"""
LLM Response Generator
Converts tool execution results into natural language responses using OpenAI
"""

import os
import sys
from pathlib import Path

# Add project root to path
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

import openai
from typing import Dict, Any, Optional
from src.config.credentials import CredentialsManager


class LLMResponseGenerator:
    """
    Generates natural language responses from tool results using LLM
    
    This is the FINAL step in the pipeline:
    User Question → Tool Selection → Tool Execution → LLM Response → User
    """
    
    def __init__(self, api_key: str = None, model: str = None):
        """
        Initialize LLM Response Generator
        
        Args:
            api_key: OpenAI API key (optional, loads from .env if not provided)
            model: OpenAI model to use (default: value from OPENAI_MODEL_NAME or gpt-4o-mini)
        """
        # Get API key
        if api_key is None:
            creds = CredentialsManager()
            api_key = creds.get_api_key("openai")
        
        self.client = openai.OpenAI(api_key=api_key)
        self.model = model or os.getenv("OPENAI_MODEL_NAME", "gpt-4o-mini")
    
    def generate_response(
        self,
        user_question: str,
        tool_name: str,
        tool_result: Dict[str, Any],
        conversation_context: list = None
    ) -> str:
        """
        Generate natural language response from tool result
        
        Args:
            user_question: Original user question
            tool_name: Name of the tool that was used
            tool_result: Result from tool execution
            conversation_context: Optional list of previous messages for context
        
        Returns:
            Natural language response string
        
        Example:
            >>> generator = LLMResponseGenerator()
            >>> result = {
            ...     "city": "London",
            ...     "temperature": 15,
            ...     "description": "partly cloudy"
            ... }
            >>> response = generator.generate_response(
            ...     "What's the weather in London?",
            ...     "weather",
            ...     result
            ... )
            >>> print(response)
            "The weather in London is currently 15°C with partly cloudy skies..."
        """
        # Route to appropriate formatter (pass context)
        if tool_name == "weather":
            return self._generate_weather_response(user_question, tool_result, conversation_context)
        elif tool_name == "soil":
            return self._generate_soil_response(user_question, tool_result, conversation_context)
        elif tool_name == "rag" or tool_name == "documentation":
            # RAG tool is now CDMS - use CDMS response generator
            return self._generate_cdms_response(user_question, tool_result, conversation_context)
        elif tool_name in ["cdms_label", "cdms", "pesticide_label"]:
            return self._generate_cdms_response(user_question, tool_result, conversation_context)
        elif tool_name in ["agriculture_web", "ag_web"]:
            return self._generate_agriculture_web_response(user_question, tool_result, conversation_context)
        else:
            return self._generate_generic_response(user_question, tool_result, conversation_context)
    
    def _generate_weather_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """Generate natural language response for weather data
        
        IMPORTANT: Only uses weather data. Does NOT call soil or RAG tools.
        """
        
        # Build context from weather data ONLY
        context = f"""
User asked: "{user_question}"

Weather data retrieved:
- Location: {data.get('city', 'Unknown')}, {data.get('country', '')}
- Temperature: {data.get('temperature', 'N/A')}°C
- Feels like: {data.get('feels_like', 'N/A')}°C
- Conditions: {data.get('description', 'N/A')}
- Humidity: {data.get('humidity', 'N/A')}%
- Wind speed: {data.get('wind_speed', 'N/A')} m/s
"""
        
        # Create prompt for LLM - ONLY weather data
        prompt = f"""You are a helpful weather assistant. Generate a natural, conversational response based ONLY on the weather data provided below.

