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import os
import streamlit as st
from git import Repo
import shutil
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
ServiceContext,
SentenceSplitter,
)
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
st.set_page_config(page_title="GitHub Repo Explainer", layout="wide")
st.title("📘 GitHub Repository Explainer (100% Free)")
github_url = st.text_input("GitHub URL", placeholder="https://github.com/user/repo")
if st.button("Load and Analyze"):
if github_url:
try:
# Clean previous repo if exists
if os.path.exists("repo"):
shutil.rmtree("repo")
with st.spinner("📥 Cloning GitHub repository..."):
Repo.clone_from(github_url, "repo")
st.success("✅ Repo cloned successfully.")
with st.spinner("🔧 Loading LLM and embeddings..."):
llm = LlamaCPP(
model_path="tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
temperature=0.7,
max_new_tokens=512,
context_window=2048,
generate_kwargs={"top_p": 0.95, "top_k": 50},
model_kwargs={"n_gpu_layers": 20, "n_batch": 512},
verbose=True,
)
embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
service_context = ServiceContext.from_defaults(
llm=llm,
embed_model=embed_model,
node_parser=SentenceSplitter(chunk_size=512, chunk_overlap=50),
)
with st.spinner("📄 Reading and parsing files..."):
docs = SimpleDirectoryReader("repo").load_data()
st.write(f"📚 {len(docs)} documents loaded.")
with st.spinner("🔍 Building index..."):
index = VectorStoreIndex.from_documents(docs, service_context=service_context)
query_engine = index.as_query_engine()
with st.spinner("🧠 Querying the model..."):
query = "Explain the purpose, structure, and setup steps of this GitHub repository."
response = query_engine.query(query)
st.subheader("🧾 Repository Summary")
st.write(str(response))
except Exception as e:
st.error(f"❌ Something went wrong:\n\n{e}")
else:
st.warning("⚠️ Please enter a GitHub repo URL.")
if st.button("Reset"):
shutil.rmtree("repo", ignore_errors=True)
st.experimental_rerun()
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