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| import streamlit as st | |
| import numpy as np | |
| import nltk | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| import pandas as pd | |
| 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 tempfile | |
| # Function to get credentials from environment variable and create a temporary file | |
| 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 | |
| # Set environment variable for Google application credentials | |
| 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') | |
| # Function to predict emotions using the custom trained model | |
| 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]: | |
| 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(" ") | |
| st.write(" ") | |
| st.header('Sentiment: Emotion Analyses', divider='rainbow') | |
| st.write('Write or paste any number of document texts to analyse the emotion percentage with your document') | |
| st.write(':sunglasses: :smile: :angry: :disappointed: :fearful: :rage: :weary: :cry: :sweat_smile: :neutral_face: :blush: :heart_eyes: :innocent: :satisfied: :joy:') | |
| # # Define the sample text | |
| # sample_text = ("Once, in a small village nestled in the rolling hills of Tuscany, lived an elderly woman named Isabella. " | |
| # "She had spent her entire life in this village, raising her children and caring for her garden, which was the most " | |
| # "beautiful in the region. Her husband, Marco, had passed away many years ago, leaving her with a heart full of memories " | |
| # "and a small, quaint house that overlooked the lush vineyards.") | |
| # # Add button to fill in sample text | |
| # if st.button("Use Sample Text"): | |
| # user_input = st.text_input(label="sample", value=sample_text, label_visibility="hidden") | |
| # else: | |
| option = st.selectbox( | |
| "How would you like to be contacted?", | |
| ("i hate that food", "happy day", "Mobile phone")) | |
| st.write("You selected:", option) | |
| user_input = "" | |
| if option: | |
| user_input = st.text_area('Enter Text to Analyze', value=option) | |
| else: | |
| user_input = st.text_area('Enter Text to Analyze') | |
| button = st.button("Analyze") | |
| if button and user_input: | |
| 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') | |
| 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) | |
| def get_emotion_heatmap(predictions): | |
| 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_heatmap = 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_heatmap) | |