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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import faiss
import os
import numpy as np
from huggingface_hub import login
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
hf_token = os.getenv("PROJECT_TEST_TOKEN")
if hf_token:
login(hf_token)
else:
st.error("Token not found")
st.stop()
# Load code T5 model
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5-base")
device = torch.device("cpu")
model.to(device)
# 🔍 Function to Generate Responses
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
output = model.generate(**inputs, max_length=300)
return tokenizer.decode(output[0], skip_special_tokens=True)
# 🔍 FAISS Index Creation
def create_faiss_index(texts):
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
embedding_vectors = embeddings.embed_documents(texts)
faiss_index = faiss.IndexFlatL2(len(embedding_vectors[0]))
faiss_index.add(np.array(embedding_vectors))
return faiss_index
# 📂 Function to Parse Logs
def parse_test_log(file):
try:
log_content = file.read().decode("utf-8")
return log_content.splitlines() # Split logs into lines
except Exception as e:
st.error(f"Error reading file: {e}")
return []
# 🌟 Streamlit UI
st.title("🔍 Test Log Failure Analysis with CodeLlama 3B")
# 📂 File Upload
uploaded_file = st.file_uploader("Upload test log (txt, json, xml, html)", type=["txt", "json", "xml", "html"])
if uploaded_file is not None:
st.info("Processing the test log file...")
test_log_lines = parse_test_log(uploaded_file)
# 🔍 Create FAISS Index
st.info("Indexing log data...")
faiss_index = create_faiss_index(test_log_lines)
# ❓ User Query
question = st.text_input("Ask a question about the test failures:")
if question:
# 🔍 Retrieve Similar Logs from FAISS
query_embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2").embed_query(question)
_, I = faiss_index.search(np.array([query_embedding]), k=5) # Retrieve top 5 results
# 📝 Combine the most relevant logs
context = "\n".join([test_log_lines[i] for i in I[0]])
# 🤖 Generate Answer with CodeLlama
prompt = f"Given the following test logs:\n{context}\n\nAnswer: {question}"
answer = generate_response(prompt)
st.subheader("Analysis Result:")
st.write(answer)
else:
st.info("Upload a test log file to begin analysis.")
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