Upload app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+
+@st.cache_resource(show_spinner="Loading Model & Tokenizer")
+def load_model():
+    # This is cached and will not run again and again.
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    import torch
+
+    base_model = AutoModelForCausalLM.from_pretrained(
+        "mosama/Qwen2.5-0.5B-Pretrained-ar-end-urd-500", device_map="cpu", torch_dtype=torch.float16)
+
+    tokenizer = AutoTokenizer.from_pretrained("mosama/Qwen2.5-0.5B-Pretrained-ar-end-urd-500")
+    st.success('Model & Tokenizer Loaded Successfully!', icon="✅")
+    return base_model, tokenizer
+
+st.title("Qwen2.5-0.5B Arabic, English & Urdu Continuous Pretrained")
+
+model, tokenizer = load_model()
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if not st.session_state.messages:
+    with st.chat_message("assistant", avatar="assistant"):
+        st.write("Hello 👋 I am an AI bot powered by Qwen 2.5 0.5B model.")
+    st.session_state.messages.append({"role": "assistant", "content": "Hello 👋 I am an AI bot powered by Qwen 2.5 0.5B model."})
+
+if prompt := st.chat_input("Say Something"):
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+if prompt:
+    with st.spinner(text="Generating response..."):
+        model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+        print(model_inputs)
+        generated_ids = model.generate(
+            **model_inputs,
+            max_new_tokens=50,
+            repetition_penalty=1.2,
+            temperature=0.5,
+            do_sample=True,
+            top_p=0.9,
+            top_k=20
+        )
+        print("Generated Response!")
+        response = tokenizer.decode(generated_ids, skip_special_tokens=True)[0]
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})