Add application file

app.py

@@ -1,4 +1,40 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.set_page_config(page_title="Chat with Qwen2.5-Omni-7B", layout="centered")
+
+st.title("Chat with Qwen2.5-Omni-7B")
+
+# Model name
+model_name = "Qwen/Qwen2.5-Omni-7B"
+
+# Prompt input
+system_prompt = st.text_area("System Prompt", "You are a helpful assistant.", height=100)
+user_input = st.text_input("Your Message", "")
+
+# Temp & token sliders
+temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
+max_tokens = st.slider("Max Tokens", 16, 1024, 256)
+
+# Optional: Hugging Face token field (left empty for user)
+hf_token = st.text_input("Hugging Face Token (optional)", type="password")
+
+# Load model pipeline
+@st.cache_resource
+def load_pipeline():
+    return pipeline(
+        "text-generation",
+        model=model_name,
+        tokenizer=model_name,
+        use_auth_token=hf_token if hf_token else None,
+        device_map="auto"
+    )
+
+if user_input:
+    pipe = load_pipeline()
+    prompt = f"{system_prompt}
+User: {user_input}
+Assistant:"
+    response = pipe(prompt, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
+    st.markdown("**Response:**")
+    st.write(response.replace(prompt, ""))

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+transformers
+torch