Create app.py

app.py

@@ -0,0 +1,65 @@
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+# Load model and tokenizer
+model_name = "abooze/ft-deepseek-llm-7b-chat-dpo-pairs"
+st.title("💬 RealMind AI")
+st.markdown("Chat with RealMind AI!")
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype= torch.float32,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    gen_config = GenerationConfig.from_pretrained(model_name)
+    gen_config.pad_token_id = gen_config.eos_token_id
+    return tokenizer, model, gen_config
+
+tokenizer, model, gen_config = load_model()
+
+# Session state to hold chat history
+# if "messages" not in st.session_state:
+#     st.session_state.messages = [
+#         {"role": "system", "content": "You are a helpful assistant."}
+#     ]
+
+# # Display chat history
+# for msg in st.session_state.messages:
+#     if msg["role"] != "system":
+#         st.chat_message(msg["role"]).write(msg["content"])
+
+# Chat input
+user_input = st.chat_input("Ask something...")
+if user_input:
+    # Add user input to message history
+    st.session_state.messages = [{"role": "user", "content": user_input}]
+    st.chat_message("user").write(user_input)
+
+    with st.spinner("Generating response..."):
+        input_ids = tokenizer.apply_chat_template(
+            st.session_state.messages,
+            return_tensors="pt",
+            add_generation_prompt=True
+        ).to(model.device)
+
+        outputs = model.generate(
+            input_ids=input_ids,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.95,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+        # Decode and extract response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # Extract only the assistant's last reply
+        assistant_reply = response.split("<|assistant|>\n")[-1].strip()
+
+    st.chat_message("assistant").write(assistant_reply)
+    st.session_state.messages.append({"role": "assistant", "content": assistant_reply})