, please:\n"
-            "• Call emergency services immediately (911 in US, 999 in UK, 112 in EU)\n"
-            "• Go to the nearest emergency room\n"
-            "• Contact your local emergency hotline\n\n"
-            "This AI cannot provide emergency medical care. Please seek immediate professional help."
-        )
-    
-    if is_only_greeting(user_input):
-        return "👋 Hello! I'm ChatDoctor — your AI medical assistant. Please tell me about any health symptoms or medical concerns you'd like to discuss."
-
-    if not is_medical_query(user_input):
-        return (
-            "Hello! I'm ChatDoctor, an AI medical assistant specialized in health and wellness.\n\n"
-            "I can help you with:\n"
-            "• Symptoms and medical conditions\n"
-            "• Treatment and prevention advice\n"
-            "• Fitness, diet, and mental health tips\n\n"
-            "Please describe your health concern in detail to get started."
-        )
-
-    human_prefix = "Patient:"
-    doctor_prefix = "ChatDoctor:"
-    system_instruction = (
-        "You are ChatDoctor, a professional medical AI assistant. "
-        "You provide accurate, concise, and empathetic responses to health-related questions only.\n"
-        "Always recommend consulting a healthcare professional for serious conditions.\n"
-        "Never provide dosage instructions or tell patients to avoid seeking professional help.\n\n"
-    )
-
-    limited_history = history_context[-MAX_HISTORY_TURNS:] if len(history_context) > MAX_HISTORY_TURNS else history_context
-    
-    history_text = [system_instruction]
-    for human, assistant in limited_history:
-        if human:
-            history_text.append(f"{human_prefix} {human}")
-        if assistant:
-            history_text.append(f"{doctor_prefix} {assistant}")
-    history_text.append(f"{human_prefix} {user_input}")
-
-    prompt = "\n".join(history_text) + f"\n{doctor_prefix} "
-    
-    try:
-        input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-        
-        stop_words = ["Patient:", "\nPatient:", "Patient :", "\n\nPatient"]
-        stop_ids = [tokenizer.encode(word, add_special_tokens=False) for word in stop_words]
-        stopping_criteria = StoppingCriteriaList([StopOnTokens(stop_ids)])
-
-        with torch.no_grad():
-            output_ids = model.generate(
-                input_ids,
-                max_new_tokens=MAX_NEW_TOKENS,
-                do_sample=True,
-                temperature=TEMPERATURE,
-                top_k=TOP_K,
-                repetition_penalty=REPETITION_PENALTY,
-                stopping_criteria=stopping_criteria,
-                pad_token_id=tokenizer.eos_token_id,
-                eos_token_id=tokenizer.eos_token_id
-            )
-
-        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)[len(prompt):].strip()
-
-        for stop_word in ["Patient:", "Patient :", "\nPatient", "Patient"]:
-            if stop_word in response:
-                response = response.split(stop_word)[0].strip()
-                break
-
-        response = response.strip()
-        
-        if contains_dangerous_advice(response):
-            response = (
-                "I apologize, but I cannot provide that specific medical advice. "
-                "Please consult with a qualified healthcare professional who can properly evaluate your situation."
-            )
-        
-        if any(x in response.lower() for x in ["chatbot", "api key", "error", "cloud", "sorry, i don't have"]):
-            response = (
-                "I apologize for the confusion. I'm ChatDoctor, trained to assist with medical and health-related topics. "
-                "Please tell me more about your symptoms or health concerns so I can help you better."
-            )
-        
-        serious_conditions = ["cancer", "tumor", "heart disease", "stroke", "diabetes complications"]
-        if any(condition in response.lower() for condition in serious_conditions):
-            response += "\n\n⚠️ **Important:** Please consult a healthcare professional for proper diagnosis and treatment."
+        error_msg = f"⚠️ Error: {str(e)}. Please check your API connection and try again."
+        history.append([message, error_msg])
+        return history
 
-        del input_ids, output_ids
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
 
-        return response
-        
-    except Exception as e:
-        print(f"Error generating response: {e}")
-        return "I apologize, but I encountered an error processing your request. Please try rephrasing your question or try again later."
+def reset_conversation():
+    """Reset the chat to start fresh"""
+    return []
 
