Update app.py

app.py

@@ -1,128 +1,127 @@
-# import libraries
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import joblib
-from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
-from sklearn.model_selection import train_test_split
-
-#load model
-import joblib
-
-@st.cache_resource  # Caches the model in Streamlit's memory
-def load_model():
-    return joblib.load("SA_model.pkl")  # Ensure your model is saved and available
-
-model = load_model()
-
-
-
-#load dataset
-df = pd.read_csv('Tweets.csv', encoding='utf-8')
-X = df['text']
-y = df['airline_sentiment']
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y , test_size=0.33, random_state=42
-)
-
-
-#compute sentiment
-class_report_data = {
-    "Precision": [0.67, 0.51, 0.88],
-    "Recall": [0.73, 0.64, 0.79],
-    "F1-score": [0.70, 0.57, 0.83]
-}
-
-# Properly structured DataFrame
-class_report_df = pd.DataFrame(class_report_data, index=["Positive", "Neutral", "Negative"])
-
-#predict text sentiment
-def predict_sentiment(text):
-    if isinstance(text, str):
-        text = [text]  # Ensure input is a list
-
-    prediction = model.predict(text)  # Get numerical prediction
-
-    # Mapping numerical labels to sentiment categories
-    sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
-
-    return sentiment_mapping.get(prediction[0], "Unknown")  # Convert number to label
-
-
-
-#Model Introduction
-st.title('📌 Sentiment Analysis Web Application')
-
-st.markdown(
-    """
-    ## 📖 Introduction
-    Welcome to the **Sentiment Analysis Web Application**! This tool is designed to analyze the sentiment of text messages 
-    using a **Support Vector Machine (SVM) model**. The model has been trained on the **Airline Tweets dataset from Kaggle** 
-    and classifies text into three sentiment categories:
-    - ✅ **Positive**
-    - ❌ **Negative**
-    - ⚖ **Neutral**
-    """
-)
-
-#Tab Structuring
-
-tab1, tab2, tab3 = st.tabs(['📊 Dataset Preview', '📈 Model Performance', '🔍 Sentiment Prediction'])
-
-with tab1:
-    st.markdown(
-        """
-        ## 📊 Dataset Preview
-        The dataset used for training this model consists of tweets related to airline services. Each tweet is labeled 
-        with one of the three sentiment categories (**Positive, Negative, or Neutral**). Below is a sample of the dataset:
-        """
-    )
-    st.write (df)
-
-
-with tab2:
-    st.markdown(
-        """
-        ## 📈 Model Performance
-        Below are the key performance metrics of the trained **Support Vector Machine (SVM)** model:
-        
-        - **Model Accuracy**: The percentage of correctly classified instances.
-        - **Classification Report**: Includes precision and recall for each sentiment class.
-        - **Confusion Matrix**: A visual representation comparing actual versus predicted classifications.
-        """
-    )
-    
-    st.write(f"**📌 Model Accuracy:** 75%")
-    
-    st.markdown("### 📋 Classification Report")
-    st.dataframe(class_report_df)
-    
-    st.markdown("### 🔢 Confusion Matrix")
-    
-    # Load and display confusion matrix image
-    cm_image_path = "cmap.png"
-    try:
-        st.image(cm_image_path, caption="Confusion Matrix", use_container_width=True)
-    except FileNotFoundError:
-        st.warning("⚠️ Confusion matrix image not found. Please check the file path.")
-
-
-
-
-with tab3:
-    st.markdown(
-        """
-        ## 🔍 Sentiment Prediction
-        Type a sentence in the text box below, and the model will classify it as **Positive, Neutral, or Negative**.
-        """
-    )
-
-    user_input = st.text_area("Type your sentence here:", "")
-
-    if st.button("🔎 Analyze Sentiment"):
-        if user_input.strip():
-            sentiment_result = predict_sentiment(user_input)
-            st.success(f"### 🎯 Prediction: **{sentiment_result}**")
-        else:
-            st.warning("⚠️ Please enter a valid text input.")
