Update app.py

app.py

@@ -1,25 +1,41 @@
+import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import os
+import torch
 
-# Define the model name (replace with your actual model name)
-model_name = "huggingface/transformers"  # Example model name
+# Sidebar for user input
+st.sidebar.header("Model Configuration")
+model_name = st.sidebar.text_input("Enter model name", "huggingface/transformers")
 
-# Load the tokenizer and model
-try:
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    print("Model and tokenizer loaded successfully!")
-except Exception as e:
-    print(f"Error loading model: {e}")
+# Load model and tokenizer on demand
+@st.cache_resource
+def load_model(model_name):
+    try:
+        # Load the model and tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        return tokenizer, model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None, None
 
-# Add your app logic here (e.g., for inference, etc.)
-def predict(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(**inputs)
-    return outputs
+# Load the model and tokenizer
+tokenizer, model = load_model(model_name)
+
+# Input text box in the main panel
+st.title("Text Classification with Hugging Face Models")
+user_input = st.text_area("Enter text for classification:")
+
+# Make prediction if user input is provided
+if user_input and model and tokenizer:
+    inputs = tokenizer(user_input, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Display results (e.g., classification logits)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    st.write(f"Predicted Class: {predicted_class}")
+    st.write(f"Logits: {logits}")
+else:
+    st.info("Please enter some text to classify.")
 
-# Example usage
-if __name__ == "__main__":
-    test_text = "Hello, world!"
-    result = predict(test_text)
-    print(result)