Update app.py

app.py

@@ -6,14 +6,11 @@ import json
 import random
 import gradio as gr
 import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader, Dataset
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import OneHotEncoder
-from deap import base, creator, tools, algorithms
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, AutoModelForSequenceClassification
+from deap import base, creator, tools, algorithms
 import gc
 
 warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download')
@@ -72,7 +69,7 @@ emotions = {
     'determination': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
     'resentment': {'percentage': 10, 'motivation': 'negative', 'intensity': 0},
     'glory': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
-    'motivation': {'percentage':    'motivation': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
+    'motivation': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
     'ideal_state': {'percentage': 100, 'motivation': 'balanced', 'intensity': 0},
     'fear': {'percentage': 10, 'motivation': 'defensive', 'intensity': 0},
     'surprise': {'percentage': 10, 'motivation': 'unexpected', 'intensity': 0},
@@ -163,7 +160,7 @@ def evolve_emotions():
                      (lambda: 100,), n=1)
     toolbox.register("population", tools.initRepeat, list, toolbox.individual)
     toolbox.register("mate", tools.cxTwoPoint)
-    toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2
+    toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
     toolbox.register("select", tools.selNSGA2)
     toolbox.register("evaluate", evaluate)
 
@@ -263,8 +260,7 @@ def optimize_ai_model(emotion_history):
 
     encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
     X = encoder.fit_transform(np.array(contexts).reshape(-1, 1))
-    y = np.array(pd.Categorical(emotions).
-    codes)
+    y = np.array(pd.Categorical(emotions).codes)
     clf = RandomForestClassifier(n_estimators=100)
     clf.fit(X, y)
 
@@ -330,6 +326,6 @@ interface = gr.Interface(respond_to_user, inputs="textbox", outputs="textbox", t
 interface.launch()
 
 # Clean up memory usage
-del finetuned_lm_tokenizer
-del finetuned_lm_model
+del _finetuned_lm_tokenizer
+del _finetuned_lm_model
 gc.collect()