Create app.py

app.py

@@ -0,0 +1,36 @@
+# streamlit_app.py
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("meta-math/MetaMath-Mistral-7B")
+    model = AutoModelForCausalLM.from_pretrained("meta-math/MetaMath-Mistral-7B")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Streamlit app layout
+st.title("MetaMath Mistral 7B Question-Answering")
+st.write("Ask any question, and the model will generate an answer:")
+
+# Input from user
+question = st.text_input("Enter your question:")
+
+if st.button("Generate Answer"):
+    if question.strip():
+        # Tokenize input
+        inputs = tokenizer.encode(question, return_tensors="pt")
+
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(inputs, max_length=200, num_return_sequences=1)
+        
+        # Decode and display the output
+        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.write("**Answer:**", answer)
+    else:
+        st.write("Please enter a question to get an answer.")