app.py

import os
import re
import torch
import numpy as np
import pandas as pd
import faiss
import base64
import tempfile
import speech_recognition as sr
from gtts import gTTS
from io import BytesIO
from PIL import Image
from sentence_transformers import SentenceTransformer
from transformers import (
    AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig,
    pipeline, AutoFeatureExtractor, AutoModelForAudioClassification,
    AutoImageProcessor, AutoModelForImageClassification,
    AutoModelForSequenceClassification
)
import gradio as gr

# Device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Model loading (global, runs on app start)
PRIMARY_MODEL = "tiiuae/falcon-rw-1b"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(PRIMARY_MODEL)
model = AutoModelForCausalLM.from_pretrained(
    PRIMARY_MODEL, device_map="auto", quantization_config=quantization_config
)

# Sentiment, emotion, ABSA, etc. (load all pipelines as in notebook)
sentiment_pipe = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", device=device.index if 'cuda' in str(device) else -1)
emotion_pipe = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True, device=device.index if 'cuda' in str(device) else -1)
absa_pipe = pipeline("text-classification", model="yangheng/deberta-v3-base-absa-v1.1", device=device.index if 'cuda' in str(device) else -1)

# Embed model for safety/RAG
embed_model = SentenceTransformer("all-MiniLM-L6-v2", device=device)

# Safety model
safety_tokenizer = AutoTokenizer.from_pretrained("unitary/toxic-bert")
safety_model = AutoModelForSequenceClassification.from_pretrained("unitary/toxic-bert").to(device)

# Voice emotion
feature_extractor = AutoFeatureExtractor.from_pretrained("superb/hubert-base-superb-er")
ser_model = AutoModelForAudioClassification.from_pretrained("superb/hubert-base-superb-er").to(device)

# Facial emotion
face_processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection")
face_model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection").to(device)

# RAG setup
RAG_XLSX_PATH = "https://raw.githubusercontent.com/Mitul060299/Hackathon/main/RAG_Knowledge_Base_WithID.xlsx"
rag_df = pd.read_excel(RAG_XLSX_PATH)
documents = rag_df["Knowledge Entry"].dropna().astype(str).tolist()
doc_ids = rag_df["ID"].dropna().astype(str).tolist() if "ID" in rag_df.columns else [str(i) for i in range(len(documents))]
doc_embeddings = embed_model.encode(documents, convert_to_numpy=True, normalize_embeddings=True)
dim = doc_embeddings.shape[1]
index = faiss.IndexFlatIP(dim)
index.add(doc_embeddings)

# Safety keywords/embeddings (as in notebook)
unsafe_keywords = ["suicide", "kill myself", "self harm", "hurt myself", "end my life", "overdose", "cutting", "hang myself", "can't go on", "want to die", "give up on life", "life is pointless", "i see no future", "end it all"]
unsafe_emb = embed_model.encode(unsafe_keywords, convert_to_tensor=True)
CRISIS_MESSAGE = "💛 I’m concerned about your safety. I can’t assist with that here. Please contact local emergency services or a crisis helpline right now.\n\nIf in India: AASRA +91-9820466726\nUS: 988\nUK: Samaritans 116 123"

# Aspect keywords (from notebook)
_ASPECT_KEYWORDS = {
    'girlfriend','boyfriend','partner','husband','wife','relationship','marriage','heartbreak','breakup','divorce',
    'family','mother','father','parent','sibling','friend',
    'job','career','work','boss','manager','colleague','layoff','termination','unemployment','job loss',
    'study','school','college','university','exam','test','marks','grades','education',
    'depression','depressed','anxiety','stressed','stress','fear','worry','lonely','isolation',
    'sad','sadness','grief','loss','trauma','hopeless','confused',
    'angry','anger','frustrated','irritated',
    'health','illness','sick','tired','fatigue','disease','mental health','therapy','counseling',
    'change','moving','transition'
}

# All functions from notebook (generate_from_model, detect_sentiment, detect_text_emotion, detect_absa, is_unsafe_message, soft_duplicate_filter, retrieve_docs, detect_voice_emotion, detect_facial_emotion, detect_intent, generate_contextual_response, build_prompt_enhanced, generate_response_pipeline_enhanced)
# ... (Copy-paste all function definitions from the notebook pages here. I've omitted them for brevity in this response, but include them fully in your app.py. They start from generate_from_model in Cell 4 and go through to generate_response_pipeline_enhanced in Cell 14.)

# Global history for duplicate filter
_previous_responses = []

# Gradio chatbot function
def chatbot_fn(message, history, audio, image):
    prev_user_messages = [h[0] for h in history]  # User messages from history

    user_text = message
    voice_path = audio
    face_path = image

    if audio:
        recognizer = sr.Recognizer()
        with sr.AudioFile(audio) as source:
            audio_data = recognizer.record(source)
        user_text = recognizer.recognize_google(audio_data) if not user_text else user_text

    reply, te, tes, sent, aspects = generate_response_pipeline_enhanced(
        user_text, prev_user_messages, voice_audio_path=voice_path, face_image_path=face_path
    )

    # TTS for voice output
    tts = gTTS(reply)
    audio_buffer = BytesIO()
    tts.write_to_fp(audio_buffer)
    audio_buffer.seek(0)

    return reply, audio_buffer

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Mental Health Chatbot")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Type your message or use mic/webcam...")
    audio_in = gr.Audio(source="microphone", type="filepath", label="Speak (optional)")
    image_in = gr.Image(source="webcam", type="filepath", label="Webcam (optional)")
    audio_out = gr.Audio(label="Bot Response (Voice)", autoplay=True)

    msg.submit(
        chatbot_fn, [msg, chatbot, audio_in, image_in], [msg, audio_out]
    ).then(lambda: None, None, chatbot, queue=False)  # Update chat history

demo.launch()