import streamlit as st
from transformers import pipeline
import google.generativeai as genai
import json
import random

# Load language configurations from JSON
with open('languages_config.json', 'r', encoding='utf-8') as f:
    LANGUAGES = json.load(f)['LANGUAGES']

# Load the JSON data for emotion templates
with open('emotion_templates.json', 'r') as f:
    data = json.load(f)
    
# Configure Gemini (replace with your API key or use environment variable)
# It's recommended to use st.secrets for API keys in Streamlit Cloud
# For local testing, you can keep it as is or load from an environment variable
# genai.configure(api_key=st.secrets["GEMINI_API_KEY"] if "GEMINI_API_KEY" in st.secrets else "YOUR_HARDCODED_API_KEY")
genai.configure(api_key="AIzaSyCYRYNwCU1f9cgJYn8pd86Xcf6hiSMwJr0")
model = genai.GenerativeModel('gemini-2.0-flash') 

# Use st.cache_resource to cache the loaded Hugging Face models
# This prevents reloading the model every time the app reruns (e.g., on user input)
@st.cache_resource
def load_emotion_classifier(model_name: str):
    """Loads and caches the Hugging Face emotion classifier pipeline."""
    st.spinner(f"Loading emotion detection model: {model_name}...")
    try:
        classifier = pipeline("text-classification", model=model_name)
        st.success(f"Model {model_name} loaded!")
        return classifier
    except Exception as e:
        st.error(f"Error loading model {model_name}: {e}")
        return None

def generate_text(prompt, context=""):
    """
    Generates text using the Gemini model.
    """
    try:
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        print(f"Error generating text: {e}")
        return "I am sorry, I encountered an error while generating the text."

def create_prompt(emotion, topic=None):
    """
    Chooses a random prompt from the template list.
    """
    templates = data["emotion_templates"][emotion]
    prompt = random.choice(templates)
    if topic:
        # Replace various placeholders in the prompt
        placeholders = ["[topic/person]", "[topic]", "[person]", "[object]", "[outcome]"]
        for placeholder in placeholders:
            prompt = prompt.replace(placeholder, topic)
    
    subfix_prompt = "Make the generated text in the same language as the topic.\n"
    subfix_prompt += "Make the generated text short.\n"
    
    prefix_prompt = "## topic\n" + topic
    prompt = subfix_prompt + prompt + prefix_prompt
    return prompt
    
# 2. Conversational Agent Logic
def get_ai_response(user_input, emotion_predictions):
    """Generates AI response based on user input and detected emotions."""
    dominant_emotion = None
    max_score = 0
    responses = None
    for prediction in emotion_predictions:
        if prediction['score'] > max_score:
            max_score = prediction['score']
            dominant_emotion = prediction['label']
    
    if dominant_emotion: # Ensure an emotion was detected
        prompt_text = create_prompt(dominant_emotion, user_input)
        responses = generate_text(prompt_text)
    else:
        responses = "I couldn't clearly detect a dominant emotion from your input."

    # Handle cases where no specific emotion is clear or generation failed
    if responses is None:
        return "I am sorry, I couldn't generate a response based on the detected emotion."
    else:
        return responses

# 3. Streamlit Frontend
def main():
    st.set_page_config(page_title="Emotion-Aware Chatbot", layout="centered")

    # --- Sidebar for Language and Model Selection ---
    with st.sidebar:
        st.header("Settings")
        
        # Language Selection
        selected_language = st.selectbox(
            "Select Interface Language",
            list(LANGUAGES.keys()),
            index=0  # Default to English
        )
        
        # Model Selection
        model_options = {
            "Multilingual GoEmotions (v1.0)": "AnasAlokla/multilingual_go_emotions",
            "Multilingual GoEmotions (v1.1)": "AnasAlokla/multilingual_go_emotions_V1.1"
        }
        
        selected_model_display_name = st.selectbox(
            "Select Emotion Detection Model",
            list(model_options.keys())
        )
        
        selected_model_path = model_options[selected_model_display_name]
        
        # Load the selected emotion classifier
        emotion_classifier = load_emotion_classifier(selected_model_path)
        
        if emotion_classifier is None:
            st.error("Emotion detection model could not be loaded. Please check your internet connection or try again.")
            return # Stop execution if model didn't load

    # --- Main Content Area ---
    # Display Image
    st.image('chatBot_image.jpg', caption="Emotion-Aware Chatbot", use_column_width=True)
    
    # Set page title and header based on selected language
    st.title(LANGUAGES[selected_language]['title'])
    st.write(LANGUAGES[selected_language]['description'])
    
    # Input Text Box
    user_input = st.text_input(
        LANGUAGES[selected_language]['input_placeholder'], 
        "",
        key="user_input_text" # Added a key for better re-rendering behavior
    )
    
    if user_input:
        if emotion_classifier: # Proceed only if model is loaded
            # Emotion Detection
            with st.spinner(LANGUAGES[selected_language]['detecting_emotion']):
                emotion_predictions = emotion_classifier(user_input)
            
            # Display Emotions
            st.subheader(LANGUAGES[selected_language]['emotions_header'])
            for prediction in emotion_predictions:
                st.write(f"- {prediction['label']}: {prediction['score']:.2f}")
            
            # Get AI Response
            with st.spinner(LANGUAGES[selected_language]['generating_response']):
                ai_response = get_ai_response(user_input, emotion_predictions)
            
            # Display AI Response
            st.subheader(LANGUAGES[selected_language]['response_header'])
            st.info(ai_response) # Using st.info for a distinct display
        else:
            st.warning("Cannot process input because the emotion detection model failed to load.")

# Run the main function
if __name__ == "__main__":
    main()