Create app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Load pre-trained model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create Streamlit app
+st.title("Hugging Face Transformers + Streamlit Example")
+
+# Define a function to use the model for prediction
+@st.cache(allow_output_mutation=True)
+def run_model(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = torch.softmax(outputs.logits, dim=1)
+    return predictions
+
+# Streamlit interface
+user_input = st.text_input("Enter text:", "Type Here...")
+if st.button("Predict"):
+    predictions = run_model(user_input)
+    st.write(f"Predictions: {predictions}")