delete files

src/streamlit_app.py

@@ -1,40 +1,32 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# 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}")

streamlit_app.py

@@ -1,32 +0,0 @@
-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}")