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import gradio as gr
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import os
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import json
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import sqlite3
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import hashlib
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import datetime
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from pathlib import Path
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CLOUDFLARE_CONFIG = {
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"api_token": os.getenv("CLOUDFLARE_API_TOKEN", ""),
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"account_id": os.getenv("CLOUDFLARE_ACCOUNT_ID", ""),
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"d1_database_id": os.getenv("CLOUDFLARE_D1_DATABASE_ID", ""),
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"r2_bucket_name": os.getenv("CLOUDFLARE_R2_BUCKET_NAME", ""),
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"kv_namespace_id": os.getenv("CLOUDFLARE_KV_NAMESPACE_ID", ""),
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"durable_objects_id": os.getenv("CLOUDFLARE_DURABLE_OBJECTS_ID", ""),
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}
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AI_MODELS = {
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"Text Generation": {
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"Qwen Models": [
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"Qwen/Qwen2.5-72B-Instruct",
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"Qwen/Qwen2.5-32B-Instruct",
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"Qwen/Qwen2.5-14B-Instruct",
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"Qwen/Qwen2.5-7B-Instruct",
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"Qwen/Qwen2.5-3B-Instruct",
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"Qwen/Qwen2.5-1.5B-Instruct",
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"Qwen/Qwen2.5-0.5B-Instruct",
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"Qwen/Qwen2-72B-Instruct",
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"Qwen/Qwen2-57B-A14B-Instruct",
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"Qwen/Qwen2-7B-Instruct",
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"Qwen/Qwen2-1.5B-Instruct",
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"Qwen/Qwen2-0.5B-Instruct",
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"Qwen/Qwen1.5-110B-Chat",
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"Qwen/Qwen1.5-72B-Chat",
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"Qwen/Qwen1.5-32B-Chat",
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"Qwen/Qwen1.5-14B-Chat",
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"Qwen/Qwen1.5-7B-Chat",
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"Qwen/Qwen1.5-4B-Chat",
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"Qwen/Qwen1.5-1.8B-Chat",
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"Qwen/Qwen1.5-0.5B-Chat",
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"Qwen/CodeQwen1.5-7B-Chat",
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"Qwen/Qwen2.5-Math-72B-Instruct",
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"Qwen/Qwen2.5-Math-7B-Instruct",
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"Qwen/Qwen2.5-Coder-32B-Instruct",
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"Qwen/Qwen2.5-Coder-14B-Instruct",
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"Qwen/Qwen2.5-Coder-7B-Instruct",
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"Qwen/Qwen2.5-Coder-3B-Instruct",
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"Qwen/Qwen2.5-Coder-1.5B-Instruct",
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"Qwen/Qwen2.5-Coder-0.5B-Instruct",
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"Qwen/QwQ-32B-Preview",
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"Qwen/Qwen2-VL-72B-Instruct",
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"Qwen/Qwen2-VL-7B-Instruct",
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"Qwen/Qwen2-VL-2B-Instruct",
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"Qwen/Qwen2-Audio-7B-Instruct",
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"Qwen/Qwen-Agent-Chat",
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"Qwen/Qwen-VL-Chat",
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],
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"DeepSeek Models": [
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"deepseek-ai/deepseek-llm-67b-chat",
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"deepseek-ai/deepseek-llm-7b-chat",
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"deepseek-ai/deepseek-coder-33b-instruct",
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"deepseek-ai/deepseek-coder-7b-instruct",
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"deepseek-ai/deepseek-coder-6.7b-instruct",
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"deepseek-ai/deepseek-coder-1.3b-instruct",
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"deepseek-ai/DeepSeek-V2-Chat",
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"deepseek-ai/DeepSeek-V2-Lite-Chat",
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"deepseek-ai/deepseek-math-7b-instruct",
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"deepseek-ai/deepseek-moe-16b-chat",
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"deepseek-ai/deepseek-vl-7b-chat",
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"deepseek-ai/deepseek-vl-1.3b-chat",
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"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
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"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
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"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
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"deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
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"deepseek-ai/DeepSeek-Reasoner-R1",
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],
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},
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"Image Processing": {
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"Image Generation": [
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"black-forest-labs/FLUX.1-dev",
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"black-forest-labs/FLUX.1-schnell",
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"black-forest-labs/FLUX.1-pro",
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"runwayml/stable-diffusion-v1-5",
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"stabilityai/stable-diffusion-xl-base-1.0",
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"stabilityai/stable-diffusion-3-medium-diffusers",
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"stabilityai/sd-turbo",
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"kandinsky-community/kandinsky-2-2-decoder",
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"playgroundai/playground-v2.5-1024px-aesthetic",
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"midjourney/midjourney-v6",
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],
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"Image Editing": [
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"timbrooks/instruct-pix2pix",
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"runwayml/stable-diffusion-inpainting",
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"stabilityai/stable-diffusion-xl-refiner-1.0",
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"lllyasviel/control_v11p_sd15_inpaint",
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"SG161222/RealVisXL_V4.0",
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"ByteDance/SDXL-Lightning",
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"segmind/SSD-1B",
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"segmind/Segmind-Vega",
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"playgroundai/playground-v2-1024px-aesthetic",
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"stabilityai/stable-cascade",
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"lllyasviel/ControlNet-v1-1",
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"lllyasviel/sd-controlnet-canny",
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"Monster-Labs/control_v1p_sd15_qrcode_monster",
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"TencentARC/PhotoMaker",
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"instantX/InstantID",
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],
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"Face Processing": [
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"InsightFace/inswapper_128.onnx",
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"deepinsight/insightface",
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"TencentARC/GFPGAN",
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"sczhou/CodeFormer",
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"xinntao/Real-ESRGAN",
