#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

set -xe
IMAGE_REPO=${IMAGE_REPO:-"opea"}
IMAGE_TAG=${IMAGE_TAG:-"latest"}
echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
export REGISTRY=${IMAGE_REPO}
export TAG=${IMAGE_TAG}
export MODEL_CACHE=${model_cache:-"/data/cache"}

WORKPATH=$(dirname "$PWD")
LOG_PATH="$WORKPATH/tests"
ip_address=$(hostname -I | awk '{print $1}')

function build_docker_images() {
    opea_branch=${opea_branch:-"main"}
    cd $WORKPATH/docker_image_build
    git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git
    pushd GenAIComps
    echo "GenAIComps test commit is $(git rev-parse HEAD)"
    docker build --no-cache -t ${REGISTRY}/comps-base:${TAG} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
    popd && sleep 1s

    echo "Build all the images with --no-cache, check docker_image_build.log for details..."
    service_list="chatqna chatqna-ui dataprep retriever llm-faqgen nginx"
    docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log

    docker images && sleep 1s
}

function start_services() {
    cd $WORKPATH/docker_compose/intel/hpu/gaudi
    export FAQGen_COMPONENT_NAME="OpeaFaqGenTgi"
    source set_env_faqgen.sh

    # Start Docker Containers
    docker compose -f compose_faqgen_tgi.yaml up -d > ${LOG_PATH}/start_services_with_compose.log

    sleep 30s
}

function validate_service() {
    local URL="$1"
    local EXPECTED_RESULT="$2"
    local SERVICE_NAME="$3"
    local DOCKER_NAME="$4"
    local INPUT_DATA="$5"

    if [[ $SERVICE_NAME == *"dataprep_upload_file"* ]]; then
        cd $LOG_PATH
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'files=@./dataprep_file.txt' -H 'Content-Type: multipart/form-data' "$URL")
    elif [[ $SERVICE_NAME == *"dataprep_upload_link"* ]]; then
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'link_list=["https://www.ces.tech/"]' "$URL")
    elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
    elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
    else
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
    fi
    HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
    RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')

    docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log

    # check response status
    if [ "$HTTP_STATUS" -ne "200" ]; then
        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
        exit 1
    else
        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
    fi
    echo "Response"
    echo $RESPONSE_BODY
    echo "Expected Result"
    echo $EXPECTED_RESULT
    # check response body
    if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
        echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
        exit 1
    else
        echo "[ $SERVICE_NAME ] Content is as expected."
    fi

    sleep 1s
}

function validate_microservices() {
    # Check if the microservices are running correctly.

    # tei for embedding service
    validate_service \
        "${ip_address}:8090/embed" \
        "[[" \
        "tei-embedding" \
        "tei-embedding-gaudi-server" \
        '{"inputs":"What is Deep Learning?"}'

    sleep 1m # retrieval can't curl as expected, try to wait for more time

    # test /v1/dataprep upload file
    echo "Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze various levels of abstract data representations. It enables computers to identify patterns and make decisions with minimal human intervention by learning from large amounts of data." > $LOG_PATH/dataprep_file.txt
    validate_service \
        "http://${ip_address}:6007/v1/dataprep/ingest" \
        "Data preparation succeeded" \
        "dataprep_upload_file" \
        "dataprep-redis-server"

    # test /v1/dataprep upload link
    validate_service \
        "http://${ip_address}:6007/v1/dataprep/ingest" \
        "Data preparation succeeded" \
        "dataprep_upload_link" \
        "dataprep-redis-server"

    # test /v1/dataprep/get_file
    validate_service \
        "http://${ip_address}:6007/v1/dataprep/get" \
        '{"name":' \
        "dataprep_get" \
        "dataprep-redis-server"

    # test /v1/dataprep/delete_file
    validate_service \
        "http://${ip_address}:6007/v1/dataprep/delete" \
        '{"status":true}' \
        "dataprep_del" \
        "dataprep-redis-server"

    # retrieval microservice
    test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
    validate_service \
        "${ip_address}:7000/v1/retrieval" \
        " " \
        "retrieval" \
        "retriever-redis-server" \
        "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"

    # tei for rerank microservice
    echo "validate tei..."
    validate_service \
        "${ip_address}:8808/rerank" \
        '{"index":1,"score":' \
        "tei-rerank" \
        "tei-reranking-gaudi-server" \
        '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'

    # tgi for llm service
    echo "validate tgi..."
    validate_service \
        "${ip_address}:${LLM_ENDPOINT_PORT}/v1/chat/completions" \
        "content" \
        "tgi-llm" \
        "tgi-gaudi-server" \
        '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}'

    # faqgen llm microservice
    echo "validate llm-faqgen..."
    validate_service \
        "${ip_address}:${LLM_SERVER_PORT}/v1/faqgen" \
        "text" \
        "llm" \
        "llm-faqgen-server" \
        '{"messages":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
}

function validate_megaservice() {
    # Curl the Mega Service
    validate_service \
        "${ip_address}:${CHATQNA_BACKEND_PORT}/v1/chatqna" \
        "Embed" \
        "chatqna-megaservice" \
        "chatqna-gaudi-backend-server" \
        '{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens":32}'

    validate_service \
        "${ip_address}:${CHATQNA_BACKEND_PORT}/v1/chatqna" \
        "Embed" \
        "chatqna-megaservice" \
        "chatqna-gaudi-backend-server" \
        '{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens":32,"stream":false}'

}

function validate_frontend() {
    cd $WORKPATH/ui/svelte
    local conda_env_name="OPEA_e2e"
    export PATH=${HOME}/miniforge3/bin/:$PATH
    if conda info --envs | grep -q "$conda_env_name"; then
        echo "$conda_env_name exist!"
    else
        conda create -n ${conda_env_name} python=3.12 -y
    fi
    source activate ${conda_env_name}

    sed -i "s/localhost/$ip_address/g" playwright.config.ts

    conda install -c conda-forge nodejs=22.6.0 -y
    npm install && npm ci && npx playwright install --with-deps
    node -v && npm -v && pip list

    exit_status=0
    npx playwright test || exit_status=$?

    if [ $exit_status -ne 0 ]; then
        echo "[TEST INFO]: ---------frontend test failed---------"
        exit $exit_status
    else
        echo "[TEST INFO]: ---------frontend test passed---------"
    fi
}

function stop_docker() {
    cd $WORKPATH/docker_compose/intel/hpu/gaudi
    docker compose -f compose_faqgen_tgi.yaml  down
}

function main() {

    echo "::group::stop_docker"
    stop_docker
    echo "::endgroup::"

    echo "::group::build_docker_images"
    if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
    echo "::endgroup::"

    echo "::group::start_services"
    start_services
    echo "::endgroup::"

    echo "::group::validate_microservices"
    validate_microservices
    echo "::endgroup::"

    echo "::group::validate_megaservice"
    validate_megaservice
    echo "::endgroup::"

    echo "::group::validate_frontend"
    validate_frontend
    echo "::endgroup::"

    echo "::group::stop_docker"
    stop_docker
    echo "::endgroup::"

    docker system prune -f

}

main