import json
import os
from dataclasses import dataclass
from typing import Dict, List

import gradio as gr
import requests
from bs4 import BeautifulSoup
from openai import OpenAI


@dataclass
class TranscriptSegment:
    speaker_id: str
    start_time: float
    end_time: float
    text: str
    speaker_name: str = ""


class TranscriptProcessor:
    def __init__(self, transcript_file: str = None, transcript_data: dict = None):
        self.transcript_file = transcript_file
        self.transcript_data = transcript_data
        self.formatted_transcript = None
        self.segments = []
        self.text_windows = []
        self.window_size = 2
        self.speaker_mapping = {}
        if self.transcript_file:
            self._load_transcript()
        elif self.transcript_data:
            pass  # transcript_data is already set
        else:
            raise ValueError(
                "Either transcript_file or transcript_data must be provided."
            )

        self._process_transcript()
        self.map_speaker_ids_to_names()

    def _load_transcript(self) -> None:
        """Load the transcript JSON file."""
        with open(self.transcript_file, "r") as f:
            self.transcript_data = json.load(f)

    def _format_time(self, seconds: float) -> str:
        """Convert seconds to formatted time string (MM:SS)."""
        minutes = int(seconds // 60)
        seconds = int(seconds % 60)
        return f"{minutes:02d}:{seconds:02d}"

    def _process_transcript(self) -> None:
        """Process the transcript into segments with speaker information and create a formatted version with timestamps."""
        results = self.transcript_data["results"]

        # Process into segments
        for segment in results["speaker_labels"]["segments"]:
            speaker_id = segment.get("speaker_label", segment.get("speakerlabel", ""))
            speaker_id = (
                speaker_id.replace("spk_", "").replace("spk", "") if speaker_id else ""
            )

            start_time = float(segment.get("start_time", 0))
            end_time = float(segment.get("end_time", 0))

            items = [
                item
                for item in results["items"]
                if "start_time" in item
                and float(item["start_time"]) >= start_time
                and float(item["start_time"]) < end_time
                and item["type"] == "pronunciation"
            ]

            words = [item["alternatives"][0]["content"] for item in items]
            if words:
                self.segments.append(
                    TranscriptSegment(
                        speaker_id=speaker_id,
                        start_time=start_time,
                        end_time=end_time,
                        text=" ".join(words),
                    )
                )

        formatted_segments = []
        for seg in self.segments:
            start_time_str = self._format_time(seg.start_time)
            end_time_str = self._format_time(seg.end_time)
            formatted_segments.append(
                f"time_stamp: {start_time_str}-{end_time_str}\n"
                f"spk {seg.speaker_id}: {seg.text}\n"
            )

        self.formatted_transcript = "\n".join(formatted_segments)

        # Create sliding windows of text for better matching
        for i in range(len(self.segments)):
            # Combine current segment with next segments within window
            window_segments = self.segments[i : i + self.window_size]
            combined_text = " ".join(seg.text for seg in window_segments)
            if window_segments:
                self.text_windows.append(
                    {
                        "text": combined_text,
                        "start_time": window_segments[0].start_time,
                        "end_time": window_segments[-1].end_time,
                    }
                )

    def map_speaker_ids_to_names(self) -> None:
        """Map speaker IDs to names based on introductions in the transcript."""
        try:

            transcript = self.formatted_transcript

            prompt = (
                "Given the following transcript where speakers are identified as spk 0, spk 1, spk 2, etc., please map each spk ID to the speaker's name based on their introduction in the transcript. If no name is introduced for a speaker, keep it as spk_id. Return the mapping as a JSON object in the format {'spk_0': 'Speaker Name', 'spk_1': 'Speaker Name', ...}\n\n"
                f"Transcript:\n{transcript}"
            )

            client = OpenAI()

            completion = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": prompt},
                ],
                temperature=0,
            )

            response_text = completion.choices[0].message.content.strip()
            try:
                self.speaker_mapping = json.loads(response_text)
            except json.JSONDecodeError:
                # extract left most and right most {}
                response_text = response_text[
                    response_text.find("{") : response_text.rfind("}") + 1
                ]
                try:
                    self.speaker_mapping = json.loads(response_text)
                except json.JSONDecodeError:
                    print("Error parsing speaker mapping JSON.")
                    self.speaker_mapping = {}
            for segment in self.segments:
                spk_id = f"spk_{segment.speaker_id}"
                speaker_name = self.speaker_mapping.get(spk_id, spk_id)
                segment.speaker_name = speaker_name