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"ESRGAN/ESRGAN",
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],
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},
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"Video Generation": {
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"Text-to-Video": [
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"ali-vilab/text-to-video-ms-1.7b",
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"damo-vilab/text-to-video-ms-1.7b",
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"modelscope/text-to-video-synthesis",
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"camenduru/potat1",
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"stabilityai/stable-video-diffusion-img2vid",
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"stabilityai/stable-video-diffusion-img2vid-xt",
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"ByteDance/AnimateDiff",
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"guoyww/animatediff",
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],
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"Image-to-Video": [
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"stabilityai/stable-video-diffusion-img2vid",
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"stabilityai/stable-video-diffusion-img2vid-xt-1-1",
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"TencentARC/MotionCtrl",
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"ali-vilab/i2vgen-xl",
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"Doubiiu/ToonCrafter",
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],
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"Video Editing": [
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"MCG-NJU/VideoMAE",
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"showlab/Tune-A-Video",
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"Picsart-AI-Research/Text2Video-Zero",
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"damo-vilab/MS-Vid2Vid-XL",
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"kabachuha/sd-webui-deforum",
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],
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},
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"AI Teacher & Education": {
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"Math & Science": [
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"Qwen/Qwen2.5-Math-72B-Instruct",
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"Qwen/Qwen2.5-Math-7B-Instruct",
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"deepseek-ai/deepseek-math-7b-instruct",
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"mistralai/Mistral-Math-7B-v0.1",
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"WizardLM/WizardMath-70B-V1.0",
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"MathGPT/MathGPT-32B",
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],
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"Coding Tutor": [
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"Qwen/Qwen2.5-Coder-32B-Instruct",
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"deepseek-ai/deepseek-coder-33b-instruct",
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"WizardLM/WizardCoder-Python-34B-V1.0",
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"bigcode/starcoder2-15b-instruct-v0.1",
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"meta-llama/CodeLlama-34b-Instruct-hf",
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],
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"Language Learning": [
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"facebook/nllb-200-3.3B",
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"facebook/seamless-m4t-v2-large",
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"Helsinki-NLP/opus-mt-multilingual",
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"google/madlad400-10b-mt",
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"Unbabel/TowerInstruct-7B-v0.1",
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],
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"General Education": [
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"Qwen/Qwen2.5-72B-Instruct",
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"microsoft/Phi-3-medium-128k-instruct",
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"mistralai/Mistral-7B-Instruct-v0.3",
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"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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"openchat/openchat-3.5-1210",
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],
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},
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"Software Engineer Agent": {
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"Code Generation": [
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"Qwen/Qwen2.5-Coder-32B-Instruct",
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"Qwen/Qwen2.5-Coder-14B-Instruct",
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"Qwen/Qwen2.5-Coder-7B-Instruct",
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"deepseek-ai/deepseek-coder-33b-instruct",
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"deepseek-ai/deepseek-coder-7b-instruct",
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"deepseek-ai/deepseek-coder-6.7b-instruct",
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"meta-llama/CodeLlama-70b-Instruct-hf",
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"meta-llama/CodeLlama-34b-Instruct-hf",
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"meta-llama/CodeLlama-13b-Instruct-hf",
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"meta-llama/CodeLlama-7b-Instruct-hf",
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],
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"Code Analysis & Review": [
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"bigcode/starcoder2-15b-instruct-v0.1",
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"bigcode/starcoder2-7b",
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"bigcode/starcoderbase-7b",
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"WizardLM/WizardCoder-Python-34B-V1.0",
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"WizardLM/WizardCoder-15B-V1.0",
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"Phind/Phind-CodeLlama-34B-v2",
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"codellama/CodeLlama-70b-Python-hf",
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],
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"Specialized Coding": [
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"Salesforce/codegen25-7b-multi",
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"Salesforce/codegen-16B-multi",
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"replit/replit-code-v1-3b",
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"NinedayWang/PolyCoder-2.7B",
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"stabilityai/stablelm-base-alpha-7b-v2",
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"teknium/OpenHermes-2.5-Mistral-7B",
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],
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"DevOps & Infrastructure": [
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"deepseek-ai/deepseek-coder-33b-instruct",
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"Qwen/Qwen2.5-Coder-32B-Instruct",
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"mistralai/Mistral-7B-Instruct-v0.3",
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"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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],
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},
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"Audio Processing": {
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"Text-to-Speech": [
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"microsoft/speecht5_tts",
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"facebook/mms-tts-eng",
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"facebook/mms-tts-ara",
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"coqui/XTTS-v2",
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"suno/bark",
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"parler-tts/parler-tts-large-v1",
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"microsoft/DisTTS",
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"facebook/fastspeech2-en-ljspeech",
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"espnet/kan-bayashi_ljspeech_vits",
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"facebook/tts_transformer-en-ljspeech",
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"microsoft/SpeechT5",
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"Voicemod/fastspeech2-en-male1",
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"facebook/mms-tts-spa",
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"facebook/mms-tts-fra",
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"facebook/mms-tts-deu",
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],
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"Speech-to-Text": [
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"openai/whisper-large-v3",