{context}

Requirements:
1. Answer directly and conversationally using ONLY the weather data above
2. Include relevant weather details from the data
3. Add a helpful suggestion if appropriate (e.g., "Bring an umbrella" if rainy)
4. Keep it concise (2-3 sentences)
5. Do NOT mention soil data or documentation (those are separate tools)
6. Use weather emoji if it enhances the message
7. Be friendly and helpful

Generate the response:"""
        
        # Call OpenAI
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful weather assistant that provides clear, friendly weather information."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=200
        )
        
        return response.choices[0].message.content.strip()
    
    def _generate_soil_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """Generate natural language response for soil data
        
        IMPORTANT: Only uses soil data. Does NOT call weather or RAG tools.
        """
        
        properties = data.get('properties', {})
        location = data.get('location', {})
        
        # Build context from soil data ONLY
        context = f"""
User asked: "{user_question}"

Soil data retrieved for location ({location.get('lat', 'N/A')}, {location.get('lon', 'N/A')}):
"""
        
        for prop_name, prop_data in properties.items():
            if isinstance(prop_data, dict):
                value = prop_data.get('value', 'N/A')
                unit = prop_data.get('unit', '')
                label = prop_data.get('label', prop_name)
                context += f"- {label}: {value} {unit}\n"
        
        # Create prompt for LLM - ONLY soil data
        prompt = f"""You are a helpful agricultural advisor. Generate a natural, informative response based ONLY on the soil data provided below.

{context}

Requirements:
1. Explain the soil properties in simple terms using ONLY the data above
2. Mention what the data means for agriculture/gardening
3. Be clear and educational
4. Keep it conversational (2-4 sentences)
5. Do NOT mention weather data or documentation (those are separate tools)
6. Add practical advice if relevant

Generate the response:"""
        
        # Call OpenAI
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful agricultural advisor that explains soil data clearly."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=250
        )
        
        return response.choices[0].message.content.strip()
    
    def _generate_rag_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """Generate natural language response for RAG results
        
        IMPORTANT: Only uses information from the RAG tool (PDFs about agriculture/pesticides).
        Does NOT call weather or soil tools.
        """
        
        api_matches = data.get('api_matches', [])
        doc_context = data.get('document_context', [])
        
        # Build context - ONLY from RAG search results (agriculture/pesticides content)
        context = f"""
User asked: "{user_question}"

Information found from agriculture knowledge base (CDMS labels about pesticides, insecticides, farming):
"""
        
        # Add API catalog matches (from fuzzy matching - might find agriculture-related APIs)
        if api_matches:
            context += "\nRelevant items from catalog:\n"
            for api in api_matches[:3]:
                context += f"- {api.get('api_name', 'N/A')}: {api.get('description', 'N/A')}\n"
        
        # Add PDF document excerpts (from vector search - agriculture/pesticides content)
        if doc_context:
            context += "\nRelevant information from documentation:\n"
            for doc in doc_context[:2]:
                content_preview = doc.get('content', '')[:400]  # First 400 chars for agriculture content
                context += f"- From {doc.get('source_file', 'document')} (Page {doc.get('page_number', 'N/A')}): {content_preview}...\n"
        
        # If no results found
        if not api_matches and not doc_context:
            context += "\nNo relevant information found in the knowledge base about agriculture, pesticides, or insecticides."
            context += "\n\nIMPORTANT: The user may need to process PDFs first by running: python src/cdms/document_loader.py"
        
        # Create prompt for LLM - agriculture/pesticides focus
        prompt = f"""You are a helpful agricultural advisor. Answer the user's question about agriculture, pesticides, insecticides, or farming based ONLY on the information provided below.

{context}

IMPORTANT:
- Only use information from the documentation provided above (CDMS labels about pesticides/agriculture)
- Do NOT make up information
- Do NOT reference weather or soil data (those are separate tools)
- Focus on agriculture, pesticides, insecticides, farming practices
- If the documentation doesn't contain the answer, say so clearly
- Cite sources when relevant (mention document name and page)
- Be clear, educational, and helpful (3-5 sentences)

Generate the response:"""
        
        # Call OpenAI
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful agricultural advisor. You answer questions about agriculture, pesticides, insecticides, and farming based on provided documentation."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=300
        )
        
        return response.choices[0].message.content.strip()
    
    def _generate_cdms_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """
        Generate natural language response for CDMS label search results
        
        Handles both old format (Tavily-only) and new format (RAG pipeline with page citations)
        """
        # Check if this is the new RAG pipeline format
        rag_chunks = data.get('rag_chunks', [])

        if rag_chunks:
            # New RAG pipeline format - use page citations
            return self._generate_cdms_rag_response(user_question, data, conversation_context)

