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| 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 | |
| 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, | |
| max_segment_duration: int = None, | |
| call_type: str = "le", | |
| ): | |
| self.transcript_file = transcript_file | |
| self.transcript_data = transcript_data | |
| self.formatted_transcript = None | |
| self.segments = [] | |
| self.speaker_mapping = {} | |
| self.max_segment_duration = max_segment_duration | |
| 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._create_formatted_transcript() # Create initial formatted transcript | |
| if call_type != "si": | |
| 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: | |
| results = self.transcript_data["results"] | |
| 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)) | |
| if self.max_segment_duration: | |
| current_start = start_time | |
| while current_start < end_time: | |
| current_end = min( | |
| current_start + self.max_segment_duration, end_time | |
| ) | |
| self._create_segment( | |
| speaker_id, current_start, current_end, results["items"] | |
| ) | |
| current_start = current_end | |
| else: | |
| self._create_segment(speaker_id, start_time, end_time, results["items"]) | |
| def _create_segment( | |
| self, speaker_id: str, start: float, end: float, items: list | |
| ) -> None: | |
| matching_items = [ | |
| item | |
| for item in items | |
| if "start_time" in item | |
| and float(item["start_time"]) >= start | |
| and float(item["start_time"]) < end | |
| and item["type"] == "pronunciation" | |
| ] | |
| words = [item["alternatives"][0]["content"] for item in matching_items] | |
| if words: | |
| self.segments.append( | |
| TranscriptSegment( | |
| speaker_id=speaker_id, | |
| start_time=start, | |
| end_time=end, | |
| text=" ".join(words), | |
| ) | |
| ) | |
| def _create_formatted_transcript(self) -> None: | |
| """Create formatted transcript with default speaker labels.""" | |
| 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) | |
| # Use default speaker label (spk_X) if no mapping exists | |
| speaker_label = f"spk_{seg.speaker_id}" | |
| formatted_segments.append( | |
| f"time_stamp: {start_time_str}-{end_time_str}\n" | |
| f"{speaker_label}: {seg.text}\n" | |
| ) | |
| self.formatted_transcript = "\n".join(formatted_segments) | |
| 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", | |
| 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: | |
| 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 = {} | |
| # Update segments with speaker names and recreate formatted transcript | |
| 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 | |
| self._create_formatted_transcript_with_names() | |
| except Exception as e: | |
| print(f"Error mapping speaker IDs to names: {str(e)}") | |
| self.speaker_mapping = {} | |
| def _create_formatted_transcript_with_names(self) -> None: | |
| """Create formatted transcript with mapped 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) | |
| speaker_name = getattr(seg, "speaker_name", f"spk_{seg.speaker_id}") | |
| formatted_segments.append( | |
| f"Start Time: {start_time_str} - End Time: {end_time_str}\n" | |
| f"{speaker_name}: {seg.text}\n" | |
| ) | |
| self.formatted_transcript = "\n".join(formatted_segments) | |
| 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" | |
| if ct == "si": # street interview | |
| prompt = f"""This is a transcript for a street interview. Call Details are as follows: | |
| User ID UID: {uid} | |
| 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: | |
| The user might repeat the answer to the question sometimes, you need to pick the very last answer intelligently | |
| 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 introduction 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)""" | |
| completion = client.chat.completions.create( | |
| model="gpt-4o", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": f"""You are analyzing a transcript for Call ID: {cid}, Session ID: {rsid}, Origin: {origin}, Call Type: {ct}. | |
| CORE REQUIREMENT: | |
| - TIMESTAMPS: A speaker can repeat the answer to a question multiple times. You need to pick the last answer very carefully and choose that as best take. Make sure that that same answer is not repeated again after the best answer. | |
| YOU SHOULD Prioritize accuracy in timestamp at every cost. Read the Transcript carefully and decide where an answer starts and ends. You will have speaker labels so you need to be very sharp.""", | |
| }, | |
| {"role": "user", "content": prompt}, | |