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"openai/whisper-large-v2",
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"openai/whisper-medium",
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"openai/whisper-small",
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"openai/whisper-base",
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"openai/whisper-tiny",
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"facebook/wav2vec2-large-960h",
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"facebook/wav2vec2-base-960h",
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"microsoft/unispeech-sat-large",
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"nvidia/stt_en_conformer_ctc_large",
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"speechbrain/asr-wav2vec2-commonvoice-en",
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"facebook/mms-1b-all",
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"facebook/seamless-m4t-v2-large",
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"distil-whisper/distil-large-v3",
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"distil-whisper/distil-medium.en",
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],
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},
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"Multimodal AI": {
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"Vision-Language": [
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"microsoft/DialoGPT-large",
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"microsoft/blip-image-captioning-large",
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"microsoft/blip2-opt-6.7b",
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"microsoft/blip2-flan-t5-xl",
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"salesforce/blip-vqa-capfilt-large",
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"dandelin/vilt-b32-finetuned-vqa",
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"google/pix2struct-ai2d-base",
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"microsoft/git-large-coco",
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"microsoft/git-base-vqa",
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"liuhaotian/llava-v1.6-34b",
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"liuhaotian/llava-v1.6-vicuna-7b",
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],
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"Talking Avatars": [
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"microsoft/SpeechT5-TTS-Avatar",
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"Wav2Lip-HD",
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"First-Order-Model",
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"LipSync-Expert",
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"DeepFaceLive",
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"FaceSwapper-Live",
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"RealTime-FaceRig",
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"AI-Avatar-Generator",
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"TalkingHead-3D",
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],
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},
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"Arabic-English Models": [
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"aubmindlab/bert-base-arabertv2",
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"aubmindlab/aragpt2-base",
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"aubmindlab/aragpt2-medium",
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"CAMeL-Lab/bert-base-arabic-camelbert-mix",
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"asafaya/bert-base-arabic",
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"UBC-NLP/MARBERT",
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"UBC-NLP/ARBERTv2",
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"facebook/nllb-200-3.3B",
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"facebook/m2m100_1.2B",
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"Helsinki-NLP/opus-mt-ar-en",
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"Helsinki-NLP/opus-mt-en-ar",
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"microsoft/DialoGPT-medium-arabic",
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],
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}
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def init_database():
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"""Initialize SQLite database for authentication"""
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db_path = Path("openmanus.db")
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS users (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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mobile_number TEXT UNIQUE NOT NULL,
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full_name TEXT NOT NULL,
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password_hash TEXT NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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last_login TIMESTAMP,
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is_active BOOLEAN DEFAULT 1
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)
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"""
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)
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cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS sessions (
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id TEXT PRIMARY KEY,
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user_id INTEGER NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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|
expires_at TIMESTAMP NOT NULL,
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ip_address TEXT,
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|
user_agent TEXT,
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FOREIGN KEY (user_id) REFERENCES users (id)
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)
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|
"""
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|
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)
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|
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cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS model_usage (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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|
|
user_id INTEGER,
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|
|
model_name TEXT NOT NULL,
|
|
|
category TEXT NOT NULL,
|
|
|
input_text TEXT,
|
|
|
output_text TEXT,
|
|
|
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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processing_time REAL,
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FOREIGN KEY (user_id) REFERENCES users (id)
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)
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|
"""
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|
)
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conn.commit()
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conn.close()
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return True
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def hash_password(password):
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|
|
"""Hash password using SHA-256"""
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|
|
return hashlib.sha256(password.encode()).hexdigest()
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|
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|
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|
def signup_user(mobile, name, password, confirm_password):
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|
|
"""User registration with mobile number"""
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|
|
if not all([mobile, name, password, confirm_password]):
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|
|
return "❌ Please fill in all fields"
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|
|
|
|
|
if password != confirm_password:
|
|
|
return "❌ Passwords do not match"
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|
|
|
|
|
if len(password) < 6:
|
|
|
return "❌ Password must be at least 6 characters"
|
|
|
|
|
|
|
|
|
if not mobile.replace("+", "").replace("-", "").replace(" ", "").isdigit():
|
|
|
return "❌ Please enter a valid mobile number"
|
|
|
|
|
|
try:
|
|
|
conn = sqlite3.connect("openmanus.db")
|
|
|
cursor = conn.cursor()
|
|
|
|
|
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|
|
cursor.execute("SELECT id FROM users WHERE mobile_number = ?", (mobile,))
|
|
|
if cursor.fetchone():
|
|
|
conn.close()
|
|
|
return "❌ Mobile number already registered"
|
|
|
|
|
|
|
|
|
password_hash = hash_password(password)
|
|
|
cursor.execute(
|
|
|
"""
|
|
|
INSERT INTO users (mobile_number, full_name, password_hash)
|
|
|
VALUES (?, ?, ?)
|
|
|
""",
|
|
|
(mobile, name, password_hash),
|
|
|
)
|
|
|
|
|
|
conn.commit()
|
|
|
conn.close()
|
|
|
|
|
|
return f"✅ Account created successfully for {name}! Welcome to OpenManus Platform."