            # Recreate the formatted transcript with speaker names
            formatted_segments = []
            for seg in self.segments:
                start_time_str = self._format_time(seg.start_time)
                end_time_str = self._format_time(seg.end_time)
                formatted_segments.append(
                    f"time_stamp: {start_time_str}-{end_time_str}\n"
                    f"{seg.speaker_name}: {seg.text}\n"
                )
            self.formatted_transcript = "\n".join(formatted_segments)

        except Exception as e:
            print(f"Error mapping speaker IDs to names: {str(e)}")
            self.speaker_mapping = {}

    def correct_speaker_mapping_with_agenda(self, url: str) -> None:
        """Fetch agenda from a URL and correct the speaker mapping using OpenAI."""
        try:

            response = requests.get(url)
            response.raise_for_status()
            html_content = response.text

            # Parse the HTML to find the desired description
            soup = BeautifulSoup(html_content, "html.parser")
            description_tag = soup.find(
                "script", {"type": "application/ld+json"}
            )  # Find the ld+json metadata block
            agenda = ""

            if description_tag:
                # Extract the JSON content
                json_data = json.loads(description_tag.string)
                if "description" in json_data:
                    agenda = json_data["description"]
                else:
                    print("Agenda description not found in the JSON metadata.")
            else:
                print("No structured data (ld+json) found.")

            if not agenda:
                print("No agenda found in the structured metadata. Trying meta tags.")

                # Fallback: Use meta description if ld+json doesn't have it
                meta_description = soup.find("meta", {"name": "description"})
                agenda = meta_description["content"] if meta_description else ""

            if not agenda:
                print("No agenda found in any description tags.")
                return

            print(self.speaker_mapping)

            prompt = (
                f"Given the original speaker mapping {self.speaker_mapping}, agenda:\n{agenda}, and the transcript: {self.formatted_transcript}\n\n"
                "Some speaker names in the mapping might have spelling errors or be incomplete."
                "Remember that the content in agenda is accurate and transcript can have errors so prioritize the spellings and names in the agenda content."
                "If the speaker name and introduction is similar to the agenda, update the speaker name in the mapping."
                "Please correct the names based on the agenda. Return the corrected mapping in JSON format as "
                "{'spk_0': 'Correct Name', 'spk_1': 'Correct Name', ...}."
                "You should only update the name if the name sounds very similar, or there is a good spelling overlap/ The Speaker Introduction matches the description of the Talk from Agends. If the name is totally unrelated, keep the original name."
                "You should always include all the speakers in the mapping from the original mapping, even if you don't update their names. i.e if there are 4 speakers in original mapping, new mapping should have 4 speakers always, ignore all the other spekaers in the agenda. I REPEAT DO NOT ADD OTHER NEW SPEAKERS IN THE MAPPING."
            )

            client = OpenAI()

            completion = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": prompt},
                ],
                temperature=0,
            )

            response_text = completion.choices[0].message.content.strip()
            try:
                corrected_mapping = json.loads(response_text)
            except Exception:
                response_text = response_text[
                    response_text.find("{") : response_text.rfind("}") + 1
                ]
                try:
                    corrected_mapping = json.loads(response_text)
                except json.JSONDecodeError:
                    print(
                        "Error parsing corrected speaker mapping JSON, keeping the original mapping."
                    )
                    corrected_mapping = self.speaker_mapping
            # Update the speaker mapping with corrected names
            self.speaker_mapping = corrected_mapping
            print("Corrected Speaker Mapping:", self.speaker_mapping)

            # Update the transcript segments with corrected names
            for segment in self.segments:
                spk_id = f"spk_{segment.speaker_id}"
                segment.speaker_name = self.speaker_mapping.get(spk_id, spk_id)

