Create app.py

app.py

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+import gradio as gr
+from datasets import load_dataset
+import pandas as pd
+
+# Load dataset
+dataset = load_dataset('dell-research-harvard/newswire', split='train')
+
+# Function to filter and match articles
+def filter_nobel_articles(laureates_file):
+    laureates_df = pd.read_csv(laureates_file.name)
+    nobel_articles = dataset.filter(lambda example: 'nobel' in example['text'].lower())
+    
+    def contains_laureate(article_text):
+        for _, row in laureates_df.iterrows():
+            if row['first_name'] in article_text and row['last_name'] in article_text:
+                return True
+        return False
+    
+    filtered_articles = nobel_articles.filter(lambda example: contains_laureate(example['text']))
+    return f"Found {len(filtered_articles)} articles mentioning Nobel laureates."
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=filter_nobel_articles,
+    inputs=gr.File(label="Upload Nobel Laureates CSV"),
+    outputs="text"
+)
+
+interface.launch()