from typing import Dict, List, Union
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
import os
import re
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
import nltk
import json
import tempfile

# process of getting credentials
def get_credentials():
    creds_json_str = os.getenv("JSONSTR") # get json credentials stored as a string
    if creds_json_str is None:
        raise ValueError("GOOGLE_APPLICATION_CREDENTIALS_JSON not found in environment")

    # create a temporary file
    with tempfile.NamedTemporaryFile(mode="w+", delete=False, suffix=".json") as temp:
        temp.write(creds_json_str) # write in json format
        temp_filename = temp.name 

    return temp_filename
    
# pass
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]= get_credentials()



max_seq_length = 2048
dtype = None
load_in_4bit = True

# Check if 'punkt' is already downloaded, otherwise download it
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

text_split_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')


def predict_custom_trained_model_sample(
    project: str,
    endpoint_id: str,
    instances: Union[Dict, List[Dict]],
    location: str = "us-east4",
    api_endpoint: str = "us-east4-aiplatform.googleapis.com",
) -> List[str]:
    """
    `instances` can be either single instance of type dict or a list
    of instances.
    """
    client_options = {"api_endpoint": api_endpoint}
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    instances = instances if isinstance(instances, list) else [instances]
    instances = [
        json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
    ]
    parameters_dict = {}
    parameters = json_format.ParseDict(parameters_dict, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.predict(
        endpoint=endpoint, instances=instances, parameters=parameters
    )
    predictions_list = []
    predictions = response.predictions
    for prediction in predictions:
        if isinstance(prediction, str):
            clean_prediction = re.sub(r'(\n|Origin|###|Optimization|Response:)', '', prediction)
            split_predictions = clean_prediction.split()
            predictions_list.extend(split_predictions)
        else:
            print(" prediction (unknown type, skipping):", prediction)
    return [emotion for emotion in predictions_list if emotion in d_emotion.values()]


d_emotion = {0: 'admiration', 1: 'amusement', 2: 'anger', 3: 'annoyance', 4: 'approval', 5: 'caring', 6: 'confusion',
             7: 'curiosity', 8: 'desire', 9: 'disappointment', 10: 'disapproval', 11: 'disgust', 12: 'embarrassment',
             13: 'excitement', 14: 'fear', 15: 'gratitude', 16: 'grief', 17: 'joy', 18: 'love', 19: 'nervousness',
             20: 'optimism', 21: 'pride', 22: 'realization', 23: 'relief', 24: 'remorse', 25: 'sadness', 26: 'surprise',
             27: 'neutral'}

st.write("Write or paste any number of document texts to analyse the emotion percentage with your document")

user_input = st.text_area('Enter Text to Analyze')
button = st.button("Analyze")

if user_input and button:
    alpaca_prompt = """Below is a conversation between a human and an AI agent. write a response based on the input.
        ### Instruction:
        predict the emotion word or words
        ### Input:
        {}
        ### Response:
        """

    instances = []
    input_array = text_split_tokenizer.tokenize(user_input)
    for sentence in input_array:
        formatted_input = alpaca_prompt.format(sentence.strip())
        instance = {
            "inputs": formatted_input,
            "parameters": {
                "max_new_tokens": 4,
                "temperature": 0.00001,
                "top_p": 0.9,
                "top_k": 10
            }
        }
        instances.append(instance)

    predictions = predict_custom_trained_model_sample(
        project=os.environ["project"],
        endpoint_id=os.environ["endpoint_id"],
        location=os.environ["location"],
        instances=instances
    )

    emotion_counts = pd.Series(predictions).value_counts(normalize=True).reset_index()
    emotion_counts.columns = ['Emotion', 'Percentage']
    emotion_counts['Percentage'] *= 100  # Convert to percentage
    fig_pie = px.pie(emotion_counts, values='Percentage', names='Emotion', title='Percentage of Emotions in Given Text')
    fig_pie.update_traces(textposition='inside', textinfo='percent+label')

    @st.cache_data
    def get_emotion_chart(predictions):
        emotion_counts = pd.Series(predictions).value_counts().reset_index()
        emotion_counts.columns = ['Emotion', 'Count']
        fig_bar = go.Figure()
        fig_bar.add_trace(go.Bar(
            x=emotion_counts['Emotion'],
            y=emotion_counts['Count'],
            marker_color='indianred'
        ))
        fig_bar.update_layout(title='Count of Each Emotion in Given Text', xaxis_title='Emotion', yaxis_title='Count')
        return fig_bar

    fig_bar = get_emotion_chart(predictions)


    @st.cache_data
    def get_emotion_heatmap(predictions):
        # Create a matrix for heatmap
        
        # Count occurrences of each emotion
        emotion_counts = pd.Series(predictions).value_counts().reset_index()
        emotion_counts.columns = ['Emotion', 'Count']
        
        heatmap_matrix = pd.DataFrame(0, index=d_emotion.values(), columns=d_emotion.values())
        for index, row in emotion_counts.iterrows():
            heatmap_matrix.at[row['Emotion'], row['Emotion']] = row['Count']
    
        fig = go.Figure(data=go.Heatmap(
            z=heatmap_matrix.values,
            x=heatmap_matrix.columns.tolist(),
            y=heatmap_matrix.index.tolist(),
            text=heatmap_matrix.values,
            hovertemplate="Count: %{text}",
            colorscale='Viridis'
        ))
        fig.update_layout(title='Emotion Heatmap', xaxis_title='Predicted Emotion', yaxis_title='Predicted Emotion')
        return fig
        
    fig_dist = get_emotion_heatmap(predictions)
    
    tab1, tab2, tab3  = st.tabs(["Emotion Analysis", "Emotion Counts Distribution", "Heatmap"])
    with tab1:
        st.plotly_chart(fig_pie)
    with tab2:
        st.plotly_chart(fig_bar)
    with tab3:
        st.plotly_chart(fig_dist)