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| from transformers import pipeline | |
| import pandas as pd | |
| from openai import OpenAI | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| from dotenv import dotenv_values | |
| #This is a model for a multi-label classification task that classifies text into different emotions. It works only in English. | |
| classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions") | |
| # This is a model for a translation task, designed to translate text. | |
| # We use it to translate any non-English text into English, so the classifier can then classify the emotions. | |
| translator = pipeline(task="translation", model="facebook/nllb-200-distilled-600M") | |
| languages = { | |
| "English": "eng_Latn", | |
| "French": "fra_Latn", | |
| "Arabic": "arb_Arab", | |
| "Spanish": "spa_Latn", | |
| "German": "deu_Latn", | |
| "Chinese (Simplified)": "zho_Hans", | |
| "Hindi": "hin_Deva" | |
| } | |
| # prepare openAI client with our api key | |
| env_values = dotenv_values("./app.env") | |
| client = OpenAI( | |
| api_key= env_values['OPENAI_API_KEY'],) | |
| # Create a DataFrame to store user entries and perform analysis. | |
| structure = { | |
| 'Date': [], | |
| 'Text': [], | |
| 'Mood': [] | |
| } | |
| df = pd.DataFrame(structure) | |
| # Take the text and its source language, translate it to English, so that the classifier can perform the task. | |
| def translator_text(text, src_lang): | |
| translation = translator(text, src_lang=src_lang, tgt_lang="eng_Latn") | |
| return translation[0]['translation_text'] | |
| # Take all the inputs from the user, including the mood (result from the classifier), and append them to the DataFrame. | |
| def appender(date, text, mood): | |
| global df | |
| new_row = pd.DataFrame({'Date': [date], 'Text': [text], 'Mood': [mood]}) | |
| df = pd.concat([df, new_row], ignore_index=True) | |
| def main(date, src_lang, text): | |
| # First: Translate the text to English if it is not already in English. | |
| if src_lang!= 'English': | |
| text = translator_text(text, languages[src_lang]) | |
| # Second : Classify the text | |
| mood = classifier(text)[0]['label'] | |
| # Third : Show a message to the user depending on how they feel. | |
| chat_completion = client.chat.completions.create( | |
| messages=[ | |
| { | |
| "role": "user", | |
| "content": f"I feel{mood}, can you tell me a message, without any introductory phrase, just the message itself.", | |
| } | |
| ], | |
| model="gpt-3.5-turbo", | |
| ) | |
| # Finally : Save to DataFrame | |
| appender(date, text, mood) | |
| #Highlighted the output utilizing 'HighlightedText' in gradio | |
| highlighted_mood = [(f"Today you're feeling", mood)] | |
| return highlighted_mood, chat_completion.choices[0].message.content | |
| #Interface | |
| demo = gr.Interface( | |
| fn=main, | |
| inputs=[gr.Textbox(label="Enter Date (YYYY-MM-DD)"), gr.Dropdown(choices=list(languages.keys()),label="Select a Language",value="English"), gr.Textbox(label="What's happened today?")], | |
| outputs=[gr.HighlightedText(label="Mood"), gr.Textbox(label="Message")], | |
| title = "Daily Journal", | |
| description=( | |
| "Capture your daily experiences, reflections, and insights in a personal journal.\n" | |
| "Log and monitor your mood daily to identify patterns and trends over time.\n" | |
| "Get inspirational or motivational messages each day." | |
| ), | |
| theme=gr.themes.Soft() # theme form gradio documentation | |
| ) | |
| demo.launch(debug=True) |