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app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+# Load Hugging Face model
+model_name = "bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to get text embeddings
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.last_hidden_state[:, 0, :].numpy()
+
+# Sample dataset (sentiment analysis)
+texts = ["I love this!", "This is terrible.", "Fantastic experience!", "I hate it."]
+labels = [1, 0, 1, 0]  # 1 = Positive, 0 = Negative
+
+# Convert text to embeddings
+X = np.vstack([get_embedding(text) for text in texts])
+y = np.array(labels)
+
+# Train Logistic Regression model
+clf =

requirements.txt

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+transformers
+scikit-learn
+torch
+streamlit
+numpy