app.py

import gradio as gr
import requests
from urllib.parse import parse_qs, urlparse
from transformers import pipeline
import os
import time

# Backend API endpoints
BACKEND_BASE_URL = "http://127.0.0.1:5000"
COMPLETE_APPOINTMENT_ENDPOINT = f"{BACKEND_BASE_URL}/complete_appointment"
VALIDATE_APPOINTMENT_ENDPOINT = f"{BACKEND_BASE_URL}/validate_appointment"

# Path to your pre-generated avatar video
AVATAR_VIDEO_PATH = "http://127.0.0.1:5000/serve_video/ai_doctor_avatar.mp4"  # Replace with your actual video URL
 # <source src="http://127.0.0.1:5000/serve_video/ai_doctor_avatar.mp4" type="video/mp4">

# Load lightweight models for faster performance
conversation_pipeline = pipeline(
    "text-generation",
    model="microsoft/DialoGPT-medium",
    device="cpu"  # Change to "cuda" if GPU available
)

medical_qa_pipeline = pipeline(
    "question-answering",
    model="medicalai/ClinicalBERT_QA",
    device="cpu"
)

def get_appointment_id():
    query_params = parse_qs(urlparse(gr.Request().url).query)
    return query_params.get("appointment_id", ["Unknown"])[0]

def validate_appointment(appointment_id):
    try:
        response = requests.post(
            VALIDATE_APPOINTMENT_ENDPOINT,
            json={"appointment_id": appointment_id},
            timeout=3
        )
        return response.json().get("status") == "success"
    except Exception:
        return False

def generate_response(user_input):
    """Lightning-fast response generation"""
    try:
        # Try medical QA first
        qa_result = medical_qa_pipeline({
            "question": user_input,
            "context": "Medical consultation between doctor and patient"
        })
        if qa_result['score'] > 0.25:
            return qa_result['answer']
        
        # Fallback to conversational AI
        return conversation_pipeline(
            f"Patient: {user_input}\nDoctor:",
            max_length=150,
            num_return_sequences=1,
            do_sample=True,
            top_p=0.9,
            temperature=0.7
        )[0]["generated_text"].split("Doctor:")[-1].strip()
    
    except Exception:
        return "I'm experiencing high demand. Could you please repeat your question?"

def end_call(appointment_id):
    try:
        response = requests.post(
            COMPLETE_APPOINTMENT_ENDPOINT,
            json={"appointment_id": appointment_id},
            timeout=3
        )
        if response.status_code == 200:
            return "Consultation completed successfully. Thank you!"
        return "Couldn't complete appointment. Please contact support."
    except Exception:
        return "Network error. Please check your connection."

# Custom CSS for blazing fast UI
custom_css = """
:root {
    --primary: #2d8cf0;
    --secondary: #f8f9fa;
    --accent: #ff6b6b;
}

.gradio-container {
    font-family: 'Inter', sans-serif;
    max-width: 1200px !important;
}

.avatar-container {
    aspect-ratio: 9/16;
    background: black;
    border-radius: 12px;
    overflow: hidden;
}

.video-container video {
    object-fit: cover;
    width: 100%;
    height: 100%;
}

.chat-container {
    height: 100%;
    display: flex;
    flex-direction: column;
}

.chatbot {
    min-height: 500px;
    flex-grow: 1;
    border-radius: 12px;
    background: var(--secondary);
}

.input-row {
    margin-top: 0.5rem !important;
}

.primary-btn {
    background: var(--primary) !important;
}

.end-btn {
    background: var(--accent) !important;
}

/* Animation for smooth loading */
@keyframes fadeIn {
    from { opacity: 0; }
    to { opacity: 1; }
}

.gradio-app {
    animation: fadeIn 0.3s ease-in;
}
"""

with gr.Blocks(css=custom_css, title="AI Doctor Consultation") as demo:
    appointment_id = gr.State(value=get_appointment_id())
    
    # Header
    gr.Markdown("""
    <div style="text-align: center; margin-bottom: 1rem;">
        <h1 style="margin: 0; color: #2d8cf0;">AI Doctor Consultation</h1>
        <p style="margin: 0; color: #666;">Your health matters to us</p>
    </div>
    """)
    
    with gr.Row(equal_height=True):
        # Left column - Avatar video
        with gr.Column(scale=1, elem_classes=["avatar-container"]):
            gr.Markdown("### Dr. AI Avatar")
            video = gr.Video(
                value=AVATAR_VIDEO_PATH,
                autoplay=True,
                interactive=False,
                elem_classes=["video-container"]
            )
            
        # Right column - Chat interface
        with gr.Column(scale=2, elem_classes=["chat-container"]):
            chatbot = gr.Chatbot(
                label="Consultation Chat",
                bubble_full_width=False,
                show_copy_button=True,
                avatar_images=(
                    "https://i.imgur.com/8Km9tLL.png",  # User
                    "https://i.imgur.com/3Q3ZQ2u.png"   # Doctor
                )
            )
            
            with gr.Row(elem_classes=["input-row"]):
                user_input = gr.Textbox(
                    placeholder="Describe your symptoms...",
                    label="",
                    container=False,
                    autofocus=True,
                    max_lines=3
                )
                submit_btn = gr.Button("Send", variant="primary", elem_classes=["primary-btn"])
            
            with gr.Row():
                clear_btn = gr.Button("Clear Chat", variant="secondary")
                end_btn = gr.Button("End Consultation", variant="stop", elem_classes=["end-btn"])
                status = gr.Textbox(visible=False)
    
    # Event handlers
    submit_btn.click(
        fn=lambda msg, hist: (msg, hist + [(msg, generate_response(msg))]),
        inputs=[user_input, chatbot],
        outputs=[user_input, chatbot],
        queue=True
    ).then(
        lambda: gr.update(autoplay=True),  # Ensure video keeps playing
        outputs=video
    )
    
    clear_btn.click(lambda: [], None, chatbot)
    end_btn.click(
        fn=end_call,
        inputs=appointment_id,
        outputs=status
    )

# Optimized launch settings
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    favicon_path="https://i.imgur.com/3Q3ZQ2u.png",
    prevent_thread_lock=True
)