-# =============================
-# Gradio Interface
-# =============================
+# ==============================
+# Custom CSS
+# ==============================
 custom_css = """
-#header {
-    text-align: center;
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    color: white;
-    padding: 25px;
-    border-radius: 12px;
-    margin-bottom: 20px;
-    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+#chatbot {
+    height: 600px;
 }
-#header h1 { margin: 0; font-size: 2.5em; font-weight: 700; }
-#header p { margin: 5px 0 0; font-size: 1.1em; opacity: 0.95; }
-.disclaimer {
-    background-color: #fff3cd;
-    border-left: 4px solid #ffc107;
-    border-radius: 8px;
-    padding: 18px;
-    margin: 20px 0;
-    color: #856404;
+.gradio-container {
+    font-family: 'Arial', sans-serif;
 }
-.disclaimer h3 { margin-top: 0; color: #d39e00; }
-.emergency-warning {
-    background-color: #f8d7da;
-    border-left: 4px solid #dc3545;
+#warning {
+    background-color: #fff3cd;
+    border: 1px solid #ffc107;
     border-radius: 8px;
     padding: 15px;
-    margin: 15px 0;
-    color: #721c24;
-}
-.voice-section {
-    background: linear-gradient(135deg, #e0c3fc 0%, #8ec5fc 100%);
-    border-radius: 10px;
-    padding: 20px;
-    margin: 15px 0;
-}
-footer {
-    margin-top: 30px;
-    padding: 15px;
-    text-align: center;
-    color: #6c757d;
-    font-size: 0.9em;
+    margin: 10px 0;
+    color: #856404;
 }
 """
 
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    session_state = gr.State(value=str(time.time()))
-    
-    gr.HTML("""
-        <div id="header">
-            <h1>🩺 ChatDoctor AI Assistant</h1>
-            <p>🎤 Voice-Enabled Medical Consultation Partner</p>
-        </div>
-    """)
-    
-    gr.HTML("""
-        <div class="disclaimer">
-            <h3>⚠️ Medical Disclaimer</h3>
-            <p><strong>This AI assistant is for informational purposes only.</strong> 
-            It is NOT a substitute for professional medical advice, diagnosis, or treatment. 
-            Always seek the advice of your physician or qualified health provider with any questions 
-            you may have regarding a medical condition.</p>
-        </div>
-    """)
-    
-    gr.HTML("""
-        <div class="emergency-warning">
-            <h4>🚨 In Case of Emergency</h4>
-            <p>If you are experiencing a medical emergency, call emergency services immediately 
-            (911 in US, 999 in UK, 112 in EU) or go to the nearest emergency room.</p>
-        </div>
-    """)
-
-    with gr.Tab("💬 Text Chat"):
-        chatbot = gr.Chatbot(
-            height=500,
-            placeholder="<div style='text-align:center;padding:50px;'><h3>👋 Welcome to ChatDoctor!</h3><p style='color:#6c757d;'>Describe your symptoms or ask a health-related question to begin.</p></div>",
-            show_label=False,
-            avatar_images=(None, "🤖"),
-        )
+# ==============================
+# Gradio Interface
+# ==============================
+with gr.Blocks(css=custom_css, title="AI Medical Consultant") as demo:
+    gr.Markdown(
+        """
+        # 🏥 AI Medical Consultant
+        ### Realistic Doctor-Patient Conversation • Medicine • Lifestyle • Nutrition
+        """
+    )
 
-        with gr.Row():
-            msg = gr.Textbox(
-                placeholder="Type your medical concern here...",
-                show_label=False,
-                scale=9,
-                container=False,
-                lines=1
-            )
-            send_btn = gr.Button("Send 📤", scale=1, variant="primary")
+    gr.HTML(
+        """
+        <div id="warning">
+            <strong>⚠️ Medical Disclaimer:</strong><br>
+            This AI provides general health information only. It is <b>NOT</b> a substitute for
+            professional medical advice, diagnosis, or treatment.<br>
+            Always consult qualified healthcare providers for medical concerns.<br>
+            For emergencies, call your local emergency number immediately.
+        </div>
+        """
+    )
 
