Delete withvoiceandfrontend .py

withvoiceandfrontend .py

@@ -1,459 +0,0 @@
-import os
-import gc
-import torch
-import gradio as gr
-from transformers import LlamaTokenizer, LlamaForCausalLM, StoppingCriteria, StoppingCriteriaList
-
-# =============================
-# Configuration
-# =============================
-MODEL_PATH = r"Muhammadidrees/JayConverstionalModel"
-MAX_NEW_TOKENS = 200
-TEMPERATURE = 0.5
-TOP_K = 50
-REPETITION_PENALTY = 1.1
-
-# Detect device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Loading model from {MODEL_PATH} on {device}...")
-
-# =============================
-# Load Tokenizer and Model
-# =============================
-tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
-model = LlamaForCausalLM.from_pretrained(
-    MODEL_PATH,
-    device_map="auto",
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True
-)
-
-generator = model.generate
-print("✅ ChatDoctor model loaded successfully!\n")
-
-# =============================
-# Stopping 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
-
-# =============================
-# Get Response Function
-# =============================
-def get_response(user_input, history_context):
-    """Generate response from ChatDoctor model"""
-    human_invitation = "Patient: "
-    doctor_invitation = "ChatDoctor: "
-
-    # Build conversation from history
-    history_text = []
-    for human, assistant in history_context:
-        if human:
-            history_text.append(human_invitation + human)
-        if assistant:
-            history_text.append(doctor_invitation + assistant)
-    
-    # Add current user input
-    history_text.append(human_invitation + user_input)
-
-    # Build conversation prompt
-    prompt = "\n".join(history_text) + "\n" + doctor_invitation
-    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-
-    # Define stop words and their token IDs
-    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)])
-
-    # Generate model response
-    with torch.no_grad():
-        output_ids = generator(
-            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
-        )
-
-    # Decode and clean response
-    full_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    response = full_output[len(prompt):].strip()
-    
-    # Remove any "Patient:" that might have slipped through
-    for stop_word in ["Patient:", "Patient :", "\nPatient:", "\nPatient", "Patient"]:
-        if stop_word in response:
-            response = response.split(stop_word)[0].strip()
-            break
-
-    response = response.strip()
-
-    # Free memory
-    del input_ids, output_ids
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    return response
-
-# =============================
-# Gradio Chat Function
-# =============================
-def chat_function(message, history):
-    """Gradio chat interface function"""
-    if not message.strip():
-        return ""
-    
-    try:
-        response = get_response(message, history)
-        return response
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# =============================
-# Text-to-Speech Function
-# =============================
-def text_to_speech(text):
-    """Convert text response to speech"""
-    try:
-        from gtts import gTTS
-        import tempfile
-        
-        if not text or text.startswith("Error:"):
-            return None
-        
-        # Create speech
-        tts = gTTS(text=text, lang='en', slow=False)
-        
-        # Save to temporary file
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
-        tts.save(temp_file.name)
-        
-        return temp_file.name
-    except Exception as e:
-        print(f"TTS Error: {e}")
-        return None
-
-# =============================
-# Custom CSS
-# =============================
-custom_css = """
-#header {
-    text-align: center;
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    color: white;
-    padding: 20px;
-    border-radius: 10px;
-    margin-bottom: 20px;
-}
-
-#header h1 {
-    margin: 0;
-    font-size: 2.5em;
-}
-
-#header p {
-    margin: 10px 0 0 0;
-    font-size: 1.1em;
-    opacity: 0.9;
-}
-
-.disclaimer {
-    background-color: #fff3cd;
-    border: 1px solid #ffc107;
-    border-radius: 8px;
-    padding: 15px;
-    margin: 20px 0;
-    color: #856404;
-}
-
-.disclaimer h3 {
-    margin-top: 0;
-    color: #856404;
-}
-
-.voice-section {
-    background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
-    padding: 20px;
-    border-radius: 10px;
-    margin: 20px 0;
-}
-
-footer {
-    text-align: center;
-    margin-top: 30px;
-    color: #666;
-    font-size: 0.9em;
-}
-"""
-
-# =============================
-# Gradio Interface
-# =============================
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    # Header
-    gr.HTML("""
-        <div id="header">
-            <h1>🩺 ChatDoctor AI Assistant</h1>
-            <p>Your AI-powered medical conversation partner with Voice Support</p>
-        </div>
-    """)
-    
-    # Disclaimer
-    gr.HTML("""
-        <div class="disclaimer">
-            <h3>⚠️ Medical Disclaimer</h3>
-            <p><strong>Important:</strong> This AI assistant is for informational and educational purposes only. 
