Update app.py

app.py

@@ -1,35 +1,9 @@
-# import gradio as gr
-# from transformers import pipeline
-# model = pipeline("text-generation", model="DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")
-# def predict(prompt):
-#     completion = model(prompt)[0]["generated_text"]
-#     return completion
-
-# gr.Interface(fn=predict, inputs="text", outputs="text").launch(share=True)
-
 import gradio as gr
 from transformers import pipeline
-
-# Load the text generation model
 model = pipeline("text-generation", model="DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")
-
 def predict(prompt):
-    # Generate text with specified parameters for larger and philosophical answers
-    completions = model(
-        prompt,
-        repetition_penalty=2.0,  # Penalty for repetitions
-        no_repeat_ngram_size=2,   # Prevent repetition of bigrams
-        max_length=500,           # Increased maximum length for more extensive responses
-        num_return_sequences=3,   # Generate multiple sequences for variety
-        num_beams=5,              # Beam search for better quality
-        do_sample=True,           # Enable sampling for generation
-        temperature=0.7,          # Temperature for randomness in generation
-        top_k=50,                 # Limit sampling to top-K probabilities
-        top_p=0.95,               # Limit sampling to top-P probabilities (nucleus sampling)
-        truncation=True            # Truncate text if it exceeds max length
-    )[0]["generated_text"]
-    
-    return completions
+    completion = model(prompt)[0]["generated_text"]
+    return completion
+
+gr.Interface(fn=predict, inputs="text", outputs="text").launch(share=True)
 
-# Create a Gradio interface with improved layout options
-gr.Interface(fn=predict, inputs="text", outputs="text").launch(share=True)