            # Recreate the formatted transcript with corrected names
            formatted_segments = []
            for seg in self.segments:
                start_time_str = self._format_time(seg.start_time)
                end_time_str = self._format_time(seg.end_time)
                formatted_segments.append(
                    f"time_stamp: {start_time_str}-{end_time_str}\n"
                    f"{seg.speaker_name}: {seg.text}\n"
                )
            self.formatted_transcript = "\n".join(formatted_segments)

        except requests.exceptions.RequestException as e:
            print(f"  ching agenda from URL: {str(e)}")
        except Exception as e:
            print(f"Error correcting speaker mapping: {str(e)}")

    def get_transcript(self) -> str:
        """Return the formatted transcript with speaker names."""
        return self.formatted_transcript

    def get_transcript_data(self) -> Dict:
        """Return the raw transcript data."""
        return self.transcript_data


def setup_openai_key() -> None:
    """Set up OpenAI API key from file."""
    try:
        with open("api.key", "r") as f:
            os.environ["OPENAI_API_KEY"] = f.read().strip()
    except FileNotFoundError:
        print("Using ENV variable")
        # raise FileNotFoundError(
        #     "api.key file not found. Please create it with your OpenAI API key."
        # )


def get_transcript_for_url(url: str) -> dict:
    """
    This function fetches the transcript data for a signed URL.
    If the URL results in a direct download, it processes the downloaded content.

    :param url: Signed URL for the JSON file
    :return: Parsed JSON data as a dictionary
    """
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
    }

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()

        if "application/json" in response.headers.get("Content-Type", ""):
            return response.json()  # Parse and return JSON directly

        # If not JSON, assume it's a file download (e.g., content-disposition header)
        content_disposition = response.headers.get("Content-Disposition", "")
        if "attachment" in content_disposition:
            # Process the content as JSON
            return json.loads(response.content)

        return json.loads(response.content)

    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err}")
    except requests.exceptions.RequestException as req_err:
        print(f"Request error occurred: {req_err}")
    except json.JSONDecodeError as json_err:
        print(f"JSON decoding error: {json_err}")

    return {}


def get_initial_analysis(
    transcript_processor: TranscriptProcessor, cid, rsid, origin, ct, uid
) -> str:
    """Perform initial analysis of the transcript using OpenAI."""
    try:
        transcript = transcript_processor.get_transcript()
        speaker_mapping = transcript_processor.speaker_mapping
        client = OpenAI()
        if "localhost" in origin:
            link_start = "http"
        else:
            link_start = "https"
        print(uid)
        if ct == "si":  # street interview
            prompt = f"""This is a transcript for a street interview. Call Details are as follows:
User ID UID: {uid}
Speakers: {", ".join(speaker_mapping.values())}
Transcript: {transcript}

Your task is to analyze this street interview transcript and identify the final/best timestamps for each topic or question discussed. Here are the key rules:

1. For any topic/answer that appears multiple times in the transcript (even partially):
   - The LAST occurrence is always considered the best version. If the same thing is said multiple times, the last time is the best, all previous times are considered as additional takes.
   - This includes cases where parts of an answer are scattered throughout the transcript
   - Even slight variations of the same answer should be tracked
   - List timestamps for ALL takes, with the final take highlighted as the best answer

2. Introduction handling:
   - Question 1 is ALWAYS the speaker's introduction/self-introduction
   - If someone introduces themselves multiple times, use the last timestamp as best answer
   - Include all variations of how they state their name/background
   - List ALL introduction timestamps chronologically

3. Question sequence:
   - After the introduction, list questions in the order they were first asked
   - If a question or introduction is revisited later at any point, please use the later timestamp
   - Track partial answers to the same question across the transcript

You need to make sure that any words that are repeated, you need to pick the last of them.

