ai: Refactor the code for 2.1.1-ft-QwQ-32B.
Browse files- jarvis.py +41 -90
- requirements.txt +1 -5
jarvis.py
CHANGED
@@ -3,38 +3,22 @@
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# SPDX-License-Identifier: Apache-2.0
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#
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import gradio as gr
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import
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import json
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import os
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import random
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import time
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import pytesseract
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import pdfplumber
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import docx
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import pandas as pd
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import
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import
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import
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import
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import concurrent.futures
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import itertools
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import threading
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import
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import asyncio
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from openai import OpenAI
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from optillm.cot_reflection import cot_reflection
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from optillm.leap import leap
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from optillm.plansearch import plansearch
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from optillm.reread import re2_approach
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from optillm.rto import round_trip_optimization
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from optillm.self_consistency import advanced_self_consistency_approach
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from optillm.z3_solver import Z3SymPySolverSystem
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from pathlib import Path
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from PIL import Image
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from pptx import Presentation
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os.system("apt-get update -q -y && apt-get install -q -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-ind libleptonica-dev libtesseract-dev")
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@@ -60,7 +44,7 @@ DEFAULT_CONFIG = json.loads(os.getenv("DEFAULT_CONFIG", "{}"))
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META_TAGS = os.getenv("META_TAGS")
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ALLOWED_EXTENSIONS = json.loads(os.getenv("ALLOWED_EXTENSIONS"))
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ACTIVE_CANDIDATE = None
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@@ -100,11 +84,9 @@ def extract_file_content(file_path):
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text = page.extract_text()
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if text:
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content += text + "\n"
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table_str = "\n".join([", ".join(row) for row in table if row])
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content += "\n" + table_str + "\n"
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elif ext in [".doc", ".docx"]:
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doc = docx.Document(file_path)
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for para in doc.paragraphs:
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@@ -119,51 +101,28 @@ def extract_file_content(file_path):
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if hasattr(shape, "text") and shape.text:
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content += shape.text + "\n"
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elif ext in [".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif", ".webp"]:
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text = pytesseract.image_to_string(image)
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content += text + "\n"
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except Exception as e:
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content += f"{e}\n"
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else:
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content = Path(file_path).read_text(encoding="utf-8")
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except Exception as e:
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content = f"{file_path}: {e}"
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return content.strip()
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def process_ai_response(ai_text):
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try:
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result = round_trip_optimization(ai_text)
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result = re2_approach(result)
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result = cot_reflection(result)
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result = advanced_self_consistency_approach(result)
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result = plansearch(result)
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result = leap(result)
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solver = Z3SymPySolverSystem()
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result = solver.solve(result)
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return result
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except Exception:
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return ai_text
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async def fetch_response_async(host, provider_key, selected_model, messages, model_config, session_id):
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timeouts = [60, 80, 120, 240]
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for timeout in timeouts:
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try:
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async with httpx.AsyncClient(timeout=timeout) as client:
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data = {"model": selected_model, "messages": messages, **model_config}
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resp = await client.post(host, json={**data, "extra_body": extra, "session_id": session_id}, headers={"Authorization": f"Bearer {provider_key}"})
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resp.raise_for_status()
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except json.JSONDecodeError:
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return RESPONSES["RESPONSE_2"]
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if isinstance(resp_json, dict) and "choices" in resp_json and isinstance(resp_json["choices"], list) and len(resp_json["choices"]) > 0 and isinstance(resp_json["choices"][0], dict):
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choice = resp_json["choices"][0]
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if
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return process_ai_response(ai_text)
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return RESPONSES["RESPONSE_2"]
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except Exception:
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continue
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@@ -177,29 +136,25 @@ async def chat_with_model_async(history, user_input, selected_model_display, ses
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sess.session_id = str(uuid.uuid4())
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selected_model = get_model_key(selected_model_display)
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model_config = MODEL_CONFIG.get(selected_model, DEFAULT_CONFIG)
