Create app.py

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Function to generate responses based on user messages
+def generate_response(messages):
+    input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+    outputs = model.generate(input_ids, max_new_tokens=100)
+    generated_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_response
+
+# Streamlit app
+st.title("Mixtral Chatbot")
+
+messages = []
+user_input = st.text_input("You:", "")
+
+if st.button("Send"):
+    if user_input:
+        messages.append({"role": "user", "content": user_input})
+        bot_response = generate_response(messages)
+        messages.append({"role": "assistant", "content": bot_response})
+    else:
+        st.warning("Please enter a message.")
+
+# Display conversation
+for message in messages:
+    if message["role"] == "user":
+        st.text_input("You:", value=message["content"], disabled=True)
+    elif message["role"] == "assistant":
+        st.text_area("Mixtral:", value=message["content"], disabled=True)