#!/bin/bash

# --- LLM Analysis Helper Function ---
# This function should be sourced by the main E2E script or test scripts.
# It requires curl and jq to be installed.
# It expects the project root path to be passed as the second argument.

analyze_log_with_llm() {
  local log_file="$1"
  local project_root="$2" # Expect project root as the second argument

  if [ -z "$project_root" ]; then
      echo "[HELPER_ERROR] Project root argument is missing. Skipping LLM analysis." >&2
      return 1
  fi

  local env_file="${project_root}/.env" # Path to .env in project root

  local provider_summary_log="provider_add_task_summary.log" # File summarizing provider test outcomes
  local api_key=""
  # !!! IMPORTANT: Replace with your actual Claude API endpoint if different !!!
  local api_endpoint="https://api.anthropic.com/v1/messages"
  # !!! IMPORTANT: Ensure this matches the variable name in your .env file !!!
  local api_key_name="ANTHROPIC_API_KEY"

  echo "" # Add a newline before analysis starts

  # Check for jq and curl
  if ! command -v jq &> /dev/null; then
    echo "[HELPER_ERROR] LLM Analysis requires 'jq'. Skipping analysis." >&2
    return 1
  fi
  if ! command -v curl &> /dev/null; then
    echo "[HELPER_ERROR] LLM Analysis requires 'curl'. Skipping analysis." >&2
    return 1
  fi

  # Check for API Key in the PROJECT ROOT's .env file
  if [ -f "$env_file" ]; then
    # Original assignment - Reading from project root .env
    api_key=$(grep "^${api_key_name}=" "$env_file" | sed -e "s/^${api_key_name}=//" -e 's/^[[:space:]"]*//' -e 's/[[:space:]"]*$//')
  fi

  if [ -z "$api_key" ]; then
    echo "[HELPER_ERROR] ${api_key_name} not found or empty in project root .env file ($env_file). Skipping LLM analysis." >&2 # Updated error message
    return 1
  fi

  # Log file path is passed as argument, need to ensure it exists relative to where the script *calling* this function is, OR use absolute path.
  # Assuming absolute path or path relative to the initial PWD for simplicity here.
  # The calling script passes the correct path relative to the original PWD.
  if [ ! -f "$log_file" ]; then
    echo "[HELPER_ERROR] Log file not found: $log_file (PWD: $(pwd)). Check path passed to function. Skipping LLM analysis." >&2 # Updated error
    return 1
  fi

  local log_content
  # Read entire file, handle potential errors
  log_content=$(cat "$log_file") || {
    echo "[HELPER_ERROR] Failed to read log file: $log_file. Skipping LLM analysis." >&2
    return 1
  }

  # Prepare the prompt using a quoted heredoc for literal interpretation
  read -r -d '' prompt_template <<'EOF'
Analyze the following E2E test log for the task-master tool. The log contains output from various 'task-master' commands executed sequentially.

Your goal is to:
1. Verify if the key E2E steps completed successfully based on the log messages (e.g., init, parse PRD, list tasks, analyze complexity, expand task, set status, manage models, add/remove dependencies, add/update/remove tasks/subtasks, generate files).
2. **Specifically analyze the Multi-Provider Add-Task Test Sequence:**
   a. Identify which providers were tested for `add-task`. Look for log steps like "Testing Add-Task with Provider: ..." and the summary log 'provider_add_task_summary.log'.
   b. For each tested provider, determine if `add-task` succeeded or failed. Note the created task ID if successful.
   c. Review the corresponding `add_task_show_output_<provider>_id_<id>.log` file (if created) for each successful `add-task` execution.
   d. **Compare the quality and completeness** of the task generated by each successful provider based on their `show` output. Assign a score (e.g., 1-10, 10 being best) based on relevance to the prompt, detail level, and correctness.
   e. Note any providers where `add-task` failed or where the task ID could not be extracted.
3. Identify any general explicit "[ERROR]" messages or stack traces throughout the *entire* log.
4. Identify any potential warnings or unusual output that might indicate a problem even if not marked as an explicit error.
5. Provide an overall assessment of the test run's health based *only* on the log content.

