Update app.py

app.py

@@ -11,7 +11,7 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 model.config.pad_token_id = tokenizer.pad_token_id
 
 # 2️⃣ Function to Generate Hindi Poetry
-def generate_poetry(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):
+def generate_poetry_base(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids
     with torch.no_grad():
         output = model.generate(
@@ -24,6 +24,25 @@ 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)
 
+def generate_poetry(prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.95):
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            max_length=max_length,
+            temperature=temperature,  # Increased randomness
+            top_p=top_p,
+            do_sample=True,
+            repetition_penalty=1.5,  # Added repetition penalty to prevent duplicates
+            num_beams=5,  # Use beam search for higher quality output
+            no_repeat_ngram_size=2,  # Prevent repeating the same n-grams
+            early_stopping=True,
+            pad_token_id=pad_token_id
+        )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+    
 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"])