Update app.py

app.py

@@ -1,26 +1,28 @@
 import gradio as gr
 from transformers import pipeline
 
-# Create a text-generation pipeline using GPT-2
-generator = pipeline('text-generation', model='gpt2')
+# Use a different language model: GPT-Neo (125M)
+generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
 
 def generate_text(prompt):
+    # Adjust parameters to reduce repetition and shorten output
     generated = generator(
         prompt,
         max_length=30,           # Limit the output length
-        do_sample=True,          # Enable sampling for more natural responses
-        temperature=0.3,         # Lower temperature for less randomness
-        repetition_penalty=1.5,  # Penalize repeated tokens
-        no_repeat_ngram_size=2   # Avoid repeating any 2-word sequences
+        do_sample=True,          # Enable sampling for varied responses
+        temperature=0.2,         # Lower temperature for less randomness
+        repetition_penalty=1.5,  # Penalize repetitive tokens
+        no_repeat_ngram_size=2   # Prevent repeating 2-word sequences
     )
     return generated[0]['generated_text']
 
+# Create the Gradio interface
 iface = gr.Interface(
     fn=generate_text,
     inputs="text",
     outputs="text",
-    title="Simple LLM with Hugging Face & Gradio",
-    description="Enter a prompt and get a concise, factual answer."
+    title="Simple LLM with GPT-Neo & Gradio",
+    description="Enter a prompt and get a concise response using GPT-Neo."
 )
 
 iface.launch()