"""
Core data processing and analysis logic for the PharmaCircle AI Data Analyst.

This module orchestrates the main analysis workflow:
1.  Takes a user's natural language query.
2.  Uses the LLM to generate a structured analysis plan.
3.  Executes parallel queries against Solr for quantitative and qualitative data.
4.  Generates a data visualization using the LLM.
5.  Synthesizes the findings into a comprehensive, user-facing report.
"""

import json
import re
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import concurrent.futures
import copy
import google.generativeai as genai
import urllib
import pysolr
import config # Import the config module to access remote host details
import tiktoken

from llm_prompts import (
    get_analysis_plan_prompt,
    get_synthesis_report_prompt,
    get_visualization_code_prompt
)
from extract_results import get_search_list_params

def parse_suggestions_from_report(report_text):
    """Extracts numbered suggestions from the report's markdown text."""
    suggestions_match = re.search(r"### (?:Deeper Dive: Suggested Follow-up Analyses|Suggestions for Further Exploration)\s*\n(.*?)$", report_text, re.DOTALL | re.IGNORECASE)
    if not suggestions_match: return []
    suggestions_text = suggestions_match.group(1)
    suggestions = re.findall(r"^\s*\d+\.\s*(.*)", suggestions_text, re.MULTILINE)
    return [s.strip() for s in suggestions]

def llm_generate_analysis_plan_with_history(llm_model, natural_language_query, chat_history):
    """
    Generates a complete analysis plan from a user query, considering chat history
    and dynamic field suggestions from an external API.
    """
    search_fields, search_name, field_mappings = [], "", {}
    intent = None
    try:
        intent, search_fields, search_name, field_mappings = get_search_list_params(natural_language_query)
        print(f"API returned intent: '{intent}', core: '{search_name}' with {len(search_fields)} fields and {len(field_mappings)} mappings.")

        if intent != 'search_list':
            print(f"API returned intent '{intent}' which is not 'search_list'. Aborting analysis.")
            return None, None, None, intent, None, None, None

    except Exception as e:
        print(f"Warning: Could not retrieve dynamic search fields. Proceeding without them. Error: {e}")
        return None, [], None, 'api_error', None, None, None

    core_name = search_name if search_name else 'news'

    mapped_search_fields = []
    if search_fields and field_mappings:
        for field in search_fields:
            original_name = field.get('field_name')
            mapped_field = field.copy()
            if original_name in field_mappings:
                mapped_field['field_name'] = field_mappings[original_name]
                print(f"Mapped field '{original_name}' to '{mapped_field['field_name']}'")
            mapped_search_fields.append(mapped_field)
    else:
        mapped_search_fields = search_fields

    prompt = get_analysis_plan_prompt(natural_language_query, chat_history, mapped_search_fields, core_name)

    try:
        response = llm_model.generate_content(prompt)
        encoding = tiktoken.encoding_for_model("gpt-4")
        input_token_count = len(encoding.encode(prompt))
        output_token_count = len(encoding.encode(response.text))
        total_token_count = (input_token_count if input_token_count is not None else 0) + (output_token_count if output_token_count is not None else 0)
        cleaned_text = re.sub(r'```json\s*|\s*```', '', response.text, flags=re.MULTILINE | re.DOTALL).strip()
        plan = json.loads(cleaned_text)
        return plan, mapped_search_fields, core_name, intent, input_token_count, output_token_count, total_token_count
    except json.JSONDecodeError as e:
        raw_response_text = response.text if 'response' in locals() else 'N/A'
        print(f"Error decoding JSON from LLM response: {e}\nRaw Response:\n{raw_response_text}")
        return None, mapped_search_fields, core_name, intent, None, None, None
    except Exception as e:
        raw_response_text = response.text if 'response' in locals() else 'N/A'
        print(f"Error in llm_generate_analysis_plan_with_history: {e}\nRaw Response:\n{raw_response_text}")
        return None, mapped_search_fields, core_name, intent, None, None, None

def execute_quantitative_query(solr_client, plan):
    """Executes the facet query to get aggregate data."""
    if not plan or 'quantitative_request' not in plan or 'json.facet' not in plan.get('quantitative_request', {}):
        print("Skipping quantitative query due to incomplete plan.")
        return None, None
    try:
        params = {
            "q": plan.get('query_filter', '*_*'),
            "rows": 0,
            "json.facet": json.dumps(plan['quantitative_request']['json.facet'])
        }
        base_url = f"{solr_client.url}/select"
        query_string = urllib.parse.urlencode(params)
        full_url = f"{base_url}?{query_string}"
        
