Create app.py

app.py

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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()