Update app.py

app.py

@@ -1,9 +1,36 @@
 import streamlit as st
-from transformers import pipeline
+import torch 
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 
+
+
+torch.random.manual_seed(0) 
+model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct",trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") 
 
-pipe = pipeline('sentiment-analysis')
 text = st.text_area("Enter text....")
+messages = [ 
+    {"role": "system", "content": "You are a helpful AI assistant."}, 
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, 
+] 
+
+
+pipe = pipeline( 
+    "text-generation", 
+    model=model, 
+    tokenizer=tokenizer, 
+) 
+
+generation_args = { 
+    "max_new_tokens": 500, 
+    "return_full_text": False, 
+    "temperature": 0.0, 
+    "do_sample": False, 
+} 
+
+output = pipe(messages, **generation_args) 
 
 if text:
     out = pipe(text)
-    st.json(out)
+    st.write(out)