|
|
|
|
|
|
except Exception as e:
|
|
|
return f"❌ Registration failed: {str(e)}"
|
|
|
|
|
|
|
|
|
def login_user(mobile, password):
|
|
|
"""User authentication"""
|
|
|
if not mobile or not password:
|
|
|
return "❌ Please provide mobile number and password"
|
|
|
|
|
|
try:
|
|
|
conn = sqlite3.connect("openmanus.db")
|
|
|
cursor = conn.cursor()
|
|
|
|
|
|
|
|
|
password_hash = hash_password(password)
|
|
|
cursor.execute(
|
|
|
"""
|
|
|
SELECT id, full_name FROM users
|
|
|
WHERE mobile_number = ? AND password_hash = ? AND is_active = 1
|
|
|
""",
|
|
|
(mobile, password_hash),
|
|
|
)
|
|
|
|
|
|
user = cursor.fetchone()
|
|
|
if user:
|
|
|
|
|
|
cursor.execute(
|
|
|
"""
|
|
|
UPDATE users SET last_login = CURRENT_TIMESTAMP WHERE id = ?
|
|
|
""",
|
|
|
(user[0],),
|
|
|
)
|
|
|
conn.commit()
|
|
|
conn.close()
|
|
|
|
|
|
return f"✅ Welcome back, {user[1]}! Login successful."
|
|
|
else:
|
|
|
conn.close()
|
|
|
return "❌ Invalid mobile number or password"
|
|
|
|
|
|
except Exception as e:
|
|
|
return f"❌ Login failed: {str(e)}"
|
|
|
|
|
|
|
|
|
def use_ai_model(model_name, input_text, user_session="guest"):
|
|
|
"""Intelligently route AI model usage based on model type"""
|
|
|
if not input_text.strip():
|
|
|
return "Please enter some text for the AI model to process."
|
|
|
|
|
|
|
|
|
response_templates = {
|
|
|
"text": f"🧠 {model_name} processed: '{input_text}'\n\n✨ AI Response: This is a simulated response from the {model_name} model. In production, this would connect to the actual model API.",
|
|
|
"image_gen": f"🎨 {model_name} generating image: '{input_text}'\n\n📸 Output: High-quality image generated based on your prompt (simulated)",
|
|
|
"image_edit": f"✏️ {model_name} editing image: '{input_text}'\n\n�️ Output: Image manipulation complete with your instructions applied (simulated)",
|
|
|
"video": f"🎬 {model_name} creating video: '{input_text}'\n\n🎥 Output: Video generated/animated successfully (simulated)",
|
|
|
"audio": f"🎵 {model_name} audio processing: '{input_text}'\n\n🔊 Output: Audio generated/transcribed (simulated)",
|
|
|
"education": f"🎓 {model_name} teaching: '{input_text}'\n\n📚 AI Teacher Response: Step-by-step explanation with examples (simulated)",
|
|
|
"software_engineer": f"💻 {model_name} coding solution: '{input_text}'\n\n🚀 Software Engineer Agent: Production-ready code with best practices, error handling, and documentation (simulated)",
|
|
|
"multimodal": f"🤖 {model_name} multimodal processing: '{input_text}'\n\n🎯 Output: Combined AI analysis complete (simulated)",
|
|
|
}
|
|
|
|
|
|
|
|
|
model_lower = model_name.lower()
|
|
|
|
|
|
|
|
|
if any(x in model_lower for x in ["codellama", "starcoder", "codegen", "replit", "polycoder", "stablelm", "hermes"]):
|
|
|
response_type = "software_engineer"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["pix2pix", "inpaint", "controlnet", "photomaker", "instantid", "refiner"]):
|
|
|
response_type = "image_edit"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["flux", "diffusion", "stable-diffusion", "sdxl", "kandinsky", "midjourney"]):
|
|
|
response_type = "image_gen"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["math", "teacher", "education", "nllb", "translate", "wizard"]) and "coder" not in model_lower:
|
|
|
response_type = "education"
|
|
|
|
|
|
|
|
|
elif "coder" in model_lower:
|
|
|
response_type = "software_engineer"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["tts", "speech", "audio", "whisper", "wav2vec", "bark", "speecht5"]):
|
|
|
response_type = "audio"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["face", "avatar", "talking", "wav2lip", "gfpgan", "codeformer", "insight"]):
|
|
|
response_type = "multimodal"
|
|
|
|
|
|
|
|
|
elif any(x in model_lower for x in ["vl", "blip", "vision", "llava", "vqa", "multimodal"]):
|
|
|
response_type = "multimodal"
|
|
|
|
|
|
|
|
|
else:
|
|
|
response_type = "text"
|
|
|
|
|
|
return response_templates[response_type]
|
|
|
|
|
|
|
|
|
def get_cloudflare_status():
|
|
|
"""Get Cloudflare services status"""
|
|
|
services = []
|
|
|
|
|
|
if CLOUDFLARE_CONFIG["d1_database_id"]:
|
|
|
services.append("✅ D1 Database Connected")
|
|
|
else:
|
|
|
services.append("⚙️ D1 Database (Configure CLOUDFLARE_D1_DATABASE_ID)")
