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app(1).py

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+from llama_cpp import Llama
+import os
+from huggingface_hub import hf_hub_download
+# Load the LLM from GGUF file
+
+repo_id = "Hiridharan10/llama-3-3b-coder-V2-gguf"
+
+model_file = "llama-3-3b-coder.gguf"
+model_path = hf_hub_download(repo_id = repo_id, filename=model_file)
+# n_threads
+llm = Llama(model_path=model_path,n_gpu_layers=30,n_ctx=512,temperature=0.2,repeat_penalty=1.1,top_k_sampling=40,top_p_sampling=0.95,min_p_sampling=0.05)
+def generate_llm_response(prompt):
+    output = llm(prompt, max_tokens=1024)
+    return output["choices"][0]["text"]
+
+import streamlit as st
+#import speech_recognition as sr
+import numpy as np
+
+# Session state for chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# Display previous messages
+for msg in st.session_state["messages"]:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+# User input (text)
+st.title("LeetCode Practice LLM")
+user_input = st.chat_input("Type a message or use voice...")
+
+
+# Process response
+if user_input:
+    st.chat_message("user").write(user_input)
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+
+    # Get response from GGUF LLM
+    response = generate_llm_response(user_input)
+
+    # Display response
+    st.chat_message("assistant").write(response)
+    st.session_state["messages"].append({"role": "assistant", "content": response})
+

requrirements.txt

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+llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
+cmake
+scikit-build
+ninja
+huggingface-hub
+streamlit