Auto-commit: app_complete.py updated

app_complete.py

@@ -13,106 +13,192 @@ CLOUDFLARE_CONFIG = {
     "d1_database_id": os.getenv("CLOUDFLARE_D1_DATABASE_ID", ""),
     "r2_bucket_name": os.getenv("CLOUDFLARE_R2_BUCKET_NAME", ""),
     "kv_namespace_id": os.getenv("CLOUDFLARE_KV_NAMESPACE_ID", ""),
-    "durable_objects_id": os.getenv("CLOUDFLARE_DURABLE_OBJECTS_ID", "")
+    "durable_objects_id": os.getenv("CLOUDFLARE_DURABLE_OBJECTS_ID", ""),
 }
 
 # AI Model Categories with 200+ models
 AI_MODELS = {
     "Text Generation": {
         "Qwen Models": [
-            "Qwen/Qwen2.5-72B-Instruct", "Qwen/Qwen2.5-32B-Instruct", "Qwen/Qwen2.5-14B-Instruct",
-            "Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-3B-Instruct", "Qwen/Qwen2.5-1.5B-Instruct",
-            "Qwen/Qwen2.5-0.5B-Instruct", "Qwen/Qwen2-72B-Instruct", "Qwen/Qwen2-57B-A14B-Instruct",
-            "Qwen/Qwen2-7B-Instruct", "Qwen/Qwen2-1.5B-Instruct", "Qwen/Qwen2-0.5B-Instruct",
-            "Qwen/Qwen1.5-110B-Chat", "Qwen/Qwen1.5-72B-Chat", "Qwen/Qwen1.5-32B-Chat",
-            "Qwen/Qwen1.5-14B-Chat", "Qwen/Qwen1.5-7B-Chat", "Qwen/Qwen1.5-4B-Chat",
-            "Qwen/Qwen1.5-1.8B-Chat", "Qwen/Qwen1.5-0.5B-Chat", "Qwen/CodeQwen1.5-7B-Chat",
-            "Qwen/Qwen2.5-Math-72B-Instruct", "Qwen/Qwen2.5-Math-7B-Instruct", "Qwen/Qwen2.5-Coder-32B-Instruct",
-            "Qwen/Qwen2.5-Coder-14B-Instruct", "Qwen/Qwen2.5-Coder-7B-Instruct", "Qwen/Qwen2.5-Coder-3B-Instruct",
-            "Qwen/Qwen2.5-Coder-1.5B-Instruct", "Qwen/Qwen2.5-Coder-0.5B-Instruct", "Qwen/QwQ-32B-Preview",
-            "Qwen/Qwen2-VL-72B-Instruct", "Qwen/Qwen2-VL-7B-Instruct", "Qwen/Qwen2-VL-2B-Instruct",
-            "Qwen/Qwen2-Audio-7B-Instruct", "Qwen/Qwen-Agent-Chat", "Qwen/Qwen-VL-Chat"
+            "Qwen/Qwen2.5-72B-Instruct",
+            "Qwen/Qwen2.5-32B-Instruct",
+            "Qwen/Qwen2.5-14B-Instruct",
+            "Qwen/Qwen2.5-7B-Instruct",
+            "Qwen/Qwen2.5-3B-Instruct",
+            "Qwen/Qwen2.5-1.5B-Instruct",
+            "Qwen/Qwen2.5-0.5B-Instruct",
+            "Qwen/Qwen2-72B-Instruct",
+            "Qwen/Qwen2-57B-A14B-Instruct",
+            "Qwen/Qwen2-7B-Instruct",
+            "Qwen/Qwen2-1.5B-Instruct",
+            "Qwen/Qwen2-0.5B-Instruct",
+            "Qwen/Qwen1.5-110B-Chat",
+            "Qwen/Qwen1.5-72B-Chat",
+            "Qwen/Qwen1.5-32B-Chat",
+            "Qwen/Qwen1.5-14B-Chat",
+            "Qwen/Qwen1.5-7B-Chat",
+            "Qwen/Qwen1.5-4B-Chat",
+            "Qwen/Qwen1.5-1.8B-Chat",
+            "Qwen/Qwen1.5-0.5B-Chat",
+            "Qwen/CodeQwen1.5-7B-Chat",
+            "Qwen/Qwen2.5-Math-72B-Instruct",
+            "Qwen/Qwen2.5-Math-7B-Instruct",
+            "Qwen/Qwen2.5-Coder-32B-Instruct",
+            "Qwen/Qwen2.5-Coder-14B-Instruct",
+            "Qwen/Qwen2.5-Coder-7B-Instruct",
+            "Qwen/Qwen2.5-Coder-3B-Instruct",
+            "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+            "Qwen/Qwen2.5-Coder-0.5B-Instruct",
+            "Qwen/QwQ-32B-Preview",
+            "Qwen/Qwen2-VL-72B-Instruct",
+            "Qwen/Qwen2-VL-7B-Instruct",
+            "Qwen/Qwen2-VL-2B-Instruct",
+            "Qwen/Qwen2-Audio-7B-Instruct",