        # Create the public-facing URL for display
        public_url = full_url.replace(f'http://127.0.0.1:{config.LOCAL_BIND_PORT}', f'http://{config.REMOTE_SOLR_HOST}:{config.REMOTE_SOLR_PORT}')
        print(f"[DEBUG] Solr QUANTITATIVE query URL (PUBLIC): {public_url}")
        results = solr_client.search(**params)
        return results.raw_response.get("facets", {}), public_url
    except pysolr.SolrError as e:
        print(f"Solr Error in quantitative query on core {solr_client.url}: {e}")
        return None, None
    except Exception as e:
        print(f"Unexpected error in quantitative query: {e}")
        return None, None

def execute_qualitative_query(solr_client, plan):
    """Executes the grouping query to get the best example docs."""
    if not plan or 'qualitative_request' not in plan:
        print("Skipping qualitative query due to incomplete plan.")
        return None, None
    try:
        qual_request = copy.deepcopy(plan['qualitative_request'])
        params = {
            "q": plan.get('query_filter', '*_*'),
            "rows": 5,
            "fl": "*,score",
            **qual_request
        }
        base_url = f"{solr_client.url}/select"
        query_string = urllib.parse.urlencode(params)
        full_url = f"{base_url}?{query_string}"
        
        # Create the public-facing URL for display
        public_url = full_url.replace(f'http://127.0.0.1:{config.LOCAL_BIND_PORT}', f'http://{config.REMOTE_SOLR_HOST}:{config.REMOTE_SOLR_PORT}')
        print(f"[DEBUG] Solr QUALITATIVE query URL (PUBLIC): {public_url}")
        results = solr_client.search(**params)
        return results.grouped, public_url
    except pysolr.SolrError as e:
        print(f"Solr Error in qualitative query on core {solr_client.url}: {e}")
        return None, None
    except Exception as e:
        print(f"Unexpected error in qualitative query: {e}")
        return None, None

def llm_synthesize_enriched_report_stream(llm_model, query, quantitative_data, qualitative_data, plan):
    """
    Generates an enriched report by synthesizing quantitative aggregates
    and qualitative examples, and streams the result.
    """
    prompt = get_synthesis_report_prompt(query, quantitative_data, qualitative_data, plan)
    try:
        response_stream = llm_model.generate_content(prompt, stream=True)        
        response_text = ""
        for chunk in response_stream:
            yield {"text": chunk.text, "tokens": None}
            response_text += chunk.text
        encoding = tiktoken.encoding_for_model("gpt-4")
        input_token_count = len(encoding.encode(prompt))
        output_token_count = len(encoding.encode(response_text))
        total_token_count = (input_token_count if input_token_count is not None else 0) + (output_token_count if output_token_count is not None else 0)
        tokens = {
            "input": input_token_count,
            "output": output_token_count,
            "total": total_token_count,
        }
        yield {"text": None, "tokens": tokens}
    except Exception as e:
        print(f"Error in llm_synthesize_enriched_report_stream: {e}")
        yield {"text": "Sorry, an error occurred while generating the report. Please check the logs for details.", "tokens": None}

def llm_generate_visualization_code(llm_model, query_context, facet_data):
    """Generates Python code for visualization based on query and data."""
    prompt = get_visualization_code_prompt(query_context, facet_data)
    try:
        generation_config = genai.types.GenerationConfig(temperature=0)
        response = llm_model.generate_content(prompt, generation_config=generation_config)
        encoding = tiktoken.encoding_for_model("gpt-4")
        input_token_count = len(encoding.encode(prompt))
        output_token_count = len(encoding.encode(response.text))
        total_token_count = (input_token_count if input_token_count is not None else 0) + (output_token_count if output_token_count is not None else 0)
        code = re.sub(r'^```python\s*|```$', '', response.text, flags=re.MULTILINE)
        return code, input_token_count, output_token_count, total_token_count
    except Exception as e:
        raw_response_text = response.text if 'response' in locals() else 'N/A'
        print(f"Error in llm_generate_visualization_code: {e}\nRaw response: {raw_response_text}")
        return

def execute_viz_code_and_get_path(viz_code, facet_data):
    """Executes visualization code and returns the path to the saved plot image."""
    if not viz_code:
        return None

    # --- SECURITY WARNING ---
    # The following code executes code generated by an LLM. This is a security
    # risk and should be handled with extreme care in a production environment.
    # Ideally, this code should be run in a sandboxed environment.
    print("\n--- WARNING: Executing LLM-generated code. ---")

    try:
        if not os.path.exists('/tmp/plots'):
            os.makedirs('/tmp/plots')
        plot_path = f"/tmp/plots/plot_{datetime.datetime.now().timestamp()}.png"

        # Create a restricted global environment for execution
        exec_globals = {'facet_data': facet_data, 'plt': plt, 'sns': sns, 'pd': pd}


        exec(viz_code, exec_globals)

        fig = exec_globals.get('fig')
        if fig:
            fig.savefig(plot_path, bbox_inches='tight')
            plt.close(fig)
            print("--- LLM-generated code executed successfully. ---")
            return plot_path
        else:
            print("--- LLM-generated code did not produce a 'fig' object. ---")
            return None

    except Exception as e:
        print(f"\n--- ERROR executing visualization code: ---")
        print(f"Error: {e}")
        print(f"--- Code---\n{viz_code}")
        print("-----------------------------------------")
        return None