Update app.py

app.py

@@ -1,25 +1,107 @@
 import streamlit as st
+import pandas as pd
+from io import StringIO
+import csv
 from transformers import pipeline
 
 # Load the text classification model pipeline
 classifier = pipeline("text-classification", model="cychristophercyc/Group12_CustomModel_victory", return_all_scores=True)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 # Streamlit application title
-st.title("New Categorization")
-st.write("Classification for 6 categories: 	 Business and Finance, Health and Wellness, Sports, [Arts, Culture, and Entertainment],Politics, Science and Technology")
+st.title("News Categorization")
+st.write("Upload a CVS file containing content in 'content' column")
+
+
+genre = st.radio(
+    "What's your Chosen Category",
+    [":rainbow[Business and Finance]", 
+     ":rainbow[Health and Wellness]",
+     ":rainbow[Sports]",
+     ":rainbow[Arts, Culture, and Entertainment]",
+     ":rainbow[Politics]",
+     ":rainbow[Science and Technology]",
+     ],
+)
+
+if genre == ':rainbow[Business and Finance]':
+    st.write('You selected Business and Finance.')
+    select = "Business and Finance"
+    
+if genre == ':rainbow[Health and Wellness]':
+    st.write('You selected Health and Wellness.')
+    select = "Health and Wellness"
+    
+if genre == ':rainbow[Sports]':
+    st.write('You selected Sports.')
+     select = "Sports"
+    
+if genre == ':rainbow[Arts, Culture, and Entertainment]':
+    st.write('You selected Arts, Culture, and Entertainment.')
+    select = "Arts, Culture, and Entertainment"
+    
+if genre == ':rainbow[Politics]':
+    st.write('You selected Politics.')
+    select = "Politics"
+      
+if genre == ':rainbow[Science and Technology]':
+    st.write('You selected Science and Technology.')
+    select = "Science and Technology"
+
+
+# upload file 
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+    st.write(bytes_data)
+
+    # To convert to a string based IO:
+    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
+    st.write(stringio)
+
+    # To read file as string:
+    string_data = stringio.read()
+    st.write(string_data)
+
+    # Can be used wherever a "file-like" object is accepted:
+    dataframe = pd.read_csv(uploaded_file)
+    st.write(dataframe)
+
+a = 0
+content_list = []
+list_of_dictionaries =[]
+for row in dataframe:
+        # Extract the 'content' element and add it to the content_list
+        content_list.append(row['content'])
+        a+=1
+
 
-text = st.text_area("Enter the text to classify", "")
-# Perform text classification when the user clicks the "Classify" button
 if st.button("Classify"):
     # Perform text classification on the input text
-    
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+    for n in range(a):
+        results = classifier(content_list)[n]
+        # Display the classification result
+        max_score = float('-inf')
+        max_label = ''
+        for result in results:
+            if result['score'] > max_score:
+                max_score = result['score']
+                max_label = result['label']
+        st.write("Text:", content_list[n])
+        st.write("Label:", max_label)
+        st.write("Score:", max_score)
+        new_dictionary = {
+        "Text":content_list[n],
+        "Label" : max_label
+        }
+        list_of_dictionaries.append(new_dictionary)
+
+        
+filtered_list = [d for d in list_of_dictionaries if d['Label'].strip() == select]
+
+
+for m in len(filtered_list):
+    Sum = summarizer(content_list)[m]
+    st.write("Summary:", content_list)[m])