import gradio as gr
from transformers import LlamaTokenizer, LlamaForCausalLM
import tempfile
import numpy as np

# Initialize LLaMA Model for Question Answering
llama_tokenizer = LlamaTokenizer.from_pretrained('huggingface/llama-7b')
llama_model = LlamaForCausalLM.from_pretrained('huggingface/llama-7b')

# Updated transcribe_and_predict_video function from your code
def transcribe_and_predict_video(video):
    # Process video frames for image-based emotion recognition
    image_emotion = process_video(video)
    
    # Process audio for text and audio-based emotion recognition
    text_emotion, audio_emotion = process_audio_from_video(video)
    
    # Determine the overall emotion (could be based on majority vote or some other logic)
    overall_emotion = Counter([text_emotion, audio_emotion, image_emotion]).most_common(1)[0][0]
    
    return overall_emotion

# Emotion-aware Question Answering with LLM
def emotion_aware_qa(question, video):
    # Get the emotion from the video (this uses the emotion detection you already implemented)
    detected_emotion = transcribe_and_predict_video(video)
    
    # Create a custom response context based on the detected emotion
    if detected_emotion == 'joy':
        emotion_context = "You're in a good mood! Let's keep the positivity going."
    elif detected_emotion == 'sadness':
        emotion_context = "It seems like you're feeling a bit down. Let me help with that."
    elif detected_emotion == 'anger':
        emotion_context = "I sense some frustration. Let's work through it together."
    elif detected_emotion == 'fear':
        emotion_context = "It sounds like you're anxious. How can I assist in calming things down?"
    elif detected_emotion == 'neutral':
        emotion_context = "You're feeling neutral. How can I help you today?"
    else:
        emotion_context = "You're in an uncertain emotional state. Let me guide you."
    
    # Prepare the prompt for LLaMA, including emotion context and user question
    prompt = f"{emotion_context} User asks: {question}"
    
    # Tokenize and generate response from LLaMA
    inputs = llama_tokenizer(prompt, return_tensors="pt")
    outputs = llama_model.generate(inputs['input_ids'], max_length=150)
    answer = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return answer

# Create Gradio interface to interact with the LLM and video emotion detection
def gradio_interface(question, video):
    response = emotion_aware_qa(question, video)
    return response

iface = gr.Interface(fn=gradio_interface, 
                     inputs=["text", gr.Video()], 
                     outputs="text", 
                     title="Emotion-Aware Question Answering",
                     description="Ask a question and get an emotion-aware response based on the video.")

iface.launch()