Delete evaluate.py

evaluate.py

@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import TFBertForSequenceClassification, BertTokenizer
-import tensorflow as tf
-import praw
-import os
-import pytesseract
-from PIL import Image
-import cv2
-import numpy as np
-import re
-
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-from scipy.special import softmax
-import matplotlib.pyplot as plt
-import pandas as pd
-def get_classification_report():
-    from sklearn.metrics import classification_report
-    import pandas as pd
-
-    # Load your test data
-    df = pd.read_csv("test.csv")
-    texts = df["text"].tolist()
-    true_labels = df["label"].tolist()
-
-    # Load tokenizer and model
-    #tokenizer = AutoTokenizer.from_pretrained("Shrish/mbert-sentiment")
-    #model = TFAutoModelForSequenceClassification.from_pretrained("Shrish/mbert-sentiment")
-    fallback_model_name = "cardiffnlp/twitter-roberta-base-sentiment"
-    tokenizer = AutoTokenizer.from_pretrained(fallback_model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(fallback_model_name)
-
-    # Tokenize and predict
-    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="tf")
-    outputs = model(inputs)
-    predictions = tf.math.argmax(outputs.logits, axis=1).numpy()
-
-    # Generate report
-    report = classification_report(true_labels, predictions, target_names=["negative", "neutral", "positive"])
-    return report