Return format:

[Question Title]
Total takes: [X] (Include ONLY if content appears more than once)
- [Take 1. <div id='topic' style="display: inline"> 15s at 12:30 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{750}}&et={{765}}&uid={{uid}})
- [Take 2. <div id='topic' style="display: inline"> 30s at 14:45 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{885}}&et={{915}}&uid={{uid}})
...
- [Take X (Best). <div id='topic' style="display: inline"> 1m 10s at 16:20 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{980}}&et={{1050}}&uid={{uid}})

URL formatting:
- Convert timestamps to seconds (e.g., 10:13 → 613)
- Format: {link_start}://[origin]/colab/[cid]/[rsid]?st=[start_seconds]&et=[end_seconds]&uid=[unique_id]
- Parameters after RSID must start with ? and subsequent parameters use &

Example:
1. Introduction
Total takes: 2
- [Take 1. <div id='topic' style="display: inline"> 10s at 09:45]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{585}}&et={{595}}&uid={{uid}})
- [Take 1. <div id='topic' style="display: inline"> 20s at 25:45]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{1245}}&et={{1265}}&uid={{uid}}))
- [Take 3 (Best). <div id='topic' style="display: inline"> 5s at 10:13 </div>]({link_start}://roll.ai/colab/1234aq_12314/51234151?st=613&et=618&uid=82314)"""
        else:
            prompt = f"""Call Details:
User ID: {uid}
Call ID: {cid}
Speakers: {", ".join(speaker_mapping.values())}
Transcript: {transcript}
Your task is to analyze speakers' discussions to identify compelling social media clips. For each speaker, identify key topics that mention people, news, events, trends, or sources.

Format requirements:

1. SPEAKER FORMAT:
**Speaker Name**
1. [Topic title <div id='topic' style="display: inline"> 20s at 12:30 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{750}}&et={{770}}&uid={{uid}})
2. [Topic title <div id='topic' style="display: inline"> 45s at 14:45 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{885}}&et={{930}}&uid={{uid}})
3. [Topic title <div id='topic' style="display: inline"> 55s at 16:20 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{980}}&et={{1035}}&uid={{uid}})

2. TIMESTAMP RULES:
- Start time (st): Must begin exactly when speaker starts discussing the specific topic
- End time (et): Must end exactly when either:
  * The speaker completes their point, or
  * Before the next speaker begins
- NO OVERLAP: Selected duration must NEVER include dialogue from other speakers
- Duration limits: Minimum 20 seconds, maximum 60 seconds
- Time format: "Xs at HH:MM" where X = seconds
- URL parameters: Convert display times to seconds
  Example: "25s at 10:13" → st=613&et=638

3. FORMATTING RULES:
- Speaker names: Use markdown bold (**Name**)
- Topic titles: First word capitalized, rest lowercase
- Each topic must be a clickable link with correct timestamp
- URL format: {link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{start_time_in_sec}}&et={{end_time_in_sec}}&uid={{uid}})

4. TOPIC SELECTION:
- Prioritize engaging, viral-worthy content
- Minimum 2 topics per speaker, aim for 3 if available (SKIP THE HOST if no compelling content)
- Topics should be self-contained discussions within the timestamp
- Skip speakers if fewer than 2 compelling topics found
"""
            print(prompt)
        completion = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": f"""You are analyzing a transcript for Call ID: {cid}, Session ID: {rsid}, Origin: {origin}, Call Type: {ct}. 

CORE REQUIREMENTS:
1. TIMESTAMPS: Each clip must contain ONLY the specified speaker's dialogue about a single topic. No overlapping dialogue from other speakers. YOU NEED TO BE VERY CAREFUL ABOUT THIS RULE. YOU HAVE THE TRANSCRIPT AND YOU CAN SEE WHO IS SPEAKING AT WHAT TIME SO BE VERY VERY CARAEFUL AND ONLY INCLUDE THE DIALOGUE OF THE SPEAKER YOU ARE MAKING THE CLIP FOR.
2. DURATION: Clips should be between 20-60 seconds long.
3. CONTENT: Select engaging, viral-worthy topics. Avoid mundane or irrelevant content
4. COVERAGE: Minimum 2 topics per speaker, aim for 3 if good content exists.
5. YOU CAN IGNORE THE HOST IF NO COMPELLING CONTENT IS FOUND.