        # No retrieved label text. The Tavily path below prompts the LLM for an
        # "About this Product" section, which with no grounding data it answers from
        # parametric memory -- i.e. it invents active ingredients and rates for a label
        # we do not have. On a legally-binding pesticide label that is the worst failure
        # mode we have, so abstain instead of generating. Only fall through to the live
        # Tavily path when it actually has results to summarise.
        labels = data.get('labels') or data.get('raw_tavily_results') or []
        if not labels or os.getenv("CDMS_OFFLINE_INDEX", "1") == "1":
            return self._abstain(data.get('product_name'))

        # Old format - Tavily-only search (legacy live mode, has real results)
        return self._generate_cdms_tavily_response(user_question, data, conversation_context)

    # When the catalog can't resolve a product, the tool falls back to a crude
    # keyword grab that yields phrases like "is application rate Liberty". Don't echo
    # that back at the user -- strip the query scaffolding and keep the actual name.
    _QUERY_WORDS = {
        "what", "whats", "is", "are", "the", "a", "an", "of", "for", "on", "in", "to",
        "how", "do", "i", "my", "apply", "application", "rate", "rates", "label",
        "use", "using", "much", "many", "can", "should", "safety", "mix", "mixing",
    }

    def _clean_product_name(self, raw: Optional[str]) -> Optional[str]:
        if not raw:
            return None
        words = [w for w in str(raw).split() if w.lower().strip(",.?") not in self._QUERY_WORDS]
        if not words or len(words) > 3:
            return None
        return " ".join(words).strip(",.?").title()

    def _abstain(self, product_name: Optional[str] = None) -> str:
        """Fixed, non-generative refusal. Never calls the LLM -- there is nothing to ground."""
        cleaned = self._clean_product_name(product_name)
        who = f"**{cleaned}**" if cleaned else "that product"
        try:
            from src.cdms.product_catalog import get_catalog
            available = ", ".join(sorted(get_catalog().available_products()))
        except Exception:
            available = ""

        msg = (
            f"I don't have the label for {who}, so I can't answer that.\n\n"
            "I only answer from pesticide labels I have indexed, and I won't guess "
            "at rates, ingredients, or safety intervals for a label I don't hold."
        )
        if available:
            msg += f"\n\nLabels I do have: {available}."
        return msg
    
    def _generate_cdms_rag_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """
        Generate response from RAG chunks with page citations and PDF links
        
        NEW: Uses RAG search results with exact page numbers and PDF download URLs
        """
        product_name = data.get('product_name', 'Unknown product')
        rag_chunks = data.get('rag_chunks', [])
        pdfs_downloaded = data.get('pdfs_downloaded', 0)
        total_chunks = data.get('total_chunks_found', 0)
        tavily_labels = data.get('tavily_labels', [])

        # ------------------------------------------------------------------
        # CROSS-PRODUCT GUARD (fix for "it answers from a different herbicide")
        # If the user asked about a specific product we recognise, but none of
        # the retrieved chunks actually come from that product, refuse instead
        # of answering from whatever the vector search surfaced (which skews to
        # the product that dominates the index -- Roundup here, "Trust" in the
        # deployed data). Better to abstain than to cite the wrong label.
        # ------------------------------------------------------------------
        try:
            from src.cdms.product_catalog import cross_product_abstention, get_catalog
            _sources = [c.get("source_file", "") for c in rag_chunks]
            _requested = cross_product_abstention(user_question, _sources)
            if _requested:
                _have = ", ".join(sorted(get_catalog().available_products()))
                return (
                    f"I couldn't find information about **{_requested}** in its "
                    f"CDMS label for that question, and I won't answer from a "
                    f"different product's label. You could rephrase the question, "
                    f"or I can try to fetch the {_requested} label.\n\n"
                    f"_Labels I currently have indexed: {_have}._"
                )
        except Exception as _guard_err:  # never let the guard break normal flow
            print(f"⚠️  Cross-product guard skipped: {_guard_err}")
        
