Create app.py

app.py

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+from flask import Flask, request, render_template
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load the fine-tuned model and tokenizer
+model_dir = "./finetune_model"
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
+model.eval()  # Set model to evaluation mode
+
+# Generate headline from article
+def generate_headline(article, max_length=128, num_beams=5):
+    # Tokenize the input article
+    inputs = tokenizer(article, max_length=256, truncation=True, return_tensors="pt", padding="max_length")
+
+    # Move inputs to the same device as the model
+    input_ids = inputs['input_ids'].to(model.device)
+    attention_mask = inputs['attention_mask'].to(model.device)
+
+    # Generate headline
+    outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=max_length, num_beams=num_beams, early_stopping=True)
+    headline = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return headline
+
+# Home route to render the form and handle POST requests
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    headline = None
+    if request.method == 'POST':
+        article = request.form.get('article')
+        if article:
+            headline = generate_headline(article)
+    return render_template('index.html', headline=headline)
+
+# Run the app
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)
+
+