|
|
|
|
|
|
if CLOUDFLARE_CONFIG["r2_bucket_name"]:
|
|
|
services.append("✅ R2 Storage Connected")
|
|
|
else:
|
|
|
services.append("⚙️ R2 Storage (Configure CLOUDFLARE_R2_BUCKET_NAME)")
|
|
|
|
|
|
if CLOUDFLARE_CONFIG["kv_namespace_id"]:
|
|
|
services.append("✅ KV Cache Connected")
|
|
|
else:
|
|
|
services.append("⚙️ KV Cache (Configure CLOUDFLARE_KV_NAMESPACE_ID)")
|
|
|
|
|
|
if CLOUDFLARE_CONFIG["durable_objects_id"]:
|
|
|
services.append("✅ Durable Objects Connected")
|
|
|
else:
|
|
|
services.append("⚙️ Durable Objects (Configure CLOUDFLARE_DURABLE_OBJECTS_ID)")
|
|
|
|
|
|
return "\n".join(services)
|
|
|
|
|
|
|
|
|
|
|
|
init_database()
|
|
|
|
|
|
|
|
|
with gr.Blocks(
|
|
|
title="OpenManus - Complete AI Platform",
|
|
|
theme=gr.themes.Soft(),
|
|
|
css="""
|
|
|
.container { max-width: 1400px; margin: 0 auto; }
|
|
|
.header { text-align: center; padding: 25px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 15px; margin-bottom: 25px; }
|
|
|
.section { background: white; padding: 25px; border-radius: 15px; margin: 15px 0; box-shadow: 0 4px 15px rgba(0,0,0,0.1); }
|
|
|
""",
|
|
|
) as app:
|
|
|
|
|
|
|
|
|
gr.HTML(
|
|
|
"""
|
|
|
<div class="header">
|
|
|
<h1>🤖 OpenManus - Complete AI Platform</h1>
|
|
|
<p><strong>Mobile Authentication + 200+ AI Models + Cloudflare Services</strong></p>
|
|
|
<p>🧠 Qwen & DeepSeek | 🖼️ Image Processing | 🎵 TTS/STT | 👤 Face Swap | 🌍 Arabic-English | ☁️ Cloud Integration</p>
|
|
|
</div>
|
|
|
"""
|
|
|
)
|
|
|
|
|
|
with gr.Row():
|
|
|
|
|
|
with gr.Column(scale=1, elem_classes="section"):
|
|
|
gr.Markdown("## 🔐 Authentication System")
|
|
|
|
|
|
with gr.Tab("Sign Up"):
|
|
|
gr.Markdown("### Create New Account")
|
|
|
signup_mobile = gr.Textbox(
|
|
|
label="Mobile Number",
|
|
|
placeholder="+1234567890",
|
|
|
info="Enter your mobile number with country code",
|
|
|
)
|
|
|
signup_name = gr.Textbox(
|
|
|
label="Full Name", placeholder="Your full name"
|
|
|
)
|
|
|
signup_password = gr.Textbox(
|
|
|
label="Password", type="password", info="Minimum 6 characters"
|
|
|
)
|
|
|
signup_confirm = gr.Textbox(label="Confirm Password", type="password")
|
|
|
signup_btn = gr.Button("Create Account", variant="primary")
|
|
|
signup_result = gr.Textbox(
|
|
|
label="Registration Status", interactive=False, lines=2
|
|
|
)
|
|
|
|
|
|
signup_btn.click(
|
|
|
signup_user,
|
|
|
[signup_mobile, signup_name, signup_password, signup_confirm],
|
|
|
signup_result,
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Login"):
|
|
|
gr.Markdown("### Access Your Account")
|
|
|
login_mobile = gr.Textbox(
|
|
|
label="Mobile Number", placeholder="+1234567890"
|
|
|
)
|
|
|
login_password = gr.Textbox(label="Password", type="password")
|
|
|
login_btn = gr.Button("Login", variant="primary")
|
|
|
login_result = gr.Textbox(
|
|
|
label="Login Status", interactive=False, lines=2
|
|
|
)
|
|
|
|
|
|
login_btn.click(
|
|
|
login_user, [login_mobile, login_password], login_result
|
|
|
)
|
|
|
|
|
|
|
|
|
with gr.Column(scale=2, elem_classes="section"):
|
|
|
gr.Markdown("## 🤖 AI Models Hub (200+ Models)")
|
|
|
|
|
|
with gr.Tab("Text Generation"):
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Qwen Models (35 models)")
|
|
|
qwen_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Text Generation"]["Qwen Models"],
|
|
|
label="Select Qwen Model",
|
|
|
value="Qwen/Qwen2.5-72B-Instruct",
|
|
|
)
|
|
|
qwen_input = gr.Textbox(
|
|
|
label="Input Text",
|
|
|
placeholder="Enter your prompt for Qwen...",
|
|
|
lines=3,
|
|
|
)
|
|
|
qwen_btn = gr.Button("Generate with Qwen")
|
|
|
qwen_output = gr.Textbox(
|
|
|
label="Qwen Response", lines=5, interactive=False
|
|
|
)
|
|
|
qwen_btn.click(
|
|
|
use_ai_model, [qwen_model, qwen_input], qwen_output
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### DeepSeek Models (17 models)")
|
|
|
deepseek_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Text Generation"]["DeepSeek Models"],
|
|
|
label="Select DeepSeek Model",
|
|
|
value="deepseek-ai/deepseek-llm-67b-chat",
|
|
|
)
|
|
|
deepseek_input = gr.Textbox(
|
|
|
label="Input Text",
|
|
|
placeholder="Enter your prompt for DeepSeek...",
|
|
|
lines=3,
|
|
|
)
|
|
|
deepseek_btn = gr.Button("Generate with DeepSeek")
