Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(
+    "universitytehran/PersianMind-v1.0",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    device_map={"": device},
+)
+tokenizer = AutoTokenizer.from_pretrained("universitytehran/PersianMind-v1.0")
+
+# Conversation template
+TEMPLATE = "{context}\nYou: {prompt}\nPersianMind: "
+CONTEXT = "This is a conversation with PersianMind. It is an artificial intelligence model designed by a team of " \
+    "NLP experts at the University of Tehran to help you with various tasks such as answering questions, " \
+    "providing recommendations, and helping with decision making. You can ask it anything you want and " \
+    "it will do its best to give you accurate and relevant information."
+
+# Streamlit app
+st.title("PersianMind Chat")
+st.markdown("Chat with **PersianMind**, an AI model by the University of Tehran.")
+
+# User input
+prompt = st.text_input("Enter your question (in Persian):")
+
+if st.button("Get Response"):
+    if prompt.strip():
+        with st.spinner("Generating response..."):
+            model_input = TEMPLATE.format(context=CONTEXT, prompt=prompt)
+            input_tokens = tokenizer(model_input, return_tensors="pt").to(device)
+            generate_ids = model.generate(**input_tokens, max_new_tokens=512, do_sample=False, repetition_penalty=1.1)
+            model_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+            response = model_output[len(model_input):]
+        
+        st.text_area("PersianMind's Response:", response, height=200)
+    else:
+        st.warning("Please enter a question.")