-        with gr.Row():
-            clear_btn = gr.Button("🗑️ Clear Chat", scale=1)
-            retry_btn = gr.Button("🔄 Retry", scale=1)
+    chatbot = gr.Chatbot(
+        value=[[None, INITIAL_MESSAGE]],
+        elem_id="chatbot",
+        height=600,
+        show_label=False,
+        avatar_images=(None, "https://api.dicebear.com/7.x/bottts/svg?seed=doctor"),
+        type="tuples"
+    )
 
-    with gr.Tab("🎤 Voice Chat"):
-        gr.HTML('<div class="voice-section"><h3>🎙️ Voice Interaction</h3><p>Record your medical question and get voice responses!</p></div>')
-        
-        voice_chatbot = gr.Chatbot(
-            height=400,
-            placeholder="<div style='text-align:center;padding:40px;'><h3>🎤 Voice Chat Mode</h3><p>Click the microphone to record your question</p></div>",
+    with gr.Row():
+        msg = gr.Textbox(
+            placeholder="Describe your symptoms or ask a question...",
             show_label=False,
-            avatar_images=(None, "🤖"),
-        )
-        
-        with gr.Row():
-            audio_input = gr.Audio(
-                sources=["microphone"],
-                type="numpy",
-                label="🎤 Record Your Question",
-                scale=8
-            )
-            voice_send_btn = gr.Button("Send Voice 🎙️", scale=2, variant="primary")
-        
-        audio_output = gr.Audio(
-            label="🔊 Voice Response",
-            autoplay=True,
-            visible=TTS_AVAILABLE
-        )
-        
-        transcribed_text = gr.Textbox(
-            label="📝 Transcribed Text",
-            interactive=False,
-            visible=True
+            scale=9,
+            lines=2
         )
-        
-        with gr.Row():
-            voice_clear_btn = gr.Button("🗑️ Clear Voice Chat", scale=1)
-        
-        if not TTS_AVAILABLE:
-            gr.Warning("⚠️ TTS model not available. Voice responses disabled. Text responses will still work.")
-
-    with gr.Accordion("⚙️ Advanced Settings", open=False):
-        temp_slider = gr.Slider(0.1, 1.0, TEMPERATURE, 0.1, label="Temperature (Lower = More Focused)")
-        max_tok_slider = gr.Slider(50, 500, MAX_NEW_TOKENS, 50, label="Max Tokens")
-        top_k_slider = gr.Slider(1, 100, TOP_K, 1, label="Top-K Sampling")
-
-    # =============================
-    # Text Chat Functions
-    # =============================
-    def user_message(user_msg, history):
-        if not user_msg.strip():
-            return "", history
-        return "", history + [[user_msg, None]]
-
-    def bot_response(history, temp, max_tok, topk, session_id):
-        if not history or history[-1][1] is not None:
-            return history
-            
-        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
-        TEMPERATURE, MAX_NEW_TOKENS, TOP_K = temp, int(max_tok), int(topk)
-        
-        user_msg = history[-1][0]
-        bot_msg = get_response(user_msg, history[:-1], session_id)
-        history[-1][1] = bot_msg
-        return history
-
-    def retry_last(history, temp, max_tok, topk, session_id):
-        if not history:
-            return history
-        user_msg = history[-1][0]
-        bot_msg = get_response(user_msg, history[:-1], session_id)
-        history[-1][1] = bot_msg
-        return history
-
-    # =============================
-    # Voice Chat Functions
-    # =============================
-    def text_to_speech(text):
-        # Convert text to speech using Bark
-        from transformers import AutoProcessor, BarkModel
-        import numpy as np
-    
-        processor = AutoProcessor.from_pretrained("suno/bark-small")
-        model = BarkModel.from_pretrained("suno/bark-small")
-    
-        inputs = processor(text, voice_preset="v2/en_speaker_6", return_tensors="pt")
-        speech = model.generate(**inputs)
-    
-        # ✅ Extract and normalize audio data
-        audio_data = speech["audio"]
-        sampling_rate = speech["sampling_rate"]
-    
-        # 🔊 Normalize & clip Bark audio output to avoid struct.error
-        if isinstance(audio_data, np.ndarray):
-            audio_data = np.clip(audio_data, -1.0, 1.0).astype(np.float32)
-        else:
-            audio_data = np.array(audio_data, dtype=np.float32)
-            audio_data = np.clip(audio_data, -1.0, 1.0)
-    
-        return (sampling_rate, audio_data)
-
-    def process_voice_input(audio, history, temp, max_tok, topk, session_id):
-        """Process voice input: transcribe, get response, convert to speech"""
-        if audio is None:
-            return history, "", None
-        
-        # Transcribe audio to text
-        transcribed = transcribe_audio(audio)
-        
-        if not transcribed:
-            return history, "⚠️ Could not transcribe audio. Please try again.", None
-        
-        # Add to history
-        history = history + [[transcribed, None]]
-        
-        # Get bot response
-        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
-        TEMPERATURE, MAX_NEW_TOKENS, TOP_K = temp, int(max_tok), int(topk)
-        
-        bot_msg = get_response(transcribed, history[:-1], session_id)
-        history[-1][1] = bot_msg
-        
-        # Convert response to speech
-        audio_response = text_to_speech(bot_msg) if TTS_AVAILABLE else None
-        
-        return history, transcribed, audio_response
+        submit_btn = gr.Button("Send 📤", scale=1, variant="primary")
+
+    with gr.Row():
+        clear_btn = gr.Button("🔄 Start New Consultation", variant="secondary")
+
+    gr.Markdown(
+        """
+        ### 💡 Tips for Best Results:
+        - Be specific about your symptoms (location, severity, duration)
+        - Mention any relevant medical history or medications
+        - Ask follow-up questions freely
+        """
+    )
 
