Delete README.md

README.md

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-import gradio as gr
-import torch
-import joblib
-import numpy as np
-import pandas as pd
-from transformers import AutoTokenizer, AutoModel
-
-# Load IndoBERT tokenizer
-tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased")
-
-# Load IndoBERT model
-model = AutoModel.from_pretrained("indolem/indobert-base-uncased")
-
-# Mapping dictionaries for labels
-priority_score_mapping = {1: "LOW", 2: "MEDIUM", 3: "HIGH"}
-problem_domain_mapping = {0: "OPERATIONAL", 1: "TECHNICAL"}
-
-# Load the trained Random Forest models
-best_classifier1 = joblib.load('best_classifier1_optimized.pkl')
-best_classifier2 = joblib.load('best_classifier2_optimized.pkl')
-
-markdown_text = '''
-## Label Description
-### Priority Score
-* **Low** label, means that the temporary/corrective solution can solve the problem. A permanent solution will be provided later because the impact on the business can still be handled.
-* **Medium** label, means that there's a need to determine the time constraint to solve the problem. If it remains too long, it will impact the business side.
-* **High** label, means that the problem is urgent and must be solved immediately.
-### Problem Domain
-* **Operational** label, means that the scope of the problem is on the business or daily operational.
-* **Technical** label, means that the scope of the problem is on the technical (technology) side like the mobile/web application.
-'''
-
-description="Write the feedback about the capsule hotel that you've ever visited or stayed there. The machine learning model will predict the priority score and problem domain of the feedback."
-
-# Function to perform predictions
-def predict(text):
-    # Convert the sentences into input features
-    encoded_inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt", max_length=128)
-
-    # Perform word embedding using IndoBERT model
-    with torch.no_grad():
-      outputs = model(**encoded_inputs)
-      embeddings = outputs.last_hidden_state
-
-    # Convert the embeddings to numpy array
-    embeddings = embeddings.numpy()
-
-    embeddings_custom_flat = embeddings.reshape(embeddings.shape[0], -1)
-
-    # Ensure mean_pooled_embeddings has exactly 768 features
-    num_features_expected = 768
-    if embeddings_custom_flat.shape[1] < num_features_expected:
-      # If the number of features is less than 768, pad the embeddings
-      pad_width = num_features_expected - embeddings_custom_flat.shape[1]
-      embeddings_custom_flat = np.pad(embeddings_custom_flat, ((0, 0), (0, pad_width)), mode='constant')
-
-    elif embeddings_custom_flat.shape[1] > num_features_expected:
-      # If the number of features is more than 768, truncate the embeddings
-      embeddings_custom_flat = embeddings_custom_flat[:, :num_features_expected]
-
-    # Predict the priority_score for the custom input
-    custom_priority_score = best_classifier1.predict(embeddings_custom_flat)
-
-    # Predict the problem_domain for the custom input
-    custom_problem_domain = best_classifier2.predict(embeddings_custom_flat)
-
-    # Map numerical labels to human-readable labels
-    mapped_priority_score = priority_score_mapping.get(custom_priority_score[0], "unknown")
-    mapped_problem_domain = problem_domain_mapping.get(custom_problem_domain[0], "unknown")
-
-    return f"Predicted Priority Score: {mapped_priority_score}, Predicted Problem Domain: {mapped_problem_domain}"
-
-
-# Create a Gradio interface
-gr.Interface(fn=predict, inputs="text", outputs="text", title="Simple Risk Classifier Demo (Case Study: Capsule Hotel)", description=description, article=markdown_text).launch(debug=True)