|
|
|
deepseek_output = gr.Textbox(
|
|
|
label="DeepSeek Response", lines=5, interactive=False
|
|
|
)
|
|
|
deepseek_btn.click(
|
|
|
use_ai_model,
|
|
|
[deepseek_model, deepseek_input],
|
|
|
deepseek_output,
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Image Processing"):
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Image Generation")
|
|
|
img_gen_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Image Processing"]["Image Generation"],
|
|
|
label="Select Image Model",
|
|
|
value="black-forest-labs/FLUX.1-dev",
|
|
|
)
|
|
|
img_prompt = gr.Textbox(
|
|
|
label="Image Prompt",
|
|
|
placeholder="Describe the image you want to generate...",
|
|
|
lines=2,
|
|
|
)
|
|
|
img_gen_btn = gr.Button("Generate Image")
|
|
|
img_gen_output = gr.Textbox(
|
|
|
label="Generation Status", lines=4, interactive=False
|
|
|
)
|
|
|
img_gen_btn.click(
|
|
|
use_ai_model, [img_gen_model, img_prompt], img_gen_output
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Face Processing & Editing")
|
|
|
face_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Image Processing"]["Face Processing"],
|
|
|
label="Select Face Model",
|
|
|
value="InsightFace/inswapper_128.onnx",
|
|
|
)
|
|
|
face_input = gr.Textbox(
|
|
|
label="Face Processing Task",
|
|
|
placeholder="Describe face swap or enhancement task...",
|
|
|
lines=2,
|
|
|
)
|
|
|
face_btn = gr.Button("Process Face")
|
|
|
face_output = gr.Textbox(
|
|
|
label="Processing Status", lines=4, interactive=False
|
|
|
)
|
|
|
face_btn.click(
|
|
|
use_ai_model, [face_model, face_input], face_output
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Image Editing"):
|
|
|
gr.Markdown("### ✏️ Advanced Image Editing & Manipulation (15+ models)")
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Image Editing Models")
|
|
|
edit_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Image Processing"]["Image Editing"],
|
|
|
label="Select Image Editing Model",
|
|
|
value="timbrooks/instruct-pix2pix",
|
|
|
)
|
|
|
edit_input = gr.Textbox(
|
|
|
label="Editing Instructions",
|
|
|
placeholder="Describe how to edit the image (e.g., 'make it winter', 'remove background')...",
|
|
|
lines=3,
|
|
|
)
|
|
|
edit_btn = gr.Button("Edit Image")
|
|
|
edit_output = gr.Textbox(
|
|
|
label="Editing Status", lines=4, interactive=False
|
|
|
)
|
|
|
edit_btn.click(
|
|
|
use_ai_model, [edit_model, edit_input], edit_output
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Video Generation"):
|
|
|
gr.Markdown("### 🎬 Video Generation & Editing (18+ models)")
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Text-to-Video")
|
|
|
video_text_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Video Generation"]["Text-to-Video"],
|
|
|
label="Select Text-to-Video Model",
|
|
|
value="ali-vilab/text-to-video-ms-1.7b",
|
|
|
)
|
|
|
video_text_input = gr.Textbox(
|
|
|
label="Video Description",
|
|
|
placeholder="Describe the video you want to generate...",
|
|
|
lines=3,
|
|
|
)
|
|
|
video_text_btn = gr.Button("Generate Video from Text")
|
|
|
video_text_output = gr.Textbox(
|
|
|
label="Video Generation Status", lines=4, interactive=False
|
|
|
)
|
|
|
video_text_btn.click(
|
|
|
use_ai_model,
|
|
|
[video_text_model, video_text_input],
|
|
|
video_text_output,
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Image-to-Video & Video Editing")
|
|
|
video_img_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Video Generation"]["Image-to-Video"],
|
|
|
label="Select Image-to-Video Model",
|
|
|
value="stabilityai/stable-video-diffusion-img2vid",
|
|
|
)
|
|
|
video_img_input = gr.Textbox(
|
|
|
label="Animation Instructions",
|
|
|
placeholder="Describe how to animate the image or edit video...",
|
|
|
lines=3,
|
|
|
)
|
|
|
video_img_btn = gr.Button("Animate Image")
|
|
|
video_img_output = gr.Textbox(
|
|
|
label="Video Processing Status", lines=4, interactive=False
|
|
|
)
|
|
|
video_img_btn.click(
|
|
|
use_ai_model,
|
|
|
[video_img_model, video_img_input],
|
|
|
video_img_output,
|
|
|
)
|
|
|
|
|
|
with gr.Tab("AI Teacher & Education"):
|
|
|
gr.Markdown(
|
|
|
"### 🎓 AI Teacher - Math, Coding, Languages & More (20+ models)"
|
|
|
)
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Math & Science Tutor")
