Update app.py

app.py

@@ -1,26 +1,25 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
+#from transformers import AutoModelForCausalLM, AutoTokenizer
 
 #model_name = "deepseek-ai/DeepSeek-R1"
 #tokenizer = AutoTokenizer.from_pretrained(model_name)
 #model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,quantization_config=None)
 
-model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
+#model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+#tokenizer = AutoTokenizer.from_pretrained(model_id)
+#model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+
 
 st.title("DeepSeek Chatbot")
 
 prompt = st.text_input("Enter your message:")
 
 if st.button("Run"):
-    messages =[]
-    messages.append({"role": "user", "content": prompt})
-     response= model.chat.completions.create(
-        model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
-        messages=messages,
-        temperature=0.6,
-        max_tokens=1024,
-        top_p=0.95
-    )
+    messages = [{"role": "user", "content": "Who are you?"},]
+    pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", trust_remote_code=True)
+    response= pipe(messages)
     st.text_area("Long Text Box", response, height=1000)