Update app.py

Implement basic Chatbot

app.py

@@ -1,35 +1,20 @@
 import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from langchain_core.prompts import ChatPromptTemplate
 
 # mistralai/Mistral-Nemo-Instruct-2407
 # Load the model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-3B-Instruct")
 model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct")
 
-
 st.title("Llama-3.2-3B-Instruct Text Generation")
 st.write("Enter a prompt and generate text using the Llama 3.2 3B model.")
 
-prompt = """
-    You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.
-    If you don't know the answer, just say that you don't know.
-    Answer in bullet points. Make sure your answer is relevant to the question and it is answered from the context only.
-    Question: {question} 
-    Context: {context} 
-    Answer:
-"""
-
-prompt = ChatPromptTemplate.from_template(prompt)
-
 with st.form("llm-form"):
     user_input = st.text_area("Enter your question or statement:")
     submit = st.form_submit_button("Submit")
 
-if submit:
-    
-    #outputs = model.generate(user_input, max_length=200)
-    inputs = tokenizer(user_input, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=50)
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    st.write(generated_text)
+    if submit:
+        inputs = tokenizer(user_input, return_tensors="pt")
+        outputs = model.generate(inputs["input_ids"], max_length=50)
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.write(generated_text)