Update app.py

app.py

@@ -20,7 +20,7 @@ model = GPT2LMHeadModel.from_pretrained(model_name)
 # model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Ensure model uses the correct pad token
-model.config.pad_token_id = tokenizer.pad_token_id
+#model.config.pad_token_id = tokenizer.pad_token_id
 
 # 2️⃣ Function to Generate Hindi Poetry
 def generate_poetry_base(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):
@@ -55,12 +55,15 @@ def generate_poetry(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.9
     return tokenizer.decode(output[0], skip_special_tokens=True)
 
     
+
+
+# Poetry Generation Function with Random Selection from Dataset and Explicit 4-Line Structure
 def generate_random_poem(num_lines=4, max_length=150, temperature=1.0, top_p=0.9):
     # Randomly select a line from the dataset
     random_line = random.choice(dataset["poem"])
 
     # Prepare the input text with the random line selected, and start with a unique phrase to avoid repetition
-    input_text = f"{random_line} "  # Unique start to force variety
+    input_text = f"मैया मोरी {random_line} "  # Unique start to force variety
 
     # Tokenize the input text
     encoding = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
@@ -110,10 +113,14 @@ def generate_random_poem(num_lines=4, max_length=150, temperature=1.0, top_p=0.9
 
 
 
+
+
+
+
 # 3️⃣ Gradio Interface
 interface = gr.Interface(
-    fn=generate_poetry,
-    #fn=generate_random_poem(num_lines=4),
+    #fn=generate_poetry,
+    fn=generate_random_poem(num_lines=4),
     inputs=[
         gr.Textbox(label="Enter Prompt", placeholder="Start your Hindi poem..."),
         gr.Slider(50, 500, step=10, value=100, label="Max Length"),