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

set -e
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}

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

function build_docker_images() {
    opea_branch=${opea_branch:-"main"}
    # If the opea_branch isn't main, replace the git clone branch in Dockerfile.
    if [[ "${opea_branch}" != "main" ]]; then
        cd $WORKPATH
        OLD_STRING="RUN git clone --depth 1 https://github.com/opea-project/GenAIComps.git"
        NEW_STRING="RUN git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git"
        find . -type f -name "Dockerfile*" | while read -r file; do
            echo "Processing file: $file"
            sed -i "s|$OLD_STRING|$NEW_STRING|g" "$file"
        done
    fi
    cd $WORKPATH/docker_image_build
    git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git
    git clone --depth 1 --branch v0.6.4.post2+Gaudi-1.19.0 https://github.com/HabanaAI/vllm-fork.git
    sed -i 's/triton/triton==3.1.0/g' vllm-fork/requirements-hpu.txt

    echo "Build all the images with --no-cache, check docker_image_build.log for details..."
    service_list="chatqna-without-rerank chatqna-ui dataprep retriever vllm-gaudi nginx"
    docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log

    docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
    docker pull ghcr.io/huggingface/tei-gaudi:1.5.0

    docker images && sleep 1s
}

function start_services() {
    cd $WORKPATH/docker_compose/intel/hpu/gaudi
    export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
    export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
    export NUM_CARDS=1
    export INDEX_NAME="rag-redis"
    export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}

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

    n=0
    until [[ "$n" -ge 160 ]]; do
        docker logs vllm-gaudi-server > vllm_service_start.log
        if grep -q "Warmup finished" vllm_service_start.log; then
            break
        fi
        sleep 5s
        n=$((n+1))
    done
}

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 "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
    fi
    # check response body
    if [[ "$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/ingest 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/ingest upload link
    validate_service \
        "http://${ip_address}:6007/v1/dataprep/ingest" \
        "Data preparation succeeded" \
        "dataprep_upload_link" \
        "dataprep-redis-server"

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

    # test /v1/dataprep/delete
    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" \
        "retrieved_docs" \
        "retrieval-microservice" \
        "retriever-redis-server" \
        "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"

    # vllm for llm service
    validate_service \
        "${ip_address}:8007/v1/chat/completions" \
        "content" \
        "vllm-llm" \
        "vllm-gaudi-server" \
        '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}'
}

function validate_megaservice() {
    # Curl the Mega Service
    validate_service \
        "${ip_address}:8888/v1/chatqna" \
        "data: " \
        "chatqna-megaservice" \
        "chatqna-gaudi-backend-server" \
        '{"messages": "What is the revenue of Nike in 2023?"}'

}

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_without_rerank.yaml down
}

function main() {

    stop_docker
    if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
    start_time=$(date +%s)
    start_services
    end_time=$(date +%s)
    duration=$((end_time-start_time))
    echo "Mega service start duration is $duration s"

    if [ "${mode}" == "perf" ]; then
        python3 $WORKPATH/tests/chatqna_benchmark.py
    elif [ "${mode}" == "" ]; then
        validate_microservices
        validate_megaservice
        validate_frontend
    fi

    stop_docker
    echo y | docker system prune

}

main