Upload app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import numpy as np
+
+# Load the model and tokenizer from Hugging Face
+model_name = "KevSun/IELTS_essay_scoring"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Streamlit app
+st.title("Automated Scoring IELTS App")
+st.write("Enter your IELTS essay below to predict scores from multiple dimensions:")
+
+# Input text from user
+user_input = st.text_area("Your text here:")
+
+if st.button("Predict"):
+    if user_input:
+        # Tokenize input text
+        inputs = tokenizer(user_input, return_tensors="pt")
+        
+        # Get predictions from the model
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Extract the predictions
+        predictions = outputs.logits.squeeze()
+
+        # Convert to numpy array if necessary
+        predicted_scores = predictions.numpy()
+        #predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        #predictions = predictions[0].tolist()
+        
+        # Convert predictions to a NumPy array for the calculations
+        #predictions_np = np.array(predictions)
+        
+        # Scale the predictions
+        scaled_scores = 2.25 * predicted_scores - 1.25
+        rounded_scores = [round(score * 2) / 2 for score in scaled_scores]  # Round to nearest 0.5
+
+        # Display the predictions
+        labels = ["Task Achievement", "Coherence and Cohesion", "Vocabulary", "Grammar", "Overall"]
+        for label, score in zip(labels, rounded_scores):
+            st.write(f"{label}: {score:}")
+    else:
+        st.write("Please enter some text to get scores.")