        # Create mapping of URLs to titles for better citations
        url_to_title = {}
        for label in tavily_labels:
            url = label.get('url', '')
            title = label.get('title', '')
            if url and title:
                # Clean up title
                if title.startswith('PDF'):
                    title = title[3:].strip()
                url_to_title[url] = title
        
        # Also get titles from downloaded PDFs
        download_info = data.get('download_info', {})
        downloaded_pdfs = download_info.get('downloaded_pdfs', [])
        for pdf_info in downloaded_pdfs:
            url = pdf_info.get('url', '')
            filename = pdf_info.get('filename', '')
            if url and url not in url_to_title:
                # Human product name (drops the stored file's hash suffix), not
                # "Atrazine 71bf2ea0fe92".
                from src.cdms.product_catalog import normalize_filename
                url_to_title[url] = normalize_filename(filename).title() or "the label"
        
        # Build context from RAG chunks with page numbers, PDF titles, and URLs
        chunks_text = []
        pages_cited = {}  # Changed to dict: {page_num: {pdf_url: pdf_title}}
        pdf_urls_used = set()
        
        # PHASE 2 FIX: Debug - log page numbers from chunks
        print(f"🔍 DEBUG: Processing {len(rag_chunks)} RAG chunks for page numbers...")
        for i, chunk in enumerate(rag_chunks[:3], 1):  # Log first 3 chunks
            original_page = chunk.get('page_number', 0)
            print(f"   Chunk {i}: page_number={original_page}, chunk_index={chunk.get('chunk_index', 0)}")
        
        for i, chunk in enumerate(rag_chunks, 1):
            page_num = chunk.get('page_number', 0)
            content = chunk.get('content', '')
            score = chunk.get('score', 0.0)
            source_file = chunk.get('source_file', 'Unknown')
            pdf_url = chunk.get('pdf_url', '')
            chunk_index = chunk.get('chunk_index', 0)
            
            # PHASE 2 FIX: Ensure page number is always valid (> 0)
            original_page_num = page_num
            if page_num <= 0:
                # Fallback: Estimate page number based on chunk_index
                if chunk_index > 0:
                    # Rough estimate: 3 chunks per page
                    page_num = (chunk_index // 3) + 1
                else:
                    # Last resort: use page 1
                    page_num = 1
                print(f"⚠️  Warning: Invalid page_number ({original_page_num}) for chunk {i}, using estimated value: {page_num}")
            
            # Get PDF title — a clean human product name (no file hash), e.g.
            # "24-d_88d837d3fd73.pdf" -> "24-D", not "24-D 88D837D3Fd73".
            from src.cdms.product_catalog import normalize_filename
            pdf_title = url_to_title.get(pdf_url) or normalize_filename(source_file).title() or "the label"
            
            # Format chunk with page number, PDF title, and URL
            # PHASE 2 FIX: Make page numbers more prominent in the output
            if pdf_url:
                chunk_text = f"**Page {page_num}** of **{pdf_title}** (Relevance: {score:.2f})\n"
                chunk_text += f"PDF URL: {pdf_url}\n"
                chunk_text += f"Source: {source_file}\n"
                pdf_urls_used.add(pdf_url)
                # Track page citations with PDF info
                if page_num not in pages_cited:
                    pages_cited[page_num] = {}
                pages_cited[page_num][pdf_url] = pdf_title
            else:
                chunk_text = f"**Page {page_num}** of **{pdf_title}** (Relevance: {score:.2f})\n"
                chunk_text += f"Source: {source_file}\n"
            chunk_text += f"\nContent:\n{content}"
            
            chunks_text.append(chunk_text)
        
        context_chunks = "\n\n---\n\n".join(chunks_text)
        
        # Build list of all available PDF URLs with descriptive titles
        # Get URLs from multiple sources to ensure we have all of them
        pdf_urls_list = []
        pdf_urls_seen = set()
        
        # Create a mapping of URLs to titles from Tavily labels first
        url_to_title = {}
        for label in tavily_labels:
            url = label.get('url', '')
            title = label.get('title', '')
            if url and title:
                url_to_title[url] = title
        
