basics working

app.py

@@ -1,4 +1,47 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+big_text = """
+    <div style='text-align: center;'>
+        <h1 style='font-size: 30x;'>Knowledge Extraction 1</h1>
+    </div>
+    """
+st.markdown(big_text, unsafe_allow_html=True)
+
+if 'is_initialized' not in st.session_state:
+    st.session_state['is_initialized'] = True
+    model_name = "EleutherAI/gpt-neo-125M"
+    st.session_state.model_name = "EleutherAI/gpt-neo-125M"
+    st.session_state.tokenizer = AutoTokenizer.from_pretrained(model_name)
+    st.session_state.model = AutoModelForCausalLM.from_pretrained("zmbfeng/gpt-neo-125M_untethered_100_epochs_multiple_paragraph")
+    if torch.cuda.is_available():
+        st.session_state.device = torch.device("cuda")
+        print("Using GPU:", torch.cuda.get_device_name(0))
+    else:
+        st.session_state.device = torch.device("cpu")
+        print("GPU is not available, using CPU instead.")
+    st.session_state.model.to(st.session_state.device)
+#prompt = "Discuss the impact of artificial intelligence on modern society."
+#prompt = "What is one of the best teachers in all of life?"
+#prompt = "What is the necessary awareness for deep and meaningful relationships?"
+#prompt = "What would happen if you knew you were going to die within a week or month?"
+#prompt = "question: What is one of the best teachers in all of life? "
+#prompt = "question: What would happen if death were to happen in an hour, week, or year?"
+#=============
+#prompt = "question: What if you live life fully?"
+#prompt = "question: What does death do to you?"
+#============
+#prompt = "question: Do you understand that every minute you're on the verge of death?"
+#most recent:
+#prompt = "question: Are you going to wait until the last moment to let death be your teacher?"
+query = st.text_input("Enter your query")
+if query:
+    prompt = "question: "+query
+    input_ids = st.session_state.tokenizer(prompt, return_tensors="pt").input_ids.to(st.session_state.device)
+    # Generate a response
+    output = st.session_state.model.generate(input_ids, max_length=2048, do_sample=True,temperature=0.01, pad_token_id=st.session_state.tokenizer.eos_token_id) #exact result for single paragraph
+
+    # Decode the output
+    response = st.session_state.tokenizer.decode(output[0], skip_special_tokens=True)
+    st.write(response)

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch