add application files

.gitignore

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+venv

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+
+
+# Function to create InferenceClient dynamically based on model selection
+def get_client(model_name):
+    return InferenceClient(model_name)
+
+
+# Chat response function
+def respond(message, history, max_tokens, top_p, model_name):
+    system_message = "You are a friendly Chatbot."
+    client = get_client(model_name)
+
+    messages = [{"role": "system", "content": system_message}]
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
+
+    messages.append({"role": "user", "content": message})
+
+    response = client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        top_p=top_p,
+        stream=False,
+    )
+
+    return response.choices[0].message["content"]
+
+
+# Streamlit UI
+st.title(f"🤖 Simple Chatbot")
+st.markdown(
+    "This is a simple chatbot application built with Streamlit and Hugging Face Inference API."
+)
+
+# Session state untuk menyimpan history chat
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Sidebar untuk konfigurasi
+st.sidebar.title("Settings")
+model_name = st.sidebar.selectbox(
+    "Choose a Model",
+    [
+        "meta-llama/Meta-Llama-3-8B-Instruct",
+        "mistralai/Mistral-7B-Instruct-v0.3",
+        "HuggingFaceH4/zephyr-7b-beta",
+        "microsoft/Phi-3.5-mini-instruct",
+    ],
+    index=0,
+)
+
+max_tokens = st.sidebar.slider("Max new tokens", 1, 2048, 512, 1)
+top_p = st.sidebar.slider("Top-p (nucleus sampling)", 0.1, 1.0, 0.95, 0.05)
+
+# Chat input dari user
+user_input = st.chat_input("Ask anything...")
+
+# Tampilkan chat history
+for user_msg, bot_msg in st.session_state.chat_history:
+    with st.chat_message("user"):
+        st.markdown(user_msg)
+    with st.chat_message("assistant"):
+        st.markdown(bot_msg)
+
+# Ketika ada input baru
+if user_input:
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    with st.chat_message("assistant"):
+        response = respond(
+            user_input,
+            st.session_state.chat_history,
+            max_tokens,
+            top_p,
+            model_name,
+        )
+        st.markdown(response)
+
+    # Simpan ke session state
+    st.session_state.chat_history.append((user_input, response))

requirements.txt

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+streamlit
+huggingface_hub