Add application file

app.py

@@ -1,4 +1,37 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the model and tokenizer
+model_name = "m3rg-iitd/llamat-3-chat" #"gpt2"  # You can replace this with any model of your choice
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+st.title("Chatbot with LlaMat")
+st.write("Ask me anything about material!")
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Function to generate response
+def generate_response(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# User input
+user_input = st.text_input("You: ", "")
+
+if user_input:
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    response = generate_response(user_input)
+    st.session_state.messages.append({"role": "bot", "content": response})
+
+# Display chat history
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        st.write(f"You: {message['content']}")
+    else:
+        st.write(f"Bot: {message['content']}")