Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load pre-trained model (or fine-tuned model)
-model_name = "Manasa1/GPT_Finetuned_tweets"  # Replace with the fine-tuned model name
+model_name = "/kaggle/working/gpt-finetuned-qa"  # Replace with the fine-tuned model name
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
@@ -13,8 +13,17 @@ def generate_tweet(input_text):
               "Ensure the response is concise, engaging, and suitable for a diverse audience on social media. "
               "Incorporate elements of thought leadership, futuristic perspectives, and practical wisdom where appropriate.").format(input_text)
 
-    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(inputs['input_ids'], max_length=280, num_return_sequences=1, top_p=0.95, top_k=50)
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True, padding=True)
+    outputs = model.generate(
+        inputs['input_ids'],
+        attention_mask=inputs['attention_mask'],
+        max_length=280,
+        num_return_sequences=1,
+        top_p=0.95,
+        top_k=50,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id
+    )
     generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     # Extract the tweet text (exclude prompt if included)
@@ -41,4 +50,4 @@ def main():
 # Run Gradio app
 if __name__ == "__main__":
     app = main()
-    app.launch()
+    app.launch(share=True)