-            It is NOT a substitute for professional medical advice, diagnosis, or treatment. 
-            Always seek the advice of your physician or other qualified health provider with any questions 
-            you may have regarding a medical condition. Never disregard professional medical advice or 
-            delay in seeking it because of something you have read here.</p>
-        </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=7):
-            # Chatbot Interface
-            chatbot = gr.Chatbot(
-                height=500,
-                placeholder="<div style='text-align: center; padding: 40px;'><h3>👋 Welcome to ChatDoctor!</h3><p>I'm here to discuss your health concerns. Type or speak your question!</p></div>",
-                show_label=False,
-                avatar_images=(None, "🤖"),
-            )
-            
-            with gr.Row():
-                msg = gr.Textbox(
-                    placeholder="Type your message here... (e.g., 'I have a headache')",
-                    show_label=False,
-                    scale=9,
-                    container=False
-                )
-                submit_btn = gr.Button("Send 📤", scale=1, variant="primary")
-            
-            with gr.Row():
-                clear_btn = gr.Button("🗑️ Clear Chat", scale=1)
-                retry_btn = gr.Button("🔄 Retry", scale=1)
-        
-        with gr.Column(scale=3):
-            # Voice Input Section
-            gr.HTML("<div class='voice-section'><h3 style='color: white; text-align: center; margin-top: 0;'>🎤 Voice Features</h3></div>")
-            
-            audio_input = gr.Audio(
-                sources=["microphone"],
-                type="filepath",
-                label="🎙️ Speak Your Question",
-                show_download_button=False
-            )
-            
-            transcribed_text = gr.Textbox(
-                label="📝 Transcribed Text",
-                placeholder="Your speech will appear here...",
-                interactive=False,
-                lines=3
-            )
-            
-            send_voice_btn = gr.Button("Send Voice Message 🔊", variant="primary")
-            
-            gr.Markdown("---")
-            
-            # Voice Output
-            tts_enabled = gr.Checkbox(
-                label="🔊 Enable Text-to-Speech for responses",
-                value=True,
-                info="Hear the doctor's response"
-            )
-            
-            audio_output = gr.Audio(
-                label="🔈 AI Response Audio",
-                autoplay=False,
-                visible=True
-            )
-    
-    # Examples
-    gr.Examples(
-        examples=[
-            "I have a persistent headache for 3 days. What should I do?",
-            "What are the symptoms of diabetes?",
-            "How can I improve my sleep quality?",
-            "I have a fever and sore throat. Should I be concerned?",
-            "What are some natural ways to reduce stress?",
-        ],
-        inputs=msg,
-        label="💡 Example Questions"
-    )
-    
-    # Settings (collapsed by default)
-    with gr.Accordion("⚙️ Advanced Settings", open=False):
-        temperature_slider = gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=TEMPERATURE,
-            step=0.1,
-            label="Temperature (Creativity)",
-            info="Higher values make responses more creative but less focused"
-        )
-        max_tokens_slider = gr.Slider(
-            minimum=50,
-            maximum=500,
-            value=MAX_NEW_TOKENS,
-            step=50,
-            label="Max Response Length",
-            info="Maximum number of tokens in response"
-        )
-        top_k_slider = gr.Slider(
-            minimum=1,
-            maximum=100,
-            value=TOP_K,
-            step=1,
-            label="Top K",
-            info="Limits vocabulary selection"
-        )
-    
-    # Footer
-    gr.HTML("""
-        <footer>
-            <p>Powered by ChatDoctor Model | Built with Gradio | Voice-Enabled 🎤</p>
-            <p>Device: """ + device.upper() + """ | Model: LLaMA-based Medical AI</p>