YOU SHOULD Prioritize accuracy in timestamp at every cost.""",
                },
                {"role": "user", "content": prompt},
            ],
            stream=True,
            temperature=0.5,
        )

        collected_messages = []
        # Iterate through the stream
        for chunk in completion:
            if chunk.choices[0].delta.content is not None:
                chunk_message = chunk.choices[0].delta.content
                collected_messages.append(chunk_message)
                # Yield the accumulated message so far
                yield "".join(collected_messages)

    except Exception as e:
        print(f"Error in initial analysis: {str(e)}")
        yield "An error occurred during initial analysis. Please check your API key and file path."


def chat(
    message: str,
    chat_history: List,
    transcript_processor: TranscriptProcessor,
    cid,
    rsid,
    origin,
    ct,
    uid,
) -> str:
    tools = [
        {
            "type": "function",
            "function": {
                "name": "correct_speaker_name_with_url",
                "description": "If a User provides a link to Agenda file, call the correct_speaker_name_with_url function to correct the speaker names based on the url, i.e if a user says 'Here is the Luma link for the event' and provides a link to the event, the function will correct the speaker names based on the event.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "url": {
                            "type": "string",
                            "description": "The url to the agenda.",
                        },
                    },
                    "required": ["url"],
                    "additionalProperties": False,
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "correct_call_type",
                "description": "If the user tells you the correct call type, you have to apologize and call this function with correct call type.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "call_type": {
                            "type": "string",
                            "description": "The correct call type. If street interview, call type is 'si'.",
                        },
                    },
                    "required": ["call_type"],
                    "additionalProperties": False,
                },
            },
        },
    ]

    try:
        client = OpenAI()

        if "localhost" in origin:
            link_start = "http"
        else:
            link_start = "https"
        prompt = f"""You are a helpful assistant analyzing transcripts and generating timestamps and URL. Call ID is {cid}, Session ID is {rsid}, origin is {origin}, Call Type is {ct}.
Transcript:\n{transcript_processor.get_transcript()}
If a user asks timestamps for a specific topic, find the start time and end time of that specific topic and return answer in the format: 
If the user provides a link to the agenda, use the correct_speaker_name_with_url function to correct the speaker names based on the agenda.
If the user provides the correct call type, use the correct_call_type function to correct the call type. Call Type for street interviews is 'si'.
Answer format:
Topic: Heading [Timestamp: start_time - end_time]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{start_time_in_sec}}&et={{end_time_in_sec}}&uid={{uid}}"').

For Example:
If the start time is 10:13 and end time is 10:18, the url will be:
{link_start}://roll.ai/colab/1234aq_12314/51234151?st=613&et=618&uid=82314
In the URL, make sure that after RSID there is ? and then rest of the fields are added via &.
"""
        messages = [{"role": "system", "content": prompt}]

        for user_msg, assistant_msg in chat_history:
            if user_msg is not None:  # Skip the initial message where user_msg is None
                messages.append({"role": "user", "content": user_msg})
            if assistant_msg is not None:
                messages.append({"role": "assistant", "content": assistant_msg})

        # Add the current message
        messages.append({"role": "user", "content": message})

        completion = client.chat.completions.create(
            model="gpt-4o-mini", messages=messages, tools=tools, stream=True
        )
        collected_messages = []
        tool_calls_detected = False

        for chunk in completion:
            if chunk.choices[0].delta.tool_calls:
                tool_calls_detected = True
                # Handle tool calls without streaming
                response = client.chat.completions.create(
                    model="gpt-4o-mini",
                    messages=messages,
                    tools=tools,
                )

                if response.choices[0].message.tool_calls:
                    tool_call = response.choices[0].message.tool_calls[0]
                    if tool_call.function.name == "correct_speaker_name_with_url":
                        args = eval(tool_call.function.arguments)
                        url = args.get("url", None)
                        if url:
                            transcript_processor.correct_speaker_mapping_with_agenda(
                                url
                            )
                            corrected_speaker_mapping = (
                                transcript_processor.speaker_mapping
                            )
                            function_call_result_message = {
                                "role": "tool",
                                "content": json.dumps(
                                    {"speaker_mapping": f"Corrected Speaker Mapping..."}
                                ),
                                "name": tool_call.function.name,
                                "tool_call_id": tool_call.id,
                            }
                            messages.append(function_call_result_message)