Return your analysis **strictly** in the following JSON format. Do not include any text outside of the JSON structure:

{
  "overall_status": "Success|Failure|Warning",
  "verified_steps": [ "Initialization", "PRD Parsing", /* ...other general steps observed... */ ],
  "provider_add_task_comparison": {
     "prompt_used": "... (extract from log if possible or state 'standard auth prompt') ...",
     "provider_results": {
       "anthropic": { "status": "Success|Failure|ID_Extraction_Failed|Set_Model_Failed", "task_id": "...", "score": "X/10 | N/A", "notes": "..." },
       "openai": { "status": "Success|Failure|...", "task_id": "...", "score": "X/10 | N/A", "notes": "..." },
       /* ... include all tested providers ... */
     },
     "comparison_summary": "Brief overall comparison of generated tasks..."
   },
  "detected_issues": [ { "severity": "Error|Warning|Anomaly", "description": "...", "log_context": "[Optional, short snippet from log near the issue]" } ],
  "llm_summary_points": [ "Overall summary point 1", "Provider comparison highlight", "Any major issues noted" ]
}

Here is the main log content:

%s
EOF
# Note: The final %s is a placeholder for printf later

  local full_prompt
  # Use printf to substitute the log content into the %s placeholder
  if ! printf -v full_prompt "$prompt_template" "$log_content"; then
    echo "[HELPER_ERROR] Failed to format prompt using printf." >&2
    # It's unlikely printf itself fails, but good practice
    return 1
  fi

  # Construct the JSON payload for Claude Messages API
  local payload
  payload=$(jq -n --arg prompt "$full_prompt" '{
    "model": "claude-3-haiku-20240307",  # Using Haiku for faster/cheaper testing
    "max_tokens": 3072, # Increased slightly
    "messages": [
      {"role": "user", "content": $prompt}
    ]
    # "temperature": 0.0 # Optional: Lower temperature for more deterministic JSON output
  }') || {
      echo "[HELPER_ERROR] Failed to create JSON payload using jq." >&2
      return 1
  }

  local response_raw response_http_code response_body
  # Capture body and HTTP status code separately
  response_raw=$(curl -s -w "\nHTTP_STATUS_CODE:%{http_code}" -X POST "$api_endpoint" \
       -H "Content-Type: application/json" \
       -H "x-api-key: $api_key" \
       -H "anthropic-version: 2023-06-01" \
       --data "$payload")

  # Extract status code and body
  response_http_code=$(echo "$response_raw" | grep '^HTTP_STATUS_CODE:' | sed 's/HTTP_STATUS_CODE://')
  response_body=$(echo "$response_raw" | sed '$d') # Remove last line (status code)

  if [ "$response_http_code" != "200" ]; then
      echo "[HELPER_ERROR] LLM API call failed with HTTP status $response_http_code." >&2
      echo "[HELPER_ERROR] Response Body: $response_body" >&2
      return 1
  fi

  if [ -z "$response_body" ]; then
      echo "[HELPER_ERROR] LLM API call returned empty response body." >&2
      return 1
  fi

  # Pipe the raw response body directly to the Node.js parser script
  if echo "$response_body" | node "${project_root}/tests/e2e/parse_llm_output.cjs" "$log_file"; then
      echo "[HELPER_SUCCESS] LLM analysis parsed and printed successfully by Node.js script."
      return 0 # Success
  else
      local node_exit_code=$?
      echo "[HELPER_ERROR] Node.js parsing script failed with exit code ${node_exit_code}."
      echo "[HELPER_ERROR] Raw API response body (first 500 chars): $(echo "$response_body" | head -c 500)"
      return 1 # Failure
  fi
}

# Export the function so it might be available to subshells if sourced
export -f analyze_log_with_llm