        # First, get URLs from downloaded PDFs (most reliable)
        download_info = data.get('download_info', {})
        downloaded_pdfs = download_info.get('downloaded_pdfs', [])
        for pdf_info in downloaded_pdfs:
            url = pdf_info.get('url', '')
            if url and url not in pdf_urls_seen:
                # Use title from Tavily if available, otherwise use filename
                title = url_to_title.get(url, pdf_info.get('filename', 'Label PDF'))
                # Clean up title (remove "PDF" prefix if present)
                if title.startswith('PDF'):
                    title = title[3:].strip()
                pdf_urls_list.append(f"- {title}: {url}")
                pdf_urls_seen.add(url)
        
        # Also get URLs from pdf_urls field
        pdf_urls = data.get('pdf_urls', [])
        for url in pdf_urls:
            if url and url not in pdf_urls_seen:
                title = url_to_title.get(url, 'Label PDF')
                if title.startswith('PDF'):
                    title = title[3:].strip()
                pdf_urls_list.append(f"- {title}: {url}")
                pdf_urls_seen.add(url)
        
        # Finally, get any remaining URLs from Tavily labels
        for label in tavily_labels:
            url = label.get('url', '')
            title = label.get('title', 'No title')
            if url and url not in pdf_urls_seen:
                if title.startswith('PDF'):
                    title = title[3:].strip()
                pdf_urls_list.append(f"- {title}: {url}")
                pdf_urls_seen.add(url)
        
        pdf_urls_text = "\n".join(pdf_urls_list) if pdf_urls_list else "(none — do NOT add a PDF Downloads or Sources section; the app appends sources automatically)"
        
        # Build conversation context summary if available
        context_summary = ""
        if conversation_context:
            context_parts = []
            for msg in conversation_context[-4:]:  # Last 4 messages
                role = msg.get("role", "user")
                content = msg.get("content", "")[:200]  # Truncate long messages
                context_parts.append(f"{'User' if role == 'user' else 'Assistant'}: {content}")
            if context_parts:
                context_summary = "\n\nPrevious conversation:\n" + "\n".join(context_parts)
        
        context = f"""
User asked: "{user_question}"
{context_summary}

Product: {product_name}

I searched the official CDMS label database and found {total_chunks} relevant excerpt(s) from the pesticide labels.

Relevant excerpts with page citations:

{context_chunks}

Pages cited (with PDF source):
{chr(10).join([f"- Page {page}: {', '.join([f'{title} ({url})' for url, title in pdfs.items()])}" for page, pdfs in sorted(pages_cited.items()) if page > 0])}

Available PDF Downloads (YOU MUST INCLUDE ALL OF THESE IN YOUR RESPONSE):
{pdf_urls_text}

CRITICAL INSTRUCTIONS - READ CAREFULLY:

1. Answer the user's question directly using the provided excerpts

2. PAGE NUMBERS ARE MANDATORY - YOU MUST INCLUDE THEM:
   - EVERY piece of information you cite MUST include the page number
   - Format: "According to page X of [PDF Title]..." or "Page X of [PDF Title] states..."
   - Example: "The application rate is 2 quarts per acre (see page 5 of Roundup Label)"
   - Example: "Page 3 of the Sevin Label indicates a 7-day waiting period"
   - NEVER provide information without citing the page number
   - If you mention ANY fact from the excerpts, you MUST include which page it came from

3. PDF TITLES AND LINKS:
   - Always specify which PDF document you're citing (use the PDF title from the excerpts)
   - Include clickable links to PDFs when available: [PDF Title](PDF_URL)
   - Format: "see page X of [PDF Title](PDF_URL)" or "page X of [PDF Title](PDF_URL) states..."

4. If this is a follow-up question (like "What about safety?" or "How do I mix it?"), use the conversation context to understand what product/topic the user is asking about

5. Quote relevant sections when providing specific data (rates, safety info, etc.) - ALWAYS with page numbers

6. Be precise and factual - only use information from the provided excerpts

6b. BE CONCISE: answer the specific question directly in 3-6 sentences. Do not
    restate the question, add generic pesticide-safety boilerplate, or pad the
    answer. Specific questions deserve specific, short answers.