                            # Get final response after tool call
                            final_response = client.chat.completions.create(
                                model="gpt-4o-mini", messages=messages, stream=True
                            )

                            # Stream the final response
                            for final_chunk in final_response:
                                if final_chunk.choices[0].delta.content:
                                    yield final_chunk.choices[0].delta.content
                            return
                        else:
                            function_call_result_message = {
                                "role": "tool",
                                "content": "No URL Provided",
                                "name": tool_call.function.name,
                                "tool_call_id": tool_call.id,
                            }

                    elif tool_call.function.name == "correct_call_type":
                        args = eval(tool_call.function.arguments)
                        call_type = args.get("call_type", None)
                        if call_type:
                            # Stream the analysis for corrected call type
                            for content in get_initial_analysis(
                                transcript_processor,
                                call_type,
                                rsid,
                                origin,
                                call_type,
                                uid,
                            ):
                                yield content
                            return
                break  # Exit streaming loop if tool calls detected

            if not tool_calls_detected and chunk.choices[0].delta.content is not None:
                chunk_message = chunk.choices[0].delta.content
                collected_messages.append(chunk_message)
                yield "".join(collected_messages)

    except Exception as e:
        print(f"Unexpected error in chat: {str(e)}")
        import traceback

        print(f"Traceback: {traceback.format_exc()}")
        yield "Sorry, there was an error processing your request."


def create_chat_interface():
    """Create and configure the chat interface."""
    css = """
    .gradio-container {

        padding-top: 0px !important;
        padding-left: 0px !important;
        padding-right: 0px !important;
        padding: 0px !important;
        margin: 0px !important;
    }
    #component-0 {
        gap: 0px !important;
    }

    .icon-button-wrapper{
        display: none !important;
    }


    footer {
        display: none !important;
    }
    #chatbot_box{
        flex-grow: 1 !important;
        border-width: 0px !important;
    }

    #link-frame {
        position: absolute !important;
        width: 1px !important;
        height: 1px !important;
        right: -100px !important;
        bottom: -100px !important;
        display: none !important;
    }
    .html-container {
        display: none !important;
    }
    a {
        text-decoration: none !important;
    }
    #topic {
        color: #aaa !important;
    }
    .bubble-wrap {
        padding-top: 0px !important;
    }
    .message-content {
        border: 0px !important;
        margin: 5px !important;
    }
    .message-row {
        border-style: none !important;
        margin: 0px !important;
        width: 100% !important;
        max-width: 100% !important;
    }
    .flex-wrap {
        border-style: none !important;
    }

    .panel-full-width {
        border-style: none !important;
        border-width: 0px !important;
    }
    ol {
        list-style-position: outside; 
        margin-left: 20px;
    }

    body.waiting * {
    cursor: progress;
    }
    """
    js = """
    function createIframeHandler() {
        let iframe = document.getElementById('link-frame');
        if (!iframe) {
            iframe = document.createElement('iframe');
            iframe.id = 'link-frame';
            iframe.style.position = 'absolute';
            iframe.style.width = '1px';
            iframe.style.height = '1px';
            iframe.style.right = '-100px';
            iframe.style.bottom = '-100px';
            iframe.style.display = 'none'; // Hidden initially
            document.body.appendChild(iframe);
        }

        document.addEventListener('click', function (event) {
            var link = event.target.closest('a');
            if (link && link.href) {
                document.body.classList.add('waiting');
                setTimeout(function () {
                    document.body.classList.remove('waiting');
                }, 2000); // Reset cursor after 1 seconds
                try {
                    iframe.src = link.href;
                    iframe.style.display = 'block'; // Show iframe on link click
                    event.preventDefault();
                    console.log('Opening link in iframe:', link.href);
                } catch (error) {
                    console.error('Failed to open link in iframe:', error);
                }
            }
        });

        return 'Iframe handler initialized';
    }
    """

