Update app.py

app.py

@@ -1,28 +1,18 @@
 
-import streamlit as st
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-@st.cache(allow_output_mutation=True)
-def load_model():
-    MODEL_NAME = "gpt2"  # Ändern Sie dies entsprechend
-    tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
-    model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)
-    return model, tokenizer
+class EinfachPrompt:
+    def __init__(self):
+        self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+        self.model = GPT2LMHeadModel.from_pretrained("gpt2")
 
-def generate_text(prompt, model, tokenizer):
-    inputs = tokenizer.encode(prompt, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=200, num_return_sequences=5)
-    generated_text = [tokenizer.decode(output) for output in outputs]
-    return generated_text
+    def generate(self, prompt):
+        inputs = self.tokenizer.encode(prompt, return_tensors="pt")
+        outputs = self.model.generate(inputs, max_length=150, num_return_sequences=1, temperature=0.7)
+        generated = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated
 
-model, tokenizer = load_model()
-
-st.title("Textgenerierung mit GPT-2")
-prompt = st.text_input("Geben Sie einen Prompt ein:")
-if prompt:
-    with st.spinner("Generieren von Text..."):
-        generated_text = generate_text(prompt, model, tokenizer)
-    st.header("Generierter Text:")
-    for i, text in enumerate(generated_text):
-        st.subheader(f"Option {i+1}:")
-        st.write(text)
+if __name__ == "__main__":
+    einfach_prompt = EinfachPrompt()
+    prompt = "Erzähl mir etwas über EinfachPrompt."
+    print(einfach_prompt.generate(prompt))