requirements.txt

transformers
gradio
torch

app.py

@@ -0,0 +1,38 @@
+from transformers import pipeline
+import gradio as gr
+
+# Load summarization models
+models = {
+    "BART Large CNN": pipeline("summarization", model="facebook/bart-large-cnn"),
+    "T5 Small": pipeline("summarization", model="t5-small")
+}
+
+# Sentiment analysis pipeline
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+def summarize_compare(text):
+    summaries = {}
+    sentiments = {}
+    
+    for model_name, summarizer in models.items():
+        summary = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
+        sentiment = sentiment_analyzer(summary)[0]
+        summaries[model_name] = summary
+        sentiments[model_name] = f"{sentiment['label']} ({round(sentiment['score'], 2)})"
+    
+    return summaries["BART Large CNN"], sentiments["BART Large CNN"], summaries["T5 Small"], sentiments["T5 Small"]
+
+demo = gr.Interface(
+    fn=summarize_compare,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your text here..."),
+    outputs=[
+        gr.Textbox(label="BART Large CNN Summary"),
+        gr.Textbox(label="BART Large CNN Sentiment"),
+        gr.Textbox(label="T5 Small Summary"),
+        gr.Textbox(label="T5 Small Sentiment")
+    ],
+    title="Multi-Model Text Summarizer + Sentiment Analyzer",
+    description="Compare summaries from multiple models and see the sentiment of each summary."
+)
+
+demo.launch()