-    # Text Chat Events
-    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, [chatbot, temp_slider, max_tok_slider, top_k_slider, session_state], chatbot
+    # Event Handlers
+    msg.submit(chat_with_doctor, [msg, chatbot], [chatbot]).then(
+        lambda: gr.update(value=""), None, [msg]
     )
-    send_btn.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, [chatbot, temp_slider, max_tok_slider, top_k_slider, session_state], chatbot
+    submit_btn.click(chat_with_doctor, [msg, chatbot], [chatbot]).then(
+        lambda: gr.update(value=""), None, [msg]
     )
-    clear_btn.click(lambda: None, None, chatbot, queue=False)
-    retry_btn.click(retry_last, [chatbot, temp_slider, max_tok_slider, top_k_slider, session_state], chatbot)
-
-    # Voice Chat Events
-    voice_send_btn.click(
-        process_voice_input,
-        [audio_input, voice_chatbot, temp_slider, max_tok_slider, top_k_slider, session_state],
-        [voice_chatbot, transcribed_text, audio_output]
+    clear_btn.click(reset_conversation, None, [chatbot]).then(
+        lambda: [[None, INITIAL_MESSAGE]], None, [chatbot]
     )
-    voice_clear_btn.click(lambda: (None, "", None), None, [voice_chatbot, transcribed_text, audio_output], queue=False)
 
-    gr.HTML(f"""
-        <footer>
-            <p><strong>🧠 Powered by LLaMA + Whisper + TTS</strong></p>
-            <p>Device: {device.upper()} | Rate Limit: {MAX_REQUESTS_PER_MINUTE} requests/minute</p>
-            <p>🎤 Voice: Whisper ASR | 🔊 TTS: {"Enabled" if TTS_AVAILABLE else "Disabled"}</p>
-            <p style='font-size:0.85em;margin-top:10px;'>
-                This AI provides general health information only. Always consult healthcare professionals for medical advice.
-            </p>
-        </footer>
-    """)
-
-# =============================
-# Launch App
-# =============================
+# ==============================
+# Launch app
+# ==============================
 if __name__ == "__main__":
-    print("\n💡 Launching ChatDoctor with Voice Support...")
-    print(f"📊 Configuration:")
-    print(f"   - Device: {device.upper()}")
-    print(f"   - Whisper Model: {WHISPER_MODEL}")
-    print(f"   - TTS Available: {TTS_AVAILABLE}")
-    print(f"   - Rate Limit: {MAX_REQUESTS_PER_MINUTE} requests/minute")
-    demo.queue()
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    demo.launch(
+        share=True,
+        show_error=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )