#!/bin/bash
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0

set -ex
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}')

export image_fn="apple.png"
export video_fn="WeAreGoingOnBullrun.mp4"
export caption_fn="apple.txt"

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 $WORKPATH/docker_image_build
    git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
    echo "Build all the images with --no-cache, check docker_image_build.log for details..."
    service_list="multimodalqna multimodalqna-ui embedding-multimodal-bridgetower embedding retriever lvm dataprep whisper vllm-rocm"
    docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log

    docker images && sleep 1m
}

function setup_env() {
    export HOST_IP=${ip_address}
    export host_ip=${ip_address}
    export MULTIMODAL_HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
    export MULTIMODAL_VLLM_SERVICE_PORT="8399"
    export no_proxy=${your_no_proxy}
    export http_proxy=${your_http_proxy}
    export https_proxy=${your_http_proxy}
    export BRIDGE_TOWER_EMBEDDING=true
    export EMBEDDER_PORT=6006
    export MMEI_EMBEDDING_ENDPOINT="http://${HOST_IP}:$EMBEDDER_PORT"
    export MM_EMBEDDING_PORT_MICROSERVICE=6000
    export WHISPER_SERVER_PORT=7066
    export WHISPER_SERVER_ENDPOINT="http://${HOST_IP}:${WHISPER_SERVER_PORT}/v1/asr"
    export REDIS_URL="redis://${HOST_IP}:6379"
    export REDIS_HOST=${HOST_IP}
    export INDEX_NAME="mm-rag-redis"
    export LVM_ENDPOINT="http://${HOST_IP}:8399"
    export EMBEDDING_MODEL_ID="BridgeTower/bridgetower-large-itm-mlm-itc"
    export MULTIMODAL_LLM_MODEL_ID="Xkev/Llama-3.2V-11B-cot"
    export WHISPER_MODEL="base"
    export MM_EMBEDDING_SERVICE_HOST_IP=${HOST_IP}
    export MM_RETRIEVER_SERVICE_HOST_IP=${HOST_IP}
    export LVM_SERVICE_HOST_IP=${HOST_IP}
    export MEGA_SERVICE_HOST_IP=${HOST_IP}
    export BACKEND_SERVICE_ENDPOINT="http://${HOST_IP}:8888/v1/multimodalqna"
    export DATAPREP_INGEST_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/ingest"
    export DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/generate_transcripts"
    export DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/generate_captions"
    export DATAPREP_GET_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/get"
    export DATAPREP_DELETE_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/delete"
    export MODEL_CACHE=${model_cache:-"/var/opea/multimodalqna-service/data"}
}

function start_services() {
    cd $WORKPATH/docker_compose/amd/gpu/rocm
    docker compose -f compose_vllm.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
    n=0
    until [[ "$n" -ge 100 ]]; do
        docker logs multimodalqna-vllm-service >& $LOG_PATH/search-vllm-service_start.log
        if grep -q "Application startup complete" $LOG_PATH/search-vllm-service_start.log; then
            break
        fi
        sleep 10s
        n=$((n+1))
    done
}

function prepare_data() {
    cd $LOG_PATH
    echo "Downloading image and video"
    wget https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true -O ${image_fn}
    wget http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WeAreGoingOnBullrun.mp4 -O ${video_fn}
    echo "Writing caption file"
    echo "This is an apple."  > ${caption_fn}
    sleep 1m
}


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-multimodal-redis-transcript"* ]]; then
        cd $LOG_PATH
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${video_fn}" -H 'Content-Type: multipart/form-data' "$URL")
    elif [[ $SERVICE_NAME == *"dataprep-multimodal-redis-caption"* ]]; then
        cd $LOG_PATH
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${image_fn}" -H 'Content-Type: multipart/form-data' "$URL")
    elif [[ $SERVICE_NAME == *"dataprep-multimodal-redis-ingest"* ]]; then
        cd $LOG_PATH
        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${image_fn}" -F "files=@./apple.txt" -H 'Content-Type: multipart/form-data' "$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": "apple.txt"}' -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.

    # Bridgetower Embedding Server
    echo "Validating embedding-multimodal-bridgetower"
    validate_service \
        "http://${host_ip}:${EMBEDDER_PORT}/v1/encode" \
        '"embedding":[' \
        "embedding-multimodal-bridgetower" \
        "embedding-multimodal-bridgetower" \
        '{"text":"This is example"}'

    validate_service \
        "http://${host_ip}:${EMBEDDER_PORT}/v1/encode" \
        '"embedding":[' \
        "embedding-multimodal-bridgetower" \
        "embedding-multimodal-bridgetower" \
        '{"text":"This is example", "img_b64_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC"}'

    # embedding microservice
    echo "Validating embedding"
    validate_service \
        "http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings" \
        '"embedding":[' \
        "embedding" \
        "embedding" \
        '{"text" : "This is some sample text."}'

    validate_service \
        "http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings" \
        '"embedding":[' \
        "embedding" \
        "embedding" \
        '{"text": {"text" : "This is some sample text."}, "image" : {"url": "https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true"}}'

