Added new demo

llm_router_app/README.md

@@ -0,0 +1,52 @@
+## 📡 RouteLLM Chat App 
+
+> Note: This project is inspired by the opensource [RouteLLM library](https://github.com/lm-sys/RouteLLM/tree/main), which provides intelligent routing between different language models.
+
+This Streamlit application demonstrates the use of RouteLLM, a system that intelligently routes queries between different language models based on the complexity of the task. It provides a chat interface where users can interact with AI models, and the app automatically selects the most appropriate model for each query.
+
+### Features
+- Chat interface for interacting with AI models
+- Automatic model selection using RouteLLM
+- Utilizes both GPT-4 and Meta-Llama 3.1 models
+- Displays chat history with model information
+
+### How to get Started?
+
+1. Clone the GitHub repository
+
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+3. Set up your API keys:
+
+```bash
+os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
+os.environ['TOGETHERAI_API_KEY'] = "your_togetherai_api_key"
+```
+Note: In a production environment, it's recommended to use environment variables or a secure configuration management system instead of hardcoding API keys.
+
+4. Run the Streamlit App
+```bash
+streamlit run llm_router.py
+```
+
+### How it Works?
+
+1. RouteLLM Initialization: The app initializes the RouteLLM controller with two models:
+    - Strong model: GPT-4 (mini)
+    -  Weak model: Meta-Llama 3.1 70B Instruct Turbo
+
+2. Chat Interface: Users can input messages through a chat interface.
+
+3. Model Selection: RouteLLM automatically selects the appropriate model based on the complexity of the user's query.
+
+4. Response Generation: The selected model generates a response to the user's input.
+
+5. Display: The app displays the response along with information about which model was used.
+
+6. History: The chat history is maintained and displayed, including model information for each response.

llm_router_app/llm_router.py

@@ -0,0 +1,52 @@
+import os
+
+os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
+os.environ['TOGETHERAI_API_KEY'] = "your_togetherai_api_key"
+
+import streamlit as st
+from routellm.controller import Controller
+
+# Initialize RouteLLM client
+client = Controller(
+    routers=["mf"],
+    strong_model="gpt-4o-mini",
+    weak_model="together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+)
+
+# Set up Streamlit app
+st.title("RouteLLM Chat App")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+        if "model" in message:
+            st.caption(f"Model used: {message['model']}")
+
+# Chat input
+if prompt := st.chat_input("What is your message?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Get RouteLLM response
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        response = client.chat.completions.create(
+            model="router-mf-0.11593",
+            messages=[{"role": "user", "content": prompt}]
+        )
+        message_content = response['choices'][0]['message']['content']
+        model_name = response['model']
+        
+        # Display assistant's response
+        message_placeholder.markdown(message_content)
+        st.caption(f"Model used: {model_name}")
+    
+    # Add assistant's response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": message_content, "model": model_name})

llm_router_app/requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+"routellm[serve,eval]"