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Update app.py
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app.py
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import os
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import streamlit as st
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from git import Repo
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import shutil
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from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
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from llama_index.llms.llama_cpp import LlamaCPP
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from llama_index.embeddings import HuggingFaceEmbedding
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from llama_index.node_parser import SimpleNodeParser
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# Streamlit app setup
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st.set_page_config(page_title="GitHub Repo Explainer", layout="wide")
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st.title("📘 GitHub Repository Explainer (100% Free)")
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github_url = st.text_input("Enter GitHub Repository URL:", placeholder="https://github.com/user/repo")
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if st.button("Load and Analyze"):
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if github_url:
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try:
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# Clone repo
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if os.path.exists("repo"):
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shutil.rmtree("repo")
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Repo.clone_from(github_url, "repo")
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# Load the TinyLlama model
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llm = LlamaCPP(
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model_path="tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
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temperature=0.7,
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max_new_tokens=512,
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context_window=2048,
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model_kwargs={"n_gpu_layers": 20, "n_batch": 512},
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verbose=True,
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)
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# Setup embedding and service context
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embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
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service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
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parser = SimpleNodeParser.from_defaults()
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# Load and parse docs
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documents = SimpleDirectoryReader("repo").load_data()
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nodes = parser.get_nodes_from_documents(documents)
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index = VectorStoreIndex(nodes, service_context=service_context)
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query_engine = index.as_query_engine()
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response = query_engine.query("Explain the purpose, structure, and setup steps of this GitHub repository.")
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st.subheader("🧠 Repository Summary")
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st.write(str(response))
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except Exception as e:
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st.error(f"Error: {e}")
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else:
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st.warning("Please enter a GitHub URL.")
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if st.button("Reset"):
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shutil.rmtree("repo", ignore_errors=True)
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st.experimental_rerun()
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