added sentiment analysis

app.py

@@ -1,4 +1,79 @@
 import streamlit as st
+import random
+import pickle
+from sentiment import get_sentiment
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the data
+novel_list = pickle.load(open('D:/projects/Recon/data/novel_list.pkl', 'rb'))
+novel_list['english_publisher'] = novel_list['english_publisher'].fillna('unknown')
+name_list = novel_list['name'].values
+
+def recommend(novel, slider_start):
+    try:
+        similarity = pickle.load(open('D:/projects/Recon/data/similarity.pkl', 'rb'))
+        novel_index = novel_list[novel_list['name'] == novel].index[0]
+        distances = similarity[novel_index]
+        new_novel_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[slider_start:slider_start+9]
+    except IndexError:
+        return None
+
+    recommend_novel = [{'name': novel_list.iloc[i[0]]['name'], 'image_url': novel_list.iloc[i[0]]['image_url'], 'english_publisher': novel_list.iloc[i[0]]['english_publisher']} for i in new_novel_list]
+    return recommend_novel
+
+def main():
+    st.title("📚 Novel Recommender System")
+
+    # Input fields and buttons
+    selected_novel_name = st.text_input("🔎 Choose a Novel to get Recommendations", "Mother of Learning")
+    slider_value = st.slider("Slider", 1, 100, 1)
+
+    col1, col2, col3 = st.columns(3)  # Create three columns to place buttons side by side
+    with col1:
+        btn_recommend = st.button("💡 Recommend")
+    with col2:
+        btn_random = st.button("🎲 Random")
+    with col3:
+        btn_analysis = st.button("Analysis")
+
+    if btn_recommend:
+        recommendations = recommend(selected_novel_name, slider_value)
+        if recommendations:
+            for i in range(0, len(recommendations), 3):  # Process 3 recommendations at a time
+                cols = st.columns(3)
+                for j in range(3):
+                    if i + j < len(recommendations):
+                        novel = recommendations[i + j]
+                        with cols[j]:
+                            st.image(novel["image_url"], use_column_width=True)
+                            st.write(novel["name"])
+        else:
+            st.warning("Novel not found in our database. Please try another one.")
+    
+    if btn_random:
+        random_novels = random.sample(list(name_list), 9)
+        for i in range(0, len(random_novels), 3):
+            cols = st.columns(3)
+            for j in range(3):
+                if i + j < len(random_novels):
+                    novel_name = random_novels[i + j]
+                    novel_img = novel_list[novel_list['name'] == novel_name]['image_url'].values[0]
+                    with cols[j]:
+                        st.image(novel_img, use_column_width=True)
+                        st.write(novel_name)
+
+    if btn_analysis:
+        try:
+            positive, negative, wordcloud = get_sentiment(selected_novel_name)
+            st.write(f"😊 {positive}% Positive")
+            st.write(f"☹️ {negative}% Negative")
+            print(wordcloud)
+           
+           
+
+            st.image(wordcloud)
+
+        except Exception as e:
+            st.error("An error occurred during sentiment analysis.")
+    
+if __name__ == "__main__":
+    main()