Create app.py

app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import warnings
+import logging
+
+warnings.filterwarnings("ignore")
+logging.getLogger("transformers").setLevel(logging.ERROR)
+
+@st.cache_resource
+def load_model():
+    model_name = "radlab/polish-gpt2-small-v2"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+st.set_page_config(page_title="Polski Chatbot AI", page_icon="🤖")
+st.title("🤖 Polski Chatbot AI")
+st.caption("Model: radlab/polish-gpt2-small-v2")
+
+if "history" not in st.session_state:
+    st.session_state.history = ""
+
+user_input = st.text_input("Wpisz wiadomość:", "")
+
+if st.button("Wyślij") and user_input.strip() != "":
+    st.session_state.history += f"Użytkownik: {user_input}\nAI:"
+
+    input_ids = tokenizer.encode(st.session_state.history, return_tensors="pt", truncation=True, max_length=1024)
+    output = model.generate(
+        input_ids,
+        max_length=input_ids.shape[1] + 80,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=0.7
+    )
+    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    model_reply = output_text[len(st.session_state.history):].split("Użytkownik:")[0].strip()
+    st.session_state.history += f" {model_reply}\n"
+
+st.subheader("🗨️ Historia rozmów")
+st.text_area("📖", st.session_state.history.strip(), height=300)
+
+if st.button("🧹 Wyczyść historię"):
+    st.session_state.history = ""