Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,83 +1,55 @@
-# import streamlit as st
-# import numpy as np
-# from tensorflow.keras.models import load_model
-# import joblib
-
-# st.title('Sentiment Analysis Prediction')
-
-# # Load the model and preprocessor
-# try:
-#     model = load_model('src/cnn_model.keras')
-#     preproc = joblib.load('src/preproc.joblib')
-# except Exception as e:
-#     st.error(f"Error loading model or preprocessor: {e}")
-
-# # Input field for text
-# text_input = st.text_area('Enter text for sentiment analysis:', '')
-
-# if st.button('Predict Sentiment'):
-#     if not text_input.strip():
-#         st.warning('Please enter some text.')
-#     else:
-#         try:
-#             # Preprocess input text
-#             X = preproc.transform([text_input])
-#             # Predict sentiment
-#             prediction = model.predict(X)
-#             # Assuming binary classification: 0=Negative, 1=Positive
-#             sentiment = 'Positive' if prediction[0][0] > 0.5 else 'Negative'
-#             st.success(f'Prediction: {sentiment} (score: {prediction[0][0]:.2f})')
-#         except Exception as e:
-#             st.error(f"Error making prediction: {e
-
 
 import streamlit as st
 import numpy as np
 from tensorflow.keras.models import load_model
 import joblib
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 st.title('Sentiment Analysis Prediction')
 
+# Initialize model and preproc as None
+model = None
+preproc = None
+
 # Load the model and preprocessor
 try:
-    model = load_model('src/cnn_model.keras')
-    preproc = joblib.load('src/preproc.joblib')
+    model = load_model('Sentiment Analyis/cnn/cnn_model.keras')
+    preproc = joblib.load('Sentiment Analyis/cnn/preproc.joblib')
 except Exception as e:
     st.error(f"Error loading model or preprocessor: {e}")
 
 text_input = st.text_area('Enter text for sentiment analysis:', '')
 
+# Preprocess function with padding to match model input shape
+MAXLEN = 60  # Change this if your model expects a different input length
+
 def preprocess_text(text, preproc):
-    # If preproc is a dict, try common keys
     if isinstance(preproc, dict):
         if 'tokenizer' in preproc:
-            # Keras Tokenizer
             tokenizer = preproc['tokenizer']
             seq = tokenizer.texts_to_sequences([text])
-            # Pad sequences if needed
-            if 'padder' in preproc:
-                padder = preproc['padder']
-                seq = padder(seq)
-            return np.array(seq)
+            seq = pad_sequences(seq, maxlen=MAXLEN)
+            return seq
         elif 'vectorizer' in preproc:
-            # scikit-learn TfidfVectorizer or similar
             vectorizer = preproc['vectorizer']
             return vectorizer.transform([text])
         else:
             raise ValueError("Unknown preprocessor dict keys.")
     else:
-        # If preproc is a single object
         if hasattr(preproc, 'transform'):
             return preproc.transform([text])
         elif hasattr(preproc, 'texts_to_sequences'):
             seq = preproc.texts_to_sequences([text])
-            return np.array(seq)
+            seq = pad_sequences(seq, maxlen=MAXLEN)
+            return seq
         else:
             raise ValueError("Unknown preprocessor type.")
 
 if st.button('Predict Sentiment'):
     if not text_input.strip():
         st.warning('Please enter some text.')
+    elif model is None or preproc is None:
+        st.error('Model or preprocessor not loaded. Please check the files and try again.')
     else:
         try:
             X = preprocess_text(text_input, preproc)
@@ -85,4 +57,4 @@ if st.button('Predict Sentiment'):
             sentiment = 'Positive' if prediction[0][0] > 0.5 else 'Negative'
             st.success(f'Prediction: {sentiment} (score: {prediction[0][0]:.2f})')
         except Exception as e:
-            st.error(f"Error making prediction: {e}")
+            st.error(f"Error making prediction: {e}")