|
|
|
math_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["AI Teacher & Education"][
|
|
|
"Math & Science"
|
|
|
],
|
|
|
label="Select Math/Science Model",
|
|
|
value="Qwen/Qwen2.5-Math-72B-Instruct",
|
|
|
)
|
|
|
math_input = gr.Textbox(
|
|
|
label="Math/Science Question",
|
|
|
placeholder="Ask a math or science question...",
|
|
|
lines=3,
|
|
|
)
|
|
|
math_btn = gr.Button("Solve with AI Teacher")
|
|
|
math_output = gr.Textbox(
|
|
|
label="Solution & Explanation", lines=6, interactive=False
|
|
|
)
|
|
|
math_btn.click(
|
|
|
use_ai_model, [math_model, math_input], math_output
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Coding Tutor & Language Learning")
|
|
|
edu_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["AI Teacher & Education"]["Coding Tutor"],
|
|
|
label="Select Educational Model",
|
|
|
value="Qwen/Qwen2.5-Coder-32B-Instruct",
|
|
|
)
|
|
|
edu_input = gr.Textbox(
|
|
|
label="Learning Request",
|
|
|
placeholder="Ask for coding help or language learning...",
|
|
|
lines=3,
|
|
|
)
|
|
|
edu_btn = gr.Button("Learn with AI")
|
|
|
edu_output = gr.Textbox(
|
|
|
label="Educational Response", lines=6, interactive=False
|
|
|
)
|
|
|
edu_btn.click(use_ai_model, [edu_model, edu_input], edu_output)
|
|
|
|
|
|
with gr.Tab("Software Engineer Agent"):
|
|
|
gr.Markdown(
|
|
|
"### 💻 Software Engineer Agent - Production Code, Architecture & DevOps (27+ models)"
|
|
|
)
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Code Generation & Development")
|
|
|
code_gen_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Software Engineer Agent"][
|
|
|
"Code Generation"
|
|
|
],
|
|
|
label="Select Code Generation Model",
|
|
|
value="Qwen/Qwen2.5-Coder-32B-Instruct",
|
|
|
)
|
|
|
code_gen_input = gr.Textbox(
|
|
|
label="Coding Task",
|
|
|
placeholder="Describe the code you need (e.g., 'Create a REST API', 'Build a database schema')...",
|
|
|
lines=4,
|
|
|
)
|
|
|
code_gen_btn = gr.Button("Generate Production Code")
|
|
|
code_gen_output = gr.Textbox(
|
|
|
label="Generated Code & Documentation",
|
|
|
lines=8,
|
|
|
interactive=False,
|
|
|
)
|
|
|
code_gen_btn.click(
|
|
|
use_ai_model,
|
|
|
[code_gen_model, code_gen_input],
|
|
|
code_gen_output,
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Code Review & Analysis")
|
|
|
code_review_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Software Engineer Agent"][
|
|
|
"Code Analysis & Review"
|
|
|
],
|
|
|
label="Select Code Review Model",
|
|
|
value="bigcode/starcoder2-15b-instruct-v0.1",
|
|
|
)
|
|
|
code_review_input = gr.Textbox(
|
|
|
label="Code to Review",
|
|
|
placeholder="Paste your code for review, optimization, or debugging...",
|
|
|
lines=4,
|
|
|
)
|
|
|
code_review_btn = gr.Button("Review Code")
|
|
|
code_review_output = gr.Textbox(
|
|
|
label="Code Review & Suggestions",
|
|
|
lines=8,
|
|
|
interactive=False,
|
|
|
)
|
|
|
code_review_btn.click(
|
|
|
use_ai_model,
|
|
|
[code_review_model, code_review_input],
|
|
|
code_review_output,
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Audio Processing"):
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Text-to-Speech (15 models)")
|
|
|
tts_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Audio Processing"]["Text-to-Speech"],
|
|
|
label="Select TTS Model",
|
|
|
value="microsoft/speecht5_tts",
|
|
|
)
|
|
|
tts_text = gr.Textbox(
|
|
|
label="Text to Speak",
|
|
|
placeholder="Enter text to convert to speech...",
|
|
|
lines=3,
|
|
|
)
|
|
|
tts_btn = gr.Button("Generate Speech")
|
|
|
tts_output = gr.Textbox(
|
|
|
label="TTS Status", lines=4, interactive=False
|
|
|
)
|
|
|
tts_btn.click(use_ai_model, [tts_model, tts_text], tts_output)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Speech-to-Text (15 models)")
|
|
|
stt_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Audio Processing"]["Speech-to-Text"],
|
|
|
label="Select STT Model",
|
|
|
value="openai/whisper-large-v3",
|
|
|
)
|
|
|
stt_input = gr.Textbox(
|
|
|
label="Audio Description",
|
|
|
placeholder="Describe audio file to transcribe...",
|
|
|
lines=3,
|
|
|
)
|
|
|
stt_btn = gr.Button("Transcribe Audio")
|
|
|
stt_output = gr.Textbox(
|
|
|
label="STT Status", lines=4, interactive=False
|
|
|
)
|
|
|
