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https://github.com/oobabooga/text-generation-webui.git
synced 2025-06-07 14:17:09 -04:00
Simplify things
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oobabooga
parent
d702a2a962
commit
1f3b1a1b94
1 changed files with 24 additions and 67 deletions
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@ -3,7 +3,6 @@ import copy
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import json
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import time
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from collections import deque
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from datetime import datetime
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import requests
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import tiktoken
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@ -86,49 +85,34 @@ def process_parameters(body, is_legacy=False):
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return generate_params
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def get_current_timestamp():
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"""Returns the current time in 24-hour format"""
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return datetime.now().strftime('%b %d, %Y %H:%M')
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def process_image_url(url, image_id):
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"""Process an image URL and return attachment data"""
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"""Process an image URL and return attachment data for llama.cpp"""
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try:
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if url.startswith("data:"):
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# Handle data URL (data:image/jpeg;base64,...)
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if "base64," in url:
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image_data = url.split("base64,", 1)[1]
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else:
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raise ValueError("Unsupported data URL format")
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else:
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# Handle regular URL - download image
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headers = {
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
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}
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headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'}
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response = requests.get(url, timeout=10, headers=headers)
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response.raise_for_status()
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image_data = base64.b64encode(response.content).decode('utf-8')
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return {
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"name": f"image_{image_id}",
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"type": "image",
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"image_data": image_data,
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"image_id": image_id,
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"file_path": f"api_image_{image_id}",
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}
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return {"image_data": image_data, "image_id": image_id}
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except Exception as e:
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logger.error(f"Error processing image URL {url}: {e}")
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return None
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def process_multimodal_content(content):
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"""Process multimodal content and return text content and attachments"""
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"""Extract text and images from OpenAI multimodal format"""
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if isinstance(content, str):
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return content, []
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if isinstance(content, list):
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text_content = ""
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attachments = []
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images = []
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for item in content:
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if item.get("type") == "text":
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@ -136,14 +120,11 @@ def process_multimodal_content(content):
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elif item.get("type") == "image_url":
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image_url = item.get("image_url", {}).get("url", "")
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if image_url:
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attachment = process_image_url(image_url, len(attachments) + 1)
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if attachment:
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attachments.append(attachment)
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else:
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# Log warning but continue processing
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logger.warning(f"Failed to process image URL: {image_url}")
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image = process_image_url(image_url, len(images) + 1)
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if image:
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images.append(image)
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return text_content, attachments
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return text_content, images
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return str(content), []
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@ -159,10 +140,7 @@ def convert_history(history):
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user_input = ""
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user_input_last = True
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system_message = ""
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metadata = {}
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# Keep track of attachments for the current message being built
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pending_attachments = []
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all_images = [] # Simple list to collect all images
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for entry in history:
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content = entry["content"]
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@ -170,16 +148,20 @@ def convert_history(history):
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if role == "user":
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# Process multimodal content
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processed_content, attachments = process_multimodal_content(content)
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processed_content, images = process_multimodal_content(content)
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if images:
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image_refs = "".join(f"[img-{img['image_id']}]" for img in images)
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processed_content = f"{processed_content} {image_refs}"
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user_input = processed_content
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user_input_last = True
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all_images.extend(images) # Add any images to our collection
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if current_message:
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chat_dialogue.append([current_message, '', ''])
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current_message = ""
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current_message = processed_content
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pending_attachments = attachments # Store attachments for when message is added
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elif role == "assistant":
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if "tool_calls" in entry and isinstance(entry["tool_calls"], list) and len(entry["tool_calls"]) > 0 and content.strip() == "":
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@ -187,18 +169,7 @@ def convert_history(history):
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current_reply = content
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user_input_last = False
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if current_message:
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row_idx = len(chat_dialogue) # Calculate index here, right before adding
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chat_dialogue.append([current_message, current_reply, ''])
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# Add attachments to metadata if any
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if pending_attachments:
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user_key = f"user_{row_idx}"
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metadata[user_key] = {
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"timestamp": get_current_timestamp(),
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"attachments": pending_attachments
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}
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pending_attachments = [] # Clear pending attachments
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current_message = ""
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current_reply = ""
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else:
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@ -209,19 +180,14 @@ def convert_history(history):
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elif role == "system":
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system_message += f"\n{content}" if system_message else content
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# Handle case where there's a pending user message at the end
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if current_message and pending_attachments:
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row_idx = len(chat_dialogue) # This will be the index when the message is processed
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user_key = f"user_{row_idx}"
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metadata[user_key] = {
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"timestamp": get_current_timestamp(),
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"attachments": pending_attachments
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}
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if not user_input_last:
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user_input = ""
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return user_input, system_message, {'internal': chat_dialogue, 'visible': copy.deepcopy(chat_dialogue), 'metadata': metadata}
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return user_input, system_message, {
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'internal': chat_dialogue,
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'visible': copy.deepcopy(chat_dialogue),
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'images': all_images # Simple list of all images from the conversation
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}
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def chat_completions_common(body: dict, is_legacy: bool = False, stream=False, prompt_only=False) -> dict:
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@ -298,15 +264,6 @@ def chat_completions_common(body: dict, is_legacy: bool = False, stream=False, p
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# History
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user_input, custom_system_message, history = convert_history(messages)
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# Collect image attachments for multimodal support
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image_attachments = []
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if 'metadata' in history:
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for key, value in history['metadata'].items():
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if 'attachments' in value:
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for attachment in value['attachments']:
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if attachment.get('type') == 'image':
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image_attachments.append(attachment)
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generate_params.update({
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'mode': body['mode'],
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'name1': name1,
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@ -323,9 +280,9 @@ def chat_completions_common(body: dict, is_legacy: bool = False, stream=False, p
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'stream': stream
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})
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# Add image attachments to state for llama.cpp multimodal support
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if image_attachments:
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generate_params['image_attachments'] = image_attachments
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# Add images to state for llama.cpp multimodal support
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if history.get('images'):
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generate_params['image_attachments'] = history['images']
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max_tokens = generate_params['max_new_tokens']
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if max_tokens in [None, 0]:
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