Update app.py

app.py

@@ -10,17 +10,14 @@ tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
 def generate_tweet(label):
-    # Updated to generate a promotional tweet without mentioning emails and websites
-    prompt = f"Write a short post about {label}"
-    
+    # Craft a prompt that naturally encourages engaging and relevant tweet content
+    prompt = f"Imagine you're at an event celebrating {label}. What's something exciting you'd share about it on social media?"
+
     inputs = tokenizer.encode(prompt, return_tensors="pt")
     outputs = model.generate(inputs, max_length=280, num_return_sequences=1, no_repeat_ngram_size=2)
-    
-    tweet_raw = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    # Process the generated tweet to remove unwanted patterns (additional processing can be added if needed)
-    tweet = tweet_raw.replace("Write a short post about", "")  # Ensure this phrase is not included
-    
+
+    tweet = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
     return tweet
 
 def predict(image):