Spaces:
Running
Running
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
3 |
+
|
4 |
+
# تحميل النموذج والTokenizer
|
5 |
+
@st.cache_resource
|
6 |
+
def load_model():
|
7 |
+
model_name = "microsoft/Phi-4-mini-instruct"
|
8 |
+
st.write("جارٍ تحميل النموذج...")
|
9 |
+
model = AutoModelForCausalLM.from_pretrained(model_name)
|
10 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
11 |
+
st.write("تم تحميل النموذج بنجاح!")
|
12 |
+
return model, tokenizer
|
13 |
+
|
14 |
+
model, tokenizer = load_model()
|
15 |
+
|
16 |
+
# واجهة Streamlit
|
17 |
+
st.title("Phi-4-mini-instruct Chatbot")
|
18 |
+
st.write("تفاعل مع نموذج Phi-4-mini-instruct من Microsoft.")
|
19 |
+
|
20 |
+
# إدخال النص
|
21 |
+
user_input = st.text_input("أدخل نصك هنا:")
|
22 |
+
|
23 |
+
# توليد النص
|
24 |
+
if user_input:
|
25 |
+
st.write("جارٍ معالجة النص...")
|
26 |
+
inputs = tokenizer(user_input, return_tensors="pt")
|
27 |
+
outputs = model.generate(**inputs, max_new_tokens=100)
|
28 |
+
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
29 |
+
st.write("النموذج يقول:", response)
|