Update app.py

app.py

@@ -1,40 +1,9 @@
 import streamlit as st
-import torch 
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 st.write("runningg")
 
-
-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") 
-
 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, 
-} 
-
-
 
 
-if text:
-    st.write("calculating")
-    out = pipe(messages, **generation_args) 
-    st.write(out)
+if st.button("Analyse"):
+    print(text)