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messages = [{"role": "user", "content": user} for user, _ in history]
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messages += [{"role": "assistant", "content": assistant} for _, assistant in history if assistant]
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if INTERNAL_TRAINING_DATA:
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messages.insert(0, {"role": "system", "content": INTERNAL_TRAINING_DATA})
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messages.append({"role": "user", "content": user_input})
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global ACTIVE_CANDIDATE
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if ACTIVE_CANDIDATE
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candidates = [(host, key) for host in
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random.shuffle(candidates)
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ACTIVE_CANDIDATE = next(((host, key) for host, key in candidates if host and key), None)
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return result
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except Exception:
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continue
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return RESPONSES["RESPONSE_2"]
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async def respond_async(multi_input, history, selected_model_display, sess):
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@@ -210,8 +165,7 @@ async def respond_async(multi_input, history, selected_model_display, sess):
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combined_input = ""
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for file_item in message["files"]:
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file_path = file_item["name"] if isinstance(file_item, dict) and "name" in file_item else file_item
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combined_input += f"{Path(file_path).name}\n\n{file_content}\n\n"
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if message["text"]:
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combined_input += message["text"]
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history.append([combined_input, ""])
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@@ -220,14 +174,13 @@ async def respond_async(multi_input, history, selected_model_display, sess):
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def convert_to_string(data):
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if isinstance(data, (str, int, float)):
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return str(data)
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return data.decode("utf-8", errors="ignore")
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return "".join(map(convert_to_string, data))
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return json.dumps(data, ensure_ascii=False)
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return repr(data)
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for character in ai_response:
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history[-1][1] += convert_to_string(character)
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await asyncio.sleep(0.0001)
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@@ -239,11 +192,9 @@ def change_model(new_model_display):
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with gr.Blocks(fill_height=True, fill_width=True, title=AI_TYPES["AI_TYPE_4"], head=META_TAGS) as jarvis:
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user_history = gr.State([])
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user_session = gr.State(create_session())
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selected_model = gr.State(MODEL_CHOICES[0])
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chatbot = gr.Chatbot(label=AI_TYPES["AI_TYPE_1"], show_copy_button=True, scale=1, elem_id=AI_TYPES["AI_TYPE_2"])
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#model_dropdown = gr.Dropdown(show_label=False, choices=MODEL_CHOICES, value=MODEL_CHOICES[0])
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with gr.Row():
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msg = gr.MultimodalTextbox(show_label=False, placeholder=RESPONSES["RESPONSE_5"], interactive=True, file_count="single", file_types=ALLOWED_EXTENSIONS)
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msg.submit(fn=respond_async, inputs=[msg, user_history, selected_model, user_session], outputs=[chatbot, msg, user_session], concurrency_limit=None, api_name=INTERNAL_AI_GET_SERVER)
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jarvis.launch(max_file_size="1mb")
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# SPDX-License-Identifier: Apache-2.0
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#
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import asyncio
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import docx
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import gradio as gr
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import httpx
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import json
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import os
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import pandas as pd
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import pdfplumber
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import pytesseract
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import random
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import requests
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import threading
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import uuid
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from PIL import Image
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from pathlib import Path
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from pptx import Presentation
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os.system("apt-get update -q -y && apt-get install -q -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-ind libleptonica-dev libtesseract-dev")
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META_TAGS = os.getenv("META_TAGS")
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ALLOWED_EXTENSIONS = json.loads(os.getenv("ALLOWED_EXTENSIONS", "[]"))
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ACTIVE_CANDIDATE = None
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text = page.extract_text()
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if text:
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content += text + "\n"
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for table in page.extract_tables():
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table_str = "\n".join([", ".join(row) for row in table if row])
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content += "\n" + table_str + "\n"
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elif ext in [".doc", ".docx"]:
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doc = docx.Document(file_path)
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for para in doc.paragraphs:
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if hasattr(shape, "text") and shape.text:
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content += shape.text + "\n"
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elif ext in [".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif", ".webp"]:
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pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
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image = Image.open(file_path)
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content += pytesseract.image_to_string(image) + "\n"
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else:
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content = Path(file_path).read_text(encoding="utf-8")
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except Exception as e:
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content = f"{file_path}: {e}"
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return content.strip()
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async def fetch_response_async(host, provider_key, selected_model, messages, model_config, session_id):
