Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Titolo dell'app
+st.title("🤖 Chatbot DeepSeek Transformers + Streamlit")
+
+@st.cache_resource
+def load_model():
+    model_name = "deepseek-ai/DeepSeek-Coder-V2-Instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+user_input = st.text_input("Scrivi il tuo messaggio:")
+
+if user_input:
+    st.session_state.chat_history.append(("🧑", user_input))
+
+    inputs = tokenizer(user_input, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    st.session_state.chat_history.append(("🤖", response))
+
+for speaker, msg in st.session_state.chat_history:
+    st.markdown(f"**{speaker}**: {msg}")