| ], | |
| stream=True, | |
| temperature=0.1, | |
| ) | |
| else: | |
| system_prompt = f"""You are analyzing a transcript for Call ID: {cid}, Session ID: {rsid}, Origin: {origin}, and 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 CAREFUL AND ONLY INCLUDE THE DIALOGUE OF THE SPEAKER YOU ARE MAKING THE CLIP FOR. | |
| 2. DURATION: Clips should be between 20-90 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 timestamps at all costs. | |
| """ | |
| user_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"> 22s at 12:30 </div>]({{link_start}}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{750}}&et={{772}}&uid={{uid}}) | |
| 2. [Topic title <div id='topic' style="display: inline"> 43s at 14:45 </div>]({{link_start}}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{885}}&et={{928}}&uid={{uid}}) | |
| 3. [Topic title <div id='topic' style="display: inline"> 58s at 16:20 </div>]({{link_start}}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{980}}&et={{1038}}&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 1 minute 30 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(user_prompt) | |
| completion = client.chat.completions.create( | |
| model="gpt-4o", | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt}, | |
| ], | |
| stream=True, | |
| temperature=0.1, | |
| ) | |
| 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" | |
| speaker_mapping = transcript_processor.speaker_mapping | |
| prompt = f"""You are a helpful assistant analyzing transcripts and generating timestamps and URL. The user will ask you questions regarding the social media clips from the transcript. | |
| Call ID is {cid}, | |
| Session ID is {rsid}, | |
| origin is {origin}, | |
| Call Type is {ct}. | |
| Speakers: {", ".join(speaker_mapping.values())} | |
| Transcript: {transcript_processor.get_transcript()} | |
| If a user asks timestamps for a specific topic or things, find the start time and end time of that specific topic and return answer in the format: | |
| Answers and URLs should be formated as follows: | |
| [Topic title <div id='topic' style="display: inline"> 22s at 12:30 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{750}}&et={{772}}&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 &. | |
| You can include multiple links here that can related to the user answer. ALWAYS ANSWER FROM THE TRANSCRIPT. | |
| RULE: When selecting timestamps for the answer, always use the **starting time (XX:YY)** as the reference point for your response, with the duration (Z seconds) calculated from this starting time, not the ending time of the segment. | |
| Example 1: | |
| User: Suggest me some clips that can go viral on Instagram. | |
| Response: | |
| 1. [Clip 1 <div id='topic' style="display: inline"> 22s at 12:30 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{750}}&et={{772}}&uid={{uid}}) | |
| User: Give me the URL where each person has introduced themselves. | |
| 2. [Clip 2 <div id='topic' style="display: inline"> 10s at 10:00 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{600}}&et={{610}}&uid={{uid}}) | |
| Example 2: | |
| Provide the exact timestamp where the person begins their introduction, typically starting with phrases like "Hi," "Hello," "I am," or "My name is," and include the full introduction, covering everything they say about themselves, including their name, role, background, current responsibilities, organization, and any additional details they provide about their work or personal interests. | |
| 1. [Person Name1 <div id='topic' style="display: inline"> 43s at 14:45 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{885}}&et={{928}}&uid={{uid}}) | |
| 2. [Person Name2 <div id='topic' style="display: inline"> 58s at 16:20 </div>]({link_start}://{{origin}}/collab/{{cid}}/{{rsid}}?st={{980}}&et={{1038}}&uid={{uid}}) | |
| .... | |
| 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'. | |
| """ | |
| messages = [{"role": "system", "content": prompt}] | |
| print(messages[0]["content"]) | |
| 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", | |
| messages=messages, | |
| tools=tools, | |
| stream=True, | |
| temperature=0.3, | |
| ) | |
| 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", | |
| 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", 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) | |
| 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, | |
| max_segment_duration=5 if ct != "si" else 10, | |
| call_type=ct, | |
| ) | |
| # 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() | |