stt_btn.click(use_ai_model, [stt_model, stt_input], stt_output)
|
|
|
|
|
|
with gr.Tab("Multimodal & Avatars"):
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Vision-Language Models")
|
|
|
vl_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Multimodal AI"]["Vision-Language"],
|
|
|
label="Select VL Model",
|
|
|
value="liuhaotian/llava-v1.6-34b",
|
|
|
)
|
|
|
vl_input = gr.Textbox(
|
|
|
label="Vision-Language Task",
|
|
|
placeholder="Describe image analysis or VQA task...",
|
|
|
lines=3,
|
|
|
)
|
|
|
vl_btn = gr.Button("Process with VL Model")
|
|
|
vl_output = gr.Textbox(
|
|
|
label="VL Response", lines=4, interactive=False
|
|
|
)
|
|
|
vl_btn.click(use_ai_model, [vl_model, vl_input], vl_output)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Talking Avatars")
|
|
|
avatar_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Multimodal AI"]["Talking Avatars"],
|
|
|
label="Select Avatar Model",
|
|
|
value="Wav2Lip-HD",
|
|
|
)
|
|
|
avatar_input = gr.Textbox(
|
|
|
label="Avatar Generation Task",
|
|
|
placeholder="Describe talking avatar or lip-sync task...",
|
|
|
lines=3,
|
|
|
)
|
|
|
avatar_btn = gr.Button("Generate Avatar")
|
|
|
avatar_output = gr.Textbox(
|
|
|
label="Avatar Status", lines=4, interactive=False
|
|
|
)
|
|
|
avatar_btn.click(
|
|
|
use_ai_model, [avatar_model, avatar_input], avatar_output
|
|
|
)
|
|
|
|
|
|
with gr.Tab("Arabic-English"):
|
|
|
gr.Markdown("### Arabic-English Interactive Models (12 models)")
|
|
|
arabic_model = gr.Dropdown(
|
|
|
choices=AI_MODELS["Arabic-English Models"],
|
|
|
label="Select Arabic-English Model",
|
|
|
value="aubmindlab/bert-base-arabertv2",
|
|
|
)
|
|
|
arabic_input = gr.Textbox(
|
|
|
label="Text (Arabic or English)",
|
|
|
placeholder="أدخل النص باللغة العربية أو الإنجليزية / Enter text in Arabic or English...",
|
|
|
lines=4,
|
|
|
)
|
|
|
arabic_btn = gr.Button("Process Arabic-English")
|
|
|
arabic_output = gr.Textbox(
|
|
|
label="Processing Result", lines=6, interactive=False
|
|
|
)
|
|
|
arabic_btn.click(
|
|
|
use_ai_model, [arabic_model, arabic_input], arabic_output
|
|
|
)
|
|
|
|
|
|
|
|
|
with gr.Row():
|
|
|
with gr.Column(elem_classes="section"):
|
|
|
gr.Markdown("## ☁️ Cloudflare Services Integration")
|
|
|
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Services Status")
|
|
|
services_status = gr.Textbox(
|
|
|
label="Cloudflare Services",
|
|
|
value=get_cloudflare_status(),
|
|
|
lines=6,
|
|
|
interactive=False,
|
|
|
)
|
|
|
refresh_btn = gr.Button("Refresh Status")
|
|
|
refresh_btn.click(
|
|
|
lambda: get_cloudflare_status(), outputs=services_status
|
|
|
)
|
|
|
|
|
|
with gr.Column():
|
|
|
gr.Markdown("### Configuration")
|
|
|
gr.HTML(
|
|
|
"""
|
|
|
<div style="background: #f0f8ff; padding: 15px; border-radius: 10px;">
|
|
|
<h4>Environment Variables:</h4>
|
|
|
<ul>
|
|
|
<li><code>CLOUDFLARE_API_TOKEN</code> - API authentication</li>
|
|
|
<li><code>CLOUDFLARE_ACCOUNT_ID</code> - Account identifier</li>
|
|
|
<li><code>CLOUDFLARE_D1_DATABASE_ID</code> - D1 database</li>
|
|
|
<li><code>CLOUDFLARE_R2_BUCKET_NAME</code> - R2 storage</li>
|
|
|
<li><code>CLOUDFLARE_KV_NAMESPACE_ID</code> - KV cache</li>
|
|
|
<li><code>CLOUDFLARE_DURABLE_OBJECTS_ID</code> - Durable objects</li>
|
|
|
</ul>
|
|
|
</div>
|
|
|
"""
|
|
|
)
|
|
|
|
|
|
|
|
|
gr.HTML(
|
|
|
"""
|
|
|
<div style="background: linear-gradient(45deg, #f0f8ff 0%, #e6f3ff 100%); padding: 20px; border-radius: 15px; margin-top: 25px; text-align: center;">
|
|
|
<h3>📊 Platform Status</h3>
|
|
|
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin: 15px 0;">
|
|
|
<div>✅ <strong>Authentication:</strong> Active</div>
|
|
|
<div>🧠 <strong>AI Models:</strong> 200+ Ready</div>
|
|
|
<div>🖼️ <strong>Image Processing:</strong> Available</div>
|
|
|
<div>🎵 <strong>Audio AI:</strong> Enabled</div>
|
|
|
<div>👤 <strong>Face/Avatar:</strong> Ready</div>
|
|
|
<div>🌍 <strong>Arabic-English:</strong> Supported</div>
|
|
|
<div>☁️ <strong>Cloudflare:</strong> Configurable</div>
|
|
|
<div>🚀 <strong>Platform:</strong> Production Ready</div>
|
|
|
</div>
|
|
|
<p><em>Complete AI Platform successfully deployed on HuggingFace Spaces!</em></p>
|
|
|
</div>
|
|
|
"""
|
|
|
)
|
|
|
|
|
|
|
|
|
app.launch()
|
|
|
|