+# import libraries
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import joblib
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+from sklearn.model_selection import train_test_split
+
+#load model
+import joblib
+
+@st.cache_resource  # Caches the model in Streamlit's memory
+def load_model():
+    return joblib.load("SA_model.pkl")  # Ensure your model is saved and available
+
+model = load_model()
+
+
+
+#load dataset
+df = pd.read_csv('Tweets.csv', encoding='utf-8')
+X = df['text']
+y = df['airline_sentiment']
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y , test_size=0.33, random_state=42
+)
+
+
+#compute sentiment
+class_report_data = {
+    "Precision": [0.67, 0.51, 0.88],
+    "Recall": [0.73, 0.64, 0.79],
+    "F1-score": [0.70, 0.57, 0.83]
+}
+
+# Properly structured DataFrame
+class_report_df = pd.DataFrame(class_report_data, index=["Positive", "Neutral", "Negative"])
+
+#predict text sentiment
+def predict_sentiment(text):
+    if isinstance(text, str):
+        text = [text]  # Ensure input is a list
+
+    prediction = model.predict(text)  # Get numerical prediction
+
+    # Mapping numerical labels to sentiment categories
+    sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
+
+    return sentiment_mapping.get(prediction[0], "Unknown")  # Convert number to label
+
+
+
+#Model Introduction
+st.title('📌 Sentiment Analysis Web Application')
+
+st.markdown(
+    """
+    ## 📖 Introduction
+    Welcome to the **Sentiment Analysis Web Application**! This tool is designed to analyze the sentiment of text messages 
+    using a **Support Vector Machine (SVM) model**. The model has been trained on the **Airline Tweets dataset from Kaggle** 
+    and classifies text into three sentiment categories:
+    - ✅ **Positive**
+    - ❌ **Negative**
+    - ⚖ **Neutral**
+    """
+)
+
+#Tab Structuring
+
+tab1, tab2, tab3 = st.tabs(['📊 Dataset Preview', '📈 Model Performance', '🔍 Sentiment Prediction'])
+
+with tab1:
+    st.markdown(
+        """
+        ## 📊 Dataset Preview
+        The dataset used for training this model consists of tweets related to airline services. Each tweet is labeled 
+        with one of the three sentiment categories (**Positive, Negative, or Neutral**). Below is a sample of the dataset:
+        """
+    )
+    st.write (df)
+
+
+with tab2:
+    st.markdown(
+        """
+        ## 📈 Model Performance
+        Below are the key performance metrics of the trained **Support Vector Machine (SVM)** model:
+        
+        - **Model Accuracy**: The percentage of correctly classified instances.
+        - **Classification Report**: Includes precision and recall for each sentiment class.
+        - **Confusion Matrix**: A visual representation comparing actual versus predicted classifications.
+        """
+    )
+    
+    st.write(f"**📌 Model Accuracy:** 75%")
+    
+    st.markdown("### 📋 Classification Report")
+    st.dataframe(class_report_df)
+    
+    st.markdown("### 🔢 Confusion Matrix")
+    
+    # Load and display confusion matrix image
+    try:
+        st.image("/cmap.png", caption="Confusion Matrix", use_container_width=True)
+    except FileNotFoundError:
+        st.warning("⚠️ Confusion matrix image not found. Please check the file path.")
+
+
+
+
+with tab3:
+    st.markdown(
+        """
+        ## 🔍 Sentiment Prediction
+        Type a sentence in the text box below, and the model will classify it as **Positive, Neutral, or Negative**.
+        """
+    )
+
+    user_input = st.text_area("Type your sentence here:", "")
+
+    if st.button("🔎 Analyze Sentiment"):
+        if user_input.strip():
+            sentiment_result = predict_sentiment(user_input)
+            st.success(f"### 🎯 Prediction: **{sentiment_result}**")
+        else:
+            st.warning("⚠️ Please enter a valid text input.")