7. If the question asks for specific information (like application rates), provide the exact numbers from the excerpts WITH page citations

8. CRITICAL: You MUST include ALL PDF download links from the "Available PDF Downloads" section above in your response. Format them in a "📄 PDF Downloads" section at the end.

9. Use the exact URLs provided above - do not modify or shorten them

REMINDER: Page numbers are NOT optional. Every citation must include a page number. If you fail to include page numbers, your response is incomplete.
"""
        
        system_prompt = """You are an expert agriculture assistant specializing in pesticide labels with access to exact excerpts from official CDMS labels.

MANDATORY REQUIREMENTS - PAGE NUMBERS ARE REQUIRED:

1. Answer the user's question directly using the provided excerpts

2. PAGE NUMBERS ARE MANDATORY - YOU CANNOT OMIT THEM:
   - EVERY fact, quote, or piece of information you provide MUST include the page number
   - Format examples:
     * "According to page 5 of Roundup Label, the application rate is..."
     * "Page 3 of the Sevin Label states that the waiting period is..."
     * "The mixing instructions on page 7 of the <Product> label indicate..."
   - NEVER say "the label states" without including the page number
   - NEVER provide information without citing which page it came from
   - If you reference ANY information from the excerpts, you MUST include the page number

3. PDF CITATIONS MUST INCLUDE:
   - The page number (REQUIRED)
   - The PDF title/name (REQUIRED)
   - A clickable link to the PDF ONLY if its URL is supplied in the "Available PDF Downloads" section below
   - NEVER invent, guess, or reuse an example URL. A wrong link to a legally-binding
     label is dangerous. If no URL is provided, cite "page N of the <Product> label" with NO link
   - NEVER cite just a page number without the product/label name

4. If this is a follow-up question (e.g., "What about safety?" after asking about application rates), use the conversation context to understand the product/topic being discussed

5. Quote relevant sections when providing specific data (rates, safety info, mixing instructions, etc.) - ALWAYS with page numbers

6. Be precise and factual - only use information from the provided excerpts

7. If specific numbers or rates are mentioned, include them exactly WITH page citations

8. CRITICAL: You MUST include ALL PDF download links provided in the "Available PDF Downloads" section at the end of your response in a "📄 PDF Downloads" section

Format your response like this:

"[Direct answer to the user's question, using information from the excerpts. Be specific and cite page numbers with PDF titles. 

EXAMPLE OF CORRECT FORMATTING:
"According to page 5 of Roundup Label, the application rate is 2 quarts per acre. Page 7 of the same label indicates a 14-day waiting period."

EXAMPLE OF INCORRECT FORMATTING (DO NOT DO THIS):
"The application rate is 2 quarts per acre."  ❌ MISSING PAGE NUMBER
"The label states the rate is 2 quarts per acre."  ❌ MISSING PAGE NUMBER
]

**Key Information:**
[Quote or summarize relevant sections with page citations that include PDF titles. EVERY sentence that references information from the excerpts MUST include a page number.]

**REQUIRED Citation Format Examples (cite page + label name; add a link ONLY if a URL is supplied below):**
- "The application rate is 1.5-2.5 quarts per acre (page 5 of the Roundup QuikPRO label)."
- "Hand protection requirements are specified on page 5 of the Roundup Custom safety data sheet."
- "Page 3 of the Sevin label states: 'Apply at a rate of 1-2 pounds per acre.'"

CRITICAL: Every citation MUST include the page number and the label name. Add a clickable
[label](URL) link ONLY when that exact URL appears in the "Available PDF Downloads" section
below — otherwise cite without a link. NEVER omit the page number; NEVER fabricate a URL.

**📄 PDF Downloads:**
You MUST include ALL the PDF links from the "Available PDF Downloads" section below. Format them as clickable markdown links with descriptive labels:
- [Label Name/Description](Full PDF URL)
- [Label Name/Description](Full PDF URL)

Include this "📄 PDF Downloads" section ONLY if the "Available PDF Downloads" section below
actually lists URLs. If it is empty (no live PDFs were fetched), OMIT the section entirely —
do NOT fabricate links.