    with gr.Blocks(
        fill_height=True,
        fill_width=True,
        css=css,
        js=js,
        theme=gr.themes.Default(
            font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"]
        ),
    ) as demo:
        chatbot = gr.Chatbot(
            elem_id="chatbot_box",
            layout="bubble",
            show_label=False,
            show_share_button=False,
            show_copy_all_button=False,
            show_copy_button=False,
        )
        msg = gr.Textbox(elem_id="chatbot_textbox", show_label=False)
        transcript_processor_state = gr.State()  # maintain state of imp things
        call_id_state = gr.State()
        colab_id_state = gr.State()
        origin_state = gr.State()
        ct_state = gr.State()
        turl_state = gr.State()
        uid_state = gr.State()
        iframe_html = "<iframe id='link-frame'></iframe>"
        gr.HTML(value=iframe_html)  # Add iframe to the UI

        def respond(
            message: str,
            chat_history: List,
            transcript_processor,
            cid,
            rsid,
            origin,
            ct,
            uid,
        ):
            if not transcript_processor:
                bot_message = "Transcript processor not initialized."
                chat_history.append((message, bot_message))
                return "", chat_history

            chat_history.append((message, ""))
            for chunk in chat(
                message,
                chat_history[:-1],  # Exclude the current incomplete message
                transcript_processor,
                cid,
                rsid,
                origin,
                ct,
                uid,
            ):
                chat_history[-1] = (message, chunk)
                yield "", chat_history

        msg.submit(
            respond,
            [
                msg,
                chatbot,
                transcript_processor_state,
                call_id_state,
                colab_id_state,
                origin_state,
                ct_state,
                uid_state,
            ],
            [msg, chatbot],
        )

        # Handle initial loading with streaming
        def on_app_load(request: gr.Request):
            cid = request.query_params.get("cid", None)
            rsid = request.query_params.get("rsid", None)
            origin = request.query_params.get("origin", None)
            ct = request.query_params.get("ct", None)
            turl = request.query_params.get("turl", None)
            uid = request.query_params.get("uid", None)
            print(uid)

            required_params = ["cid", "rsid", "origin", "ct", "turl", "uid"]
            missing_params = [
                param
                for param in required_params
                if request.query_params.get(param) is None
            ]

            if missing_params:
                error_message = (
                    f"Missing required parameters: {', '.join(missing_params)}"
                )
                chatbot_value = [(None, error_message)]
                return [chatbot_value, None, None, None, None, None, None, None]

            try:
                transcript_data = get_transcript_for_url(turl)
                transcript_processor = TranscriptProcessor(
                    transcript_data=transcript_data
                )

                # Initialize with empty message
                chatbot_value = [(None, "")]

                # Return initial values with the transcript processor
                return [
                    chatbot_value,
                    transcript_processor,
                    cid,
                    rsid,
                    origin,
                    ct,
                    turl,
                    uid,
                ]
            except Exception as e:
                error_message = f"Error processing call_id {cid}: {str(e)}"
                chatbot_value = [(None, error_message)]
                return [chatbot_value, None, None, None, None, None, None, None]

        def stream_initial_analysis(
            chatbot_value, transcript_processor, cid, rsid, origin, ct, uid
        ):
            if transcript_processor:
                for chunk in get_initial_analysis(
                    transcript_processor, cid, rsid, origin, ct, uid
                ):
                    chatbot_value[0] = (None, chunk)
                    yield chatbot_value
            else:
                yield chatbot_value

        # Modified load event to handle streaming
        demo.load(
            on_app_load,
            inputs=None,
            outputs=[
                chatbot,
                transcript_processor_state,
                call_id_state,
                colab_id_state,
                origin_state,
                ct_state,
                turl_state,
                uid_state,
            ],
        ).then(
            stream_initial_analysis,
            inputs=[
                chatbot,
                transcript_processor_state,
                call_id_state,
                colab_id_state,
                origin_state,
                ct_state,
                uid_state,
            ],
            outputs=[chatbot],
        )

    return demo


def main():
    """Main function to run the application."""
    try:
        setup_openai_key()
        demo = create_chat_interface()
        demo.launch(share=True)
    except Exception as e:
        print(f"Error starting application: {str(e)}")
        raise


if __name__ == "__main__":
    main()