Update app.py

app.py

@@ -1,16 +1,24 @@
-import torch
-import gradio as gr
+import streamlit as st
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Define the summarization pipeline
+summarizer_ntg = pipeline("summarization", model="mrm8488/t5-base-finetuned-summarize-news")
 
-# Step 1: Define Summarization Pipeline
-summarizer_ntg = pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-summarize-news")
+# Load the tokenizer and model for classification
+tokenizer_bb = AutoTokenizer.from_pretrained("your-username/your-model-name")
+model_bb = AutoModelForSequenceClassification.from_pretrained("your-username/your-model-name")
 
-# Step 2: Define Classification Pipeline
-tokenizer_bb = AutoTokenizer.from_pretrained("Lauraayu/News_Classi_Model")
-model_bb = AutoModelForSequenceClassification.from_pretrained("Lauraayu/News_Classi_Model")
+# Streamlit application title
+st.title("News Article Summarizer and Classifier")
+st.write("Enter a news article text to get its summary and category.")
 
-def summarize_and_classify(text):
-    # Summarize the article
+# Text input for user to enter the news article text
+text = st.text_area("Enter the news article text here:")
+
+# Perform summarization and classification when the user clicks the "Classify" button
+if st.button("Classify"):
+    # Perform text summarization
     summary = summarizer_ntg(text)[0]['summary_text']
     
     # Tokenize the summarized text
@@ -21,7 +29,7 @@ def summarize_and_classify(text):
     inputs = {k: v.to(device) for k, v in inputs.items()}
     model_bb.to(device)
     
-    # Perform classification
+    # Perform text classification
     with torch.no_grad():
         outputs = model_bb(**inputs)
     
@@ -30,16 +38,6 @@ def summarize_and_classify(text):
     label_mapping = model_bb.config.id2label
     predicted_label = label_mapping[predicted_label_id]
     
-    return summary, predicted_label
-
-# Create Gradio Interface
-iface = gr.Interface(
-    fn=summarize_and_classify,
-    inputs=gr.inputs.Textbox(lines=10, placeholder="Enter news article text here..."),
-    outputs=[gr.outputs.Textbox(label="Summary"), gr.outputs.Textbox(label="Category")],
-    title="News Article Summarizer and Classifier",
-    description="Enter a news article text and get its summary and category."
-)
-
-# Launch the interface
-iface.launch()
+    # Display the summary and classification result
+    st.write("Summary:", summary)
+    st.write("Category:", predicted_label)