Rename app.py to gpt2.py

app.py → gpt2.py

@@ -8,24 +8,33 @@ model_path = "zhvanetsky_model"
 tokenizer = GPT2Tokenizer.from_pretrained(model_path)
 model = GPT2LMHeadModel.from_pretrained(model_path).to(DEVICE)
 
-def generate_text(input_text):
+def generate_text(input_text, num_beams, temperature, max_length, top_p):
     model.eval()
     input_ids = tokenizer.encode(input_text, return_tensors="pt").to(DEVICE)
     with torch.no_grad():
         out = model.generate(input_ids,
                             do_sample=True,
-                            num_beams=10,
-                            temperature=2.2,
-                            top_p=0.85,
+                            num_beams=num_beams,
+                            temperature=temperature,
+                            top_p=top_p,
                             top_k=500,
-                            max_length=100,
+                            max_length=max_length,
                             no_repeat_ngram_size=3,
                             num_return_sequences=3,
                             )
     return tokenizer.decode(out[0], skip_special_tokens=True)
 
+# Streamlit interface
 st.title("GPT-2 Text Generator")
+
 user_input = st.text_area("Input Text", "Введите ваш текст")
+
+# Add sliders or input boxes for model parameters
+num_beams = st.slider("Number of Beams", min_value=1, max_value=20, value=10)
+temperature = st.slider("Temperature", min_value=0.1, max_value=3.0, value=1.0, step=0.1)
+max_length = st.number_input("Max Length", min_value=10, max_value=300, value=100)
+top_p = st.slider("Top P", min_value=0.1, max_value=1.0, value=0.85, step=0.05)
+
 if st.button("Generate"):
-    generated_output = generate_text(user_input)
-    st.text_area("Generated Text", generated_output)
+    generated_output = generate_text(user_input, num_beams, temperature, max_length, top_p)
+    st.text_area("Generated Text", generated_output)