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timeouts = [60, 80, 120, 240]
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for timeout in timeouts:
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try:
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async with httpx.AsyncClient(timeout=timeout) as client:
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data = {"model": selected_model, "messages": messages, **model_config}
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resp = await client.post(host, json={**data, "session_id": session_id}, headers={"Authorization": f"Bearer {provider_key}"})
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resp.raise_for_status()
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resp_json = resp.json()
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if isinstance(resp_json, dict) and resp_json.get("choices"):
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choice = resp_json["choices"][0]
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if choice.get("message") and isinstance(choice["message"].get("content"), str):
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return choice["message"]["content"]
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return RESPONSES["RESPONSE_2"]
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except Exception:
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continue
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sess.session_id = str(uuid.uuid4())
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selected_model = get_model_key(selected_model_display)
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model_config = MODEL_CONFIG.get(selected_model, DEFAULT_CONFIG)
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messages = [{"role": "user", "content": user} for user, _ in history] + [{"role": "assistant", "content": assistant} for _, assistant in history if assistant]
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if INTERNAL_TRAINING_DATA:
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messages.insert(0, {"role": "system", "content": INTERNAL_TRAINING_DATA})
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messages.append({"role": "user", "content": user_input})
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global ACTIVE_CANDIDATE
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if ACTIVE_CANDIDATE:
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result = await fetch_response_async(ACTIVE_CANDIDATE[0], ACTIVE_CANDIDATE[1], selected_model, messages, model_config, sess.session_id)
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if result != RESPONSES["RESPONSE_2"]:
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return result
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ACTIVE_CANDIDATE = None
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keys = get_available_items(LINUX_SERVER_PROVIDER_KEYS, LINUX_SERVER_PROVIDER_KEYS_MARKED)
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hosts = get_available_items(LINUX_SERVER_HOSTS, LINUX_SERVER_HOSTS_MARKED)
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candidates = [(host, key) for host in hosts for key in keys]
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random.shuffle(candidates)
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for host, key in candidates:
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result = await fetch_response_async(host, key, selected_model, messages, model_config, sess.session_id)
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if result != RESPONSES["RESPONSE_2"]:
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ACTIVE_CANDIDATE = (host, key)
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return result
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return RESPONSES["RESPONSE_2"]
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async def respond_async(multi_input, history, selected_model_display, sess):
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combined_input = ""
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for file_item in message["files"]:
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file_path = file_item["name"] if isinstance(file_item, dict) and "name" in file_item else file_item
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combined_input += f"{Path(file_path).name}\n\n{extract_file_content(file_path)}\n\n"
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if message["text"]:
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combined_input += message["text"]
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history.append([combined_input, ""])
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def convert_to_string(data):
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if isinstance(data, (str, int, float)):
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return str(data)
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if isinstance(data, bytes):
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return data.decode("utf-8", errors="ignore")
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if isinstance(data, (list, tuple)):
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return "".join(map(convert_to_string, data))
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if isinstance(data, dict):
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return json.dumps(data, ensure_ascii=False)
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return repr(data)
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for character in ai_response:
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history[-1][1] += convert_to_string(character)
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await asyncio.sleep(0.0001)
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with gr.Blocks(fill_height=True, fill_width=True, title=AI_TYPES["AI_TYPE_4"], head=META_TAGS) as jarvis:
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user_history = gr.State([])
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user_session = gr.State(create_session())
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selected_model = gr.State(MODEL_CHOICES[0] if MODEL_CHOICES else "")
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chatbot = gr.Chatbot(label=AI_TYPES["AI_TYPE_1"], show_copy_button=True, scale=1, elem_id=AI_TYPES["AI_TYPE_2"])
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with gr.Row():
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msg = gr.MultimodalTextbox(show_label=False, placeholder=RESPONSES["RESPONSE_5"], interactive=True, file_count="single", file_types=ALLOWED_EXTENSIONS)
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msg.submit(fn=respond_async, inputs=[msg, user_history, selected_model, user_session], outputs=[chatbot, msg, user_session], api_name=INTERNAL_AI_GET_SERVER)
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jarvis.launch(max_file_size="1mb")
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requirements.txt
CHANGED
@@ -1,11 +1,7 @@
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huggingface_hub
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httpx
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-
openai
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4 |
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optillm
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pandas
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pdfplumber
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pymupdf
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python-docx
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python-pptx
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pytesseract
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httpx
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pandas
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pdfplumber
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Pillow
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python-docx
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python-pptx
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pytesseract
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