-        </footer>
-    """)
-    
-    # =============================
-    # Event Handlers
-    # =============================
-    
-    def user_message(user_msg, history):
-        return "", history + [[user_msg, None]], None
-    
-    def bot_response(history, temp, max_tok, top_k_val, tts_enabled_val):
-        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
-        TEMPERATURE = temp
-        MAX_NEW_TOKENS = int(max_tok)
-        TOP_K = int(top_k_val)
-        
-        user_msg = history[-1][0]
-        bot_msg = chat_function(user_msg, history[:-1])
-        history[-1][1] = bot_msg
-        
-        # Generate audio if TTS is enabled
-        audio_file = None
-        if tts_enabled_val and bot_msg and not bot_msg.startswith("Error:"):
-            audio_file = text_to_speech(bot_msg)
-        
-        return history, audio_file
-    
-    def transcribe_audio(audio_file):
-        """Transcribe audio to text using Whisper"""
-        if audio_file is None:
-            return ""
-        
-        try:
-            import whisper
-            model = whisper.load_model("base")
-            result = model.transcribe(audio_file)
-            return result["text"]
-        except ImportError:
-            return "Error: Please install whisper: pip install openai-whisper"
-        except Exception as e:
-            return f"Transcription error: {str(e)}"
-    
-    def process_voice_input(audio_file, history, temp, max_tok, top_k_val, tts_enabled_val):
-        """Process voice input: transcribe -> send -> get response"""
-        if audio_file is None:
-            return history, "", None, None
-        
-        # Transcribe
-        transcribed = transcribe_audio(audio_file)
-        
-        if transcribed.startswith("Error:"):
-            return history, transcribed, None, None
-        
-        # Add to chat
-        history = history + [[transcribed, None]]
-        
-        # Get response
-        global TEMPERATURE, MAX_NEW_TOKENS, TOP_K
-        TEMPERATURE = temp
-        MAX_NEW_TOKENS = int(max_tok)
-        TOP_K = int(top_k_val)
-        
-        bot_msg = chat_function(transcribed, history[:-1])
-        history[-1][1] = bot_msg
-        
-        # Generate audio if TTS is enabled
-        audio_file = None
-        if tts_enabled_val and bot_msg and not bot_msg.startswith("Error:"):
-            audio_file = text_to_speech(bot_msg)
-        
-        return history, transcribed, None, audio_file
-    
-    # Text input events
-    msg.submit(
-        user_message, 
-        [msg, chatbot], 
-        [msg, chatbot, audio_output], 
-        queue=False
-    ).then(
-        bot_response, 
-        [chatbot, temperature_slider, max_tokens_slider, top_k_slider, tts_enabled], 
-        [chatbot, audio_output]
-    )
-    
-    submit_btn.click(
-        user_message, 
-        [msg, chatbot], 
-        [msg, chatbot, audio_output], 
-        queue=False
-    ).then(
-        bot_response, 
-        [chatbot, temperature_slider, max_tokens_slider, top_k_slider, tts_enabled], 
-        [chatbot, audio_output]
-    )
-    
-    # Voice input events
-    audio_input.change(
-        transcribe_audio,
-        [audio_input],
-        [transcribed_text]
-    )
-    
-    send_voice_btn.click(
-        process_voice_input,
-        [audio_input, chatbot, temperature_slider, max_tokens_slider, top_k_slider, tts_enabled],
-        [chatbot, transcribed_text, audio_input, audio_output]
-    )
-    
-    # Clear and retry
-    clear_btn.click(lambda: (None, None, None), None, [chatbot, audio_output, transcribed_text], queue=False)
-    
-    retry_btn.click(lambda: None, None, chatbot, queue=False)
-
-# =============================
-# Launch Interface
-# =============================
-if __name__ == "__main__":
-    print("\n🚀 Launching ChatDoctor Gradio Interface with Voice Support...")
-    print("\n📦 Required packages:")
-    print("   pip install gradio gTTS openai-whisper")
-    print("\nNote: Whisper will download models on first use (~100MB for base model)\n")
-    
-    demo.queue()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True
-    )