Create app.py

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.", "Absolutely wonderful!", "Worst ever!"]
+labels = [1, 0, 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)
+
+# ✅ Fix: Assign Logistic Regression Model
+clf = LogisticRegression()  # This line was missing
+clf.fit(X, y)  # Train the model
+
+# Streamlit UI
+st.title("Sentiment Analysis with Hugging Face & Logistic Regression")
+st.write("Enter a sentence and the model will predict whether the sentiment is Positive or Negative.")
+
+# User input
+user_input = st.text_input("Enter your text here:")
+
+if user_input:
+    user_embedding = get_embedding(user_input)
+    prediction = clf.predict(user_embedding)
+    sentiment = "Positive 😊" if prediction[0] == 1 else "Negative 😡"
+    st.write(f"**Predicted Sentiment:** {sentiment}")