Create streamlit_app.py

streamlit_app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+
+st.set_page_config(page_title="OpenChat 3.5 - HF Space", page_icon="🤖")
+st.title("🤖 YahyaGPT")
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("openchat/openchat-3.5-1210")
+    model = AutoModelForCausalLM.from_pretrained(
+        "openchat/openchat-3.5-1210",
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+
+prompt = st.chat_input("Ask Yahya anything...")
+
+if prompt:
+    st.chat_message("user").write(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    full_prompt = "You are a helpful assistant.\n"
+    for msg in st.session_state.messages:
+        role = msg["role"]
+        content = msg["content"]
+        if role == "user":
+            full_prompt += f"User: {content}\n"
+        else:
+            full_prompt += f"Assistant: {content}\n"
+    full_prompt += "Assistant:"
+
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=300,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    reply = response.split("Assistant:")[-1].strip()
+
+    st.chat_message("assistant").write(reply)
+    st.session_state.messages.append({"role": "assistant", "content": reply})