Add error handling for model loading and improve analysis feedback

app.py

@@ -8,8 +8,12 @@ x = st.slider('Select a value')
 st.write(x, 'squared is', x * x)
 # load the model and cache it
 model, label_encoder, tokenizer = load_model_for_prediction()
+if model is None or label_encoder is None or tokenizer is None:
+    st.error("Failed to load model components")
+    st.stop()
+
 st.session_state['model'] = model
 st.session_state['label_encoder'] = label_encoder
 st.session_state['tokenizer'] = tokenizer
-st.write('Model loaded')
+st.success('Model loaded successfully')
 

pages/Analysis.py

@@ -42,43 +42,52 @@ def split_sentences_with_abbrev(text):
 
 def show_analysis():
     st.title("Text Analysis")
-    st.write("Use this section to analyze the logical structure of your text.")
     
-    try:
-        if 'model' not in st.session_state:
-            st.error("Please initialize the model from the home page first.")
-            return
-            
-        model = st.session_state.model
-        label_encoder = st.session_state.label_encoder
-        tokenizer = st.session_state.tokenizer
+    # Check model loading state
+    if not all(key in st.session_state for key in ['model', 'label_encoder', 'tokenizer']):
+        st.warning("Model components not found in session state")
+        st.info("Please go to the Home page first to load the model")
+        return
         
-        # Text input section
-        st.header("Analyze Your Text")
-        user_text = st.text_area("Enter your text here (multiple sentences allowed):", height=150)
+    if any(st.session_state[key] is None for key in ['model', 'label_encoder', 'tokenizer']):
+        st.error("One or more model components failed to load")
+        return
         
-        if st.button("Analyze"):
-            if user_text:
-                # Split and analyze sentences
+    # Get model components
+    model = st.session_state.model
+    label_encoder = st.session_state.label_encoder
+    tokenizer = st.session_state.tokenizer
+    
+    # Text input section
+    st.header("Analyze Your Text")
+    user_text = st.text_area("Enter your text here (multiple sentences allowed):", height=150)
+    
+    if st.button("Analyze"):
+        if not user_text:
+            st.warning("Please enter some text to analyze")
+            return
+            
+        with st.spinner("Analyzing text..."):
+            try:
+                # Split text into sentences
                 sentences = split_sentences_regex(user_text)
+                results = []
                 
-                st.subheader("Analysis Results:")
-                for i, sentence in enumerate(sentences, 1):
-                    with st.container():
-                        label, confidence = predict_sentence(
-                            model, sentence, tokenizer, label_encoder
-                        )
-                        if label not in ("Unknown", "Error"):
-                            st.write("---")
-                            st.write(f"**Sentence:** {sentence}")
-                            st.write(f"**Predicted:** {label}")
-                            st.progress(confidence)
-            else:
-                st.warning("Please enter some text to analyze.")
-        
+                # Process each sentence
+                for sentence in sentences:
+                    label, confidence = predict_sentence(model, sentence, tokenizer, label_encoder)
+                    results.append({
+                        "Sentence": sentence,
+                        "Label": label,
+                        "Confidence": f"{confidence:.2%}"
+                    })
+                
+                # Display results
+                df = pd.DataFrame(results)
+                st.dataframe(df)
                 
-    except Exception as e:
-        st.error(f"Error: {str(e)}")
+            except Exception as e:
+                st.error(f"Analysis failed: {str(e)}")
 
 if __name__ == "__main__":
     show_analysis()