All information is from the official CDMS label database. Be conversational, helpful, and emphasize accuracy and safety.
IMPORTANT: End with a "📄 PDF Downloads" section ONLY when real URLs are provided in the context; never invent one."
"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": context}
            ],
            temperature=0.7,
            max_tokens=1000
        )
        
        answer = response.choices[0].message.content.strip()
        return self._finalize_cdms_answer(answer, rag_chunks)

    def _sources_footer(self, rag_chunks: list) -> str:
        """Clean 'Sources' line from the retrieved chunks: the label + the exact
        pages the answer used. Links the label only when a real pdf_url exists
        (online-fetched); offline just names the label + pages (no fabricated URL).
        Replaces the old, ugly '(No PDF URLs available)' downloads section."""
        from src.cdms.product_catalog import normalize_filename
        by_prod: Dict[str, Dict] = {}
        for c in rag_chunks or []:
            prod = normalize_filename(c.get("source_file", "")).title() or "Label"
            info = by_prod.setdefault(prod, {"pages": set(), "url": ""})
            try:
                p = int(c.get("page_number", 0))
                if p > 0:
                    info["pages"].add(p)
            except (TypeError, ValueError):
                pass
            url = c.get("pdf_url", "")
            if url and not info["url"]:
                info["url"] = url
        if not by_prod:
            return ""
        import urllib.parse
        parts = []
        for prod, info in sorted(by_prod.items()):
            # Online: the exact PDF Tavily fetched. Offline (no stored URL): a
            # product-specific cdms.net search so the source is still clickable —
            # never a fabricated/guessed direct PDF (that risks the wrong label).
            url = info["url"] or (
                "https://www.cdms.net/Label-Database?" +
                urllib.parse.urlencode({"searchStr": f"{prod}"})
            )
            label = f"[{prod} label]({url})"
            if info["pages"]:
                label += " — p. " + ", ".join(str(p) for p in sorted(info["pages"]))
            parts.append(label)
        return "\n\n**Sources:** " + "; ".join(parts)

    def _finalize_cdms_answer(self, answer: str, rag_chunks: list) -> str:
        """Strip any model-added 'PDF Downloads' block (offline has no URLs, so it
        printed an empty '(No PDF URLs available)'), then append a real, code-built
        Sources line so offline answers still show their sources."""
        import re
        answer = re.split(r"\n\s*(?:📄\s*)?\*{0,2}\s*PDF Downloads\b\*{0,2}\s*:?", answer)[0].rstrip()
        answer = re.sub(r"\(?No PDF URLs available\)?\s*$", "", answer).rstrip()
        return (answer + self._sources_footer(rag_chunks)).strip()

    def _generate_cdms_tavily_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """
        Generate response from Tavily-only search (old format, fallback)
        """
        product_name = data.get('product_name', 'Unknown product')
        ingredient = data.get('active_ingredient')
        summary = data.get('summary', '')
        labels = data.get('labels', [])
        
        # Build context with citation emphasis
        label_list = []
        for i, label in enumerate(labels, 1):
            label_list.append(
                f"{i}. {label.get('title', 'No title')}\n"
                f"   URL: {label.get('url', '')}\n"
                f"   Relevance: {label.get('relevance', 0):.2f}\n"
                f"   Preview: {label.get('snippet', '')[:150]}..."
            )
        
        labels_text = "\n".join(label_list) if label_list else "No labels found"
        
        context = f"""
User asked: "{user_question}"

CDMS Label Search Results:
Product: {product_name}
{f'Active Ingredient: {ingredient}' if ingredient else ''}

Tavily AI Summary:
{summary}

Found {len(labels)} label(s):
{labels_text}

IMPORTANT: 
1. Answer the user's question based on the summary and labels
2. Provide direct PDF download links
3. Be clear about what information is available
"""
        
        system_prompt = """You are an expert agriculture assistant specializing in pesticide information and safety.