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

    # test data prep
    echo "Data Prep with Generating Transcript for Video"
    validate_service \
        "${DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT}" \
        "Data preparation succeeded" \
        "dataprep-multimodal-redis-transcript" \
        "dataprep-multimodal-redis"

    echo "Data Prep with Image & Caption Ingestion"
    validate_service \
        "${DATAPREP_INGEST_SERVICE_ENDPOINT}" \
        "Data preparation succeeded" \
        "dataprep-multimodal-redis-ingest" \
        "dataprep-multimodal-redis"

    echo "Validating get file returns mp4"
    validate_service \
        "${DATAPREP_GET_FILE_ENDPOINT}" \
        '.mp4' \
        "dataprep_get" \
        "dataprep-multimodal-redis"

    echo "Validating get file returns png"
    validate_service \
        "${DATAPREP_GET_FILE_ENDPOINT}" \
        '.png' \
        "dataprep_get" \
        "dataprep-multimodal-redis"

    sleep 2m

    # multimodal retrieval microservice
    echo "Validating retriever-redis"
    your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
    validate_service \
        "http://${host_ip}:7000/v1/retrieval" \
        "retrieved_docs" \
        "retriever-redis" \
        "retriever-redis" \
        "{\"text\":\"test\",\"embedding\":${your_embedding}}"

    sleep 5m

    #vLLM Service
    echo "Evaluating vllm"
    validate_service \
        "${host_ip}:${MULTIMODAL_VLLM_SERVICE_PORT}/v1/chat/completions" \
        "content" \
        "multimodalqna-vllm-service" \
        "multimodalqna-vllm-service" \
        '{"model": "Xkev/Llama-3.2V-11B-cot", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'

    # lvm
    echo "Evaluating lvm"
    validate_service \
        "http://${host_ip}:9399/v1/lvm" \
        '"text":"' \
        "lvm" \
        "lvm" \
        '{"retrieved_docs": [], "initial_query": "What is this?", "top_n": 1, "metadata": [{"b64_img_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "transcript_for_inference": "yellow image", "video_id": "8c7461df-b373-4a00-8696-9a2234359fe0", "time_of_frame_ms":"37000000", "source_video":"WeAreGoingOnBullrun_8c7461df-b373-4a00-8696-9a2234359fe0.mp4"}], "chat_template":"The caption of the image is: '\''{context}'\''. {question}"}'

    # data prep requiring lvm
    echo "Data Prep with Generating Caption for Image"
    validate_service \
        "${DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT}" \
        "Data preparation succeeded" \
        "dataprep-multimodal-redis-caption" \
        "dataprep-multimodal-redis"

    sleep 3m
}

function validate_megaservice() {
    # Curl the Mega Service with retrieval
    echo "Validate megaservice with first query"
    validate_service \
        "http://${host_ip}:8888/v1/multimodalqna" \
        '"time_of_frame_ms":' \
        "multimodalqna" \
        "multimodalqna-backend-server" \
        '{"messages": "What is the revenue of Nike in 2023?"}'

    echo "Validate megaservice with first audio query"
    validate_service \
        "http://${host_ip}:8888/v1/multimodalqna" \
        '"time_of_frame_ms":' \
        "multimodalqna" \
        "multimodalqna-backend-server" \
        '{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}]}]}'

    echo "Validate megaservice with follow-up query"
    validate_service \
        "http://${host_ip}:8888/v1/multimodalqna" \
        '"content":"' \
        "multimodalqna" \
        "multimodalqna-backend-server" \
        '{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}, {"type": "image_url", "image_url": {"url": "https://www.ilankelman.org/stopsigns/australia.jpg"}}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": [{"type": "text", "text": "goodbye"}]}]}'

    echo "Validate megaservice with multiple text queries"
    validate_service \
        "http://${host_ip}:8888/v1/multimodalqna" \
        '"content":"' \
        "multimodalqna" \
        "multimodalqna-backend-server" \
        '{"messages": [{"role": "user", "content": [{"type": "text", "text": "hello, "}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": [{"type": "text", "text": "goodbye"}]}]}'
}

function validate_delete {
    echo "Validate data prep delete files"
    export DATAPREP_DELETE_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/delete"
    validate_service \
        "${DATAPREP_DELETE_FILE_ENDPOINT}" \
        '{"status":true}' \
        "dataprep_del" \
        "dataprep-multimodal-redis"
}

function delete_data() {
    cd $LOG_PATH
    echo "Deleting image, video, and caption"
    rm -rf ${image_fn}
    rm -rf ${video_fn}
    rm -rf ${caption_fn}
}

function stop_docker() {
    cd $WORKPATH/docker_compose/amd/gpu/rocm
    docker compose -f compose.yaml stop && docker compose -f compose.yaml rm -f
}

function main() {

    setup_env
    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" && sleep 1s
    prepare_data

    validate_microservices
    echo "==== microservices validated ===="
    validate_megaservice
    echo "==== megaservice validated ===="
    validate_delete
    echo "==== delete validated ===="

    delete_data
    stop_docker
    echo y | docker system prune

}

main