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from transformers import (
AutoModelForSequenceClassification, # For text emotion detection model
pipeline, # For creating inference pipeline
PreTrainedTokenizerFast # For processing text input
)
from WebCam import capture_webcam
from colorama import Fore, Style # For colored console output
import pandas as pd # For data handling
import time
from datetime import datetime
import matplotlib.pyplot as plt
import gradio as gr
# Dictionaries to store emotion data over time
text_dataDict = {"Time": [], "Emotion": [], "Confidence Score": []}
face_dataDict = {"Time": [], "Emotion": [], "Confidence Score": []}
emotionDetectModel = AutoModelForSequenceClassification.from_pretrained("borisn70/bert-43-multilabel-emotion-detection") #to be replaced with my fine-tuned model once it is ready
tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
pipe = pipeline(task="text-classification", model=emotionDetectModel, tokenizer=tokenizer)
localFormat = "%Y-%m-%d %H:%M:%S" #this is how will print the timestamp: year-month-day hour-minutes-seconds (army time)
#currTime = datetime.now().astimezone().strftime(localFormat) this returns the time in the localFormat
#current_Time_Tuple = time.strptime(str(currTime), str(localFormat)) #creates a tuple that contains each part of the local format separate
#current_Time_In_Seconds = time.mktime(current_Time_Tuple) #converts the tuple into the number of seconds
def emotionAnalysis(message):
"""
Main function that processes both text and facial emotions
Args:
inp (str): User input text
Returns:
tuple: (str, plt) Contains the emotion results text and the updated plot
"""
if (message.lower() == "quit"):
return "Quitting...", displayResults()
# Process text emotion
result = pipe(message)
text_emotion = result[0]["label"]
text_score = result[0]["score"]
words_timestamp = datetime.now().astimezone().strftime(localFormat)
# Store text emotion data for plotting
text_dataDict["Time"].append(words_timestamp)
text_dataDict["Emotion"].append(text_emotion)
text_dataDict["Confidence Score"].append(round(text_score, 2))
# Capture and process facial emotion
face_emotion, face_score = capture_webcam()
face_timestamp = datetime.now().astimezone().strftime(localFormat)
# Store facial emotion data for plotting
face_dataDict["Time"].append(face_timestamp)
face_dataDict["Emotion"].append(face_emotion)
face_dataDict["Confidence Score"].append(face_score)
# Return both the text result and the updated plot
return f"Text: {text_emotion} | Face: {face_emotion}", displayResults()
def displayResults():
"""
Creates and returns a matplotlib plot showing emotion trends over time
Returns:
matplotlib.pyplot: Plot object showing emotion analysis results
"""
# Create a new figure with specified size
plt.figure(figsize=(10, 6))
# Set up plot labels and title
plt.title("Emotions Detected Through Facial Expressions and Text Over Time")
plt.xlabel("Time")
plt.ylabel("Emotions")
#plot facial emotions versus time where time is on the x-axis
plt.plot(face_dataDict["Time"], face_dataDict["Emotion"], marker='o', linestyle='-', label="Facial Emotions")
#plot facial emotions versus time where time is on the x-axis
plt.plot(text_dataDict["Time"], text_dataDict["Emotion"], marker='o', linestyle='-', color='red', label="Text Emotions")
#showing the graph and the legend
plt.legend()
plt.xticks(rotation=45) # Rotate timestamps for better readability
plt.tight_layout() # Adjust layout to prevent label cutoff
return plt
# Get user consent for webcam access
print(Fore.GREEN + "This program will analyze your text for emotions and use your webcame to detect your emotions from your face. Do you give consent? (yes/no): ")
consent = input()
if (consent.lower() == 'yes'):
# Create Gradio interface with both text output and plot visualization
interface = gr.Interface(
fn=emotionAnalysis,
inputs=["text"],
outputs=[
gr.Text(label="Emotion Results"), # Shows current emotion analysis
gr.Plot(label="Emotion Timeline") # Shows emotion trends over time
],
title="Emotion Analysis from Text and Face",
description="Enter text into the textbox. Then, press 'Submit' or 'Enter' to activate the webcam. Wait and see the results."
)
interface.launch() |