Your response MUST be comprehensive and include:
1. Brief introduction about the product
2. Key safety information from the AI summary
3. All available labels with clear download links
4. Helpful context about using the labels

Format your response like this:

"I found [X] label(s) for **[Product Name]** from the CDMS database.

**About this Product:**
[Provide a clear summary of what the product is, its uses, and key safety info from the AI summary. Be specific and practical.]

**Available Labels:**
1. **[Label Name]**
   📄 Download: [URL]
   
2. **[Label Name]**
   📄 Download: [URL]

**💡 Tip:** These labels contain important safety information, application rates, and usage instructions. Always read the full label before use.

All labels are from the official CDMS database."

Be conversational, helpful, and emphasize safety. Use the information provided to give farmers actionable guidance."
"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": context}
            ],
            temperature=0.7,
            max_tokens=800
        )
        
        return response.choices[0].message.content.strip()
    
    def _generate_agriculture_web_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """
        Generate natural language response for agriculture web search results
        
        IMPORTANT: Must include ALL citations with URLs
        """
        query = data.get('query', user_question)
        answer = data.get('answer', '')
        sources = data.get('sources', [])
        citations = data.get('citations', '')
        
        # Build context with citation emphasis
        source_list = []
        for i, source in enumerate(sources, 1):
            source_list.append(
                f"{i}. {source.get('title', 'No title')}\n"
                f"   URL: {source.get('url', '')}\n"
                f"   Snippet: {source.get('snippet', '')[:200]}..."
            )
        
        sources_text = "\n".join(source_list) if source_list else "No sources found"
        
        context = f"""
User asked: "{user_question}"

Web Search Results:

Tavily AI Answer:
{answer}

Found {len(sources)} source(s):
{sources_text}

IMPORTANT:
1. Answer based on the Tavily AI answer and sources
2. Provide source links for further reading
3. Be informative and helpful
"""
        
        # ISA feedback (Matt Carroll): general-question answers were too verbose.
        # Keep them tight — a direct answer, a couple of points only if useful, and
        # the source links. No mandatory multi-section template.
        system_prompt = """You are an expert agriculture consultant helping farmers.

Answer the question CONCISELY and directly — aim for about 80-100 words, never more than ~130.

- Open with a 1-2 sentence direct answer (use the Tavily AI answer as your primary source).
- Add at most 2-3 short bullet points, and only if they add real value.
- End with the source link(s) for further reading, e.g. "Source: <title> — <URL>".
- Be specific and practical. No long preambles, no filler, no repetition.
"""

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": context}
            ],
            temperature=0.7,
            max_tokens=280
        )
        
        return response.choices[0].message.content.strip()
    
    def _generate_generic_response(self, user_question: str, data: Dict, conversation_context: list = None) -> str:
        """Generate generic response for unknown tool types"""
        
        prompt = f"""User asked: "{user_question}"

Tool returned this data: {data}

Generate a helpful, natural language response based on this information. Keep it concise and conversational (2-3 sentences)."""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=200
        )
        
        return response.choices[0].message.content.strip()


# Test function
if __name__ == "__main__":
    print("Testing LLM Response Generator...")
    print("-" * 70)
    
    try:
        generator = LLMResponseGenerator()
        
        # Test with weather data
        print("\n📝 Test: Weather Response")
        print("-" * 70)
        
        weather_data = {
            "city": "London",
            "country": "GB",
            "temperature": 15.5,
            "feels_like": 14.2,
            "humidity": 72,
            "wind_speed": 5.2,
            "description": "partly cloudy"
        }
        
        response = generator.generate_response(
            user_question="What's the weather in London?",
            tool_name="weather",
            tool_result=weather_data
        )
        
        print(f"User Question: What's the weather in London?")
        print(f"\n🤖 LLM Response:\n{response}")
        
        print("\n" + "=" * 70)
        print("✅ Test complete!")
        
    except Exception as e:
        print(f"❌ Error: {e}")
        print("\n💡 Make sure:")
        print("   1. You have OPENAI_API_KEY in your .env file")
        print("   2. The API key is valid and active")
        print("   3. You have internet connection")