Add new demo

local_chatgpt_clone/README.md

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+## 🦙💬 ChatGPT Clone using Llama-3
+This project demonstrates how to build a ChatGPT clone using the Llama-3 model running locally on your computer. The application is built using Python and Streamlit, providing a user-friendly interface for interacting with the language model. Best of all, it's 100% free and doesn't require an internet connection!
+
+### Features
+- Runs locally on your computer without the need for an internet connection and completely free to use.
+- Utilizes the Llama-3 instruct model for generating responses
+- Provides a chat-like interface for seamless interaction
+
+### 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. Download and install the [LM Studio desktop app](https://lmstudio.ai/). Download the Llama-3 instruct model.
+
+4. Expose the Llama-3 model as an OpenAI API by starting the server in LM Studio. Watch this [video walkthrough](https://x.com/Saboo_Shubham_/status/1783715814790549683).
+
+5. Run the Streamlit App
+```bash
+streamlit run chatgpt_clone_llama3.py
+```
+

local_chatgpt_clone/chatgpt_clone_llama3.py

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+import streamlit as st
+from openai import OpenAI
+
+# Set up the Streamlit App
+st.title("ChatGPT Clone using Llama-3 🦙")
+st.caption("Chat with locally hosted Llama-3 using the LM Studio 💯")
+
+# Point to the local server setup using LM Studio
+client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
+
+# Initialize the chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display the chat history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Generate response
+    response = client.chat.completions.create(
+        model="lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF",
+        messages=st.session_state.messages, temperature=0.7
+    )
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response.choices[0].message.content})
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response.choices[0].message.content)

local_chatgpt_clone/requirements.txt

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+streamlit 
+openai