Create app.py

app.py

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+#import streamlit as st
+#from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
+#import torch
+
+## Load the fine-tuned model and tokenizer
+#model_name = "fine-tuned-model"
+#model = DistilBertForSequenceClassification.from_pretrained(model_name)
+#tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+
+## Function to classify text
+#def classify_text(text):
+#    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+#    with torch.no_grad():
+#        outputs = model(**inputs)
+#    logits = outputs.logits
+#    predicted_class_id = torch.argmax(logits, dim=1).item()
+#    return "spam" if predicted_class_id == 1 else "ham"
+
+## Streamlit app
+#st.title("Text Message Classification")
+#st.write("Enter a text message and see if it's classified as spam or ham.")
+
+#user_input = st.text_area("Text Message", "")
+#if st.button("Classify"):
+#    if user_input:
+#        prediction = classify_text(user_input)
+#        st.write(f"The message is classified as: \n **{prediction}**")
+#    else:
+#        st.write("Please enter a text message.")
+import streamlit as st
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
+import torch
+
+# Load the fine-tuned model and tokenizer
+model_name = "fine-tuned-model"
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+
+# Function to classify text
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = torch.argmax(logits, dim=1).item()
+    return "spam" if predicted_class_id == 1 else "ham"
+
+# Streamlit app
+st.set_page_config(page_title="Text Message Classification", page_icon="📧")
+
+# Header
+st.title("📧 Text Message Classification")
+
+# Text input area
+#st.subheader("Enter a Text Message:")
+user_input = st.text_area("Type your message here...", height=50)
+
+# Classify button and result display
+if st.button("Classify"):
+    if user_input:
+        prediction = classify_text(user_input)
+        if prediction == "ham":
+            st.success(f"The message is classified as: **{prediction}**")
+        else:
+            st.error(f"The message is classified as: **{prediction}**")
+    else:
+        st.warning("Please enter a text message.")
+
+# Footer
+st.markdown("""
+    ---
+    Built with ❤️ using [Streamlit](https://streamlit.io/) and [Transformers](https://huggingface.co/transformers/).
+    """)