Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,58 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+st.set_page_config(page_title="OpenChat Bot", page_icon="💬")
+st.title("🧠 Chatbot - OpenChat 3.5")
+
+@st.cache_resource
+def load_model():
+    model_name = "openchat/openchat-3.5-0106"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        device_map="auto"
+    )
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "Bonjour ! Pose-moi une question."}]
+
+# Afficher les messages précédents
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+def generate_response(prompt, history):
+    history_text = ""
+    for m in history:
+        speaker = "User" if m["role"] == "user" else "Assistant"
+        history_text += f"{speaker}: {m['content']}\n"
+    full_prompt = history_text + f"User: {prompt}\nAssistant:"
+
+    inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=2048)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.95
+    )
+    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+    return decoded.split("Assistant:")[-1].strip()
+
+user_input = st.chat_input("Posez votre question ici...")
+if user_input:
+    st.chat_message("user").markdown(user_input)
+    st.session_state.messages.append({"role": "user", "content": user_input})
+
+    with st.chat_message("assistant"):
+        with st.spinner("OpenChat réfléchit..."):
+            response = generate_response(user_input, st.session_state.messages)
+            st.markdown(response)
+    st.session_state.messages.append({"role": "assistant", "content": response})