Update app.py

app.py

@@ -1,38 +1,39 @@
-import os
 import gradio as gr
 from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
+import os
 
+# Define your model details
 HF_TOKEN = os.getenv("HF_TOKEN")
+model_name = "Jonathanmann/GPT2-medium-SADnov21"
 
-# Load the tokenizer and model explicitly as GPT2 to avoid config issues
-model_name = 'Jonathanmann/GPT2-medium-SADnov21'
+# Load tokenizer and model from Hugging Face
 tokenizer = GPT2Tokenizer.from_pretrained(model_name, use_auth_token=HF_TOKEN)
 model = GPT2LMHeadModel.from_pretrained(model_name, use_auth_token=HF_TOKEN)
 
 # Define the text generation pipeline
-generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 # Define a function for generating text
 def generate_text(prompt, max_length, temperature, top_k, top_p):
     response = generator(
-        prompt, 
+        prompt,
         max_length=max_length,
         temperature=temperature,
         top_k=top_k,
         top_p=top_p,
         num_return_sequences=1
     )
-    return response[0]['generated_text']
+    return response[0]["generated_text"]
 
-# Define the Gradio interface
+# Create the Gradio interface
 demo = gr.Interface(
     fn=generate_text,
     inputs=[
         gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
-        gr.Slider(minimum=20, maximum=200, default=50, label="Max Length"),
-        gr.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature"),
-        gr.Slider(minimum=1, maximum=100, default=50, label="Top-k"),
-        gr.Slider(minimum=0.1, maximum=1.0, default=0.9, label="Top-p")
+        gr.Slider(minimum=20, maximum=200, value=50, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
+        gr.Slider(minimum=1, maximum=100, value=50, label="Top-k"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, label="Top-p")
     ],
     outputs="text",
     title="GPT-2 Text Generation",