Create app.py

app.py

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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)