Main file updated

app.py

@@ -1,7 +1,27 @@
 import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-st.title('Hello, Streamlit!')
-st.write('This is a simple Streamlit app.')
+# Load the model and tokenizer
+model_name = "acorreal/phi3-project-management"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-if st.button('Say hello'):
-    st.write('Hello, World!')
+# Streamlit app
+st.title('Project Management Educational Tutor')
+st.write('This app uses the "acorreal/phi3-project-management" model')
+
+user_input = st.text_area("Enter your project management question or topic here:")
+
+if st.button('Get Response'):
+    if user_input:
+        inputs = tokenizer(user_input, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class_id = logits.argmax().item()
+
+        st.write(f"Predicted class ID: {predicted_class_id}")
+        # You can add more logic here to provide detailed responses based on the predicted_class_id
+    else:
+        st.write("Please enter a question or topic to get a response.")