Update app.py

app.py

@@ -3,14 +3,29 @@ from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import torch
 
 # Initialisierung des Modells und des Tokenizers
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-model = GPT2LMHeadModel.from_pretrained("gpt2")
+tokenizer = GPT2Tokenizer.from_pretrained("Loewolf/GPT1")
+model = GPT2LMHeadModel.from_pretrained("Loewolf/GPT1")
 
-def generate_text(prompt):
+def generate_text(prompt, max_length, temperature, top_k, top_p):
     inputs = tokenizer.encode(prompt, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
+    outputs = model.generate(inputs, 
+                             max_length=max_length, 
+                             temperature=temperature, 
+                             top_k=top_k, 
+                             top_p=top_p, 
+                             num_return_sequences=1)
     text = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return text
 
-iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+iface = gr.Interface(
+    fn=generate_text, 
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Geben Sie einen Prompt ein..."), 
+        gr.inputs.Slider(minimum=10, maximum=100, default=50, label="Maximale Länge"),
+        gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.7, label="Temperatur"),
+        gr.inputs.Slider(minimum=0, maximum=50, default=20, label="Top K"),
+        gr.inputs.Slider(minimum=0.0, maximum=1.0, step=0.1, default=0.9, label="Top P")
+    ],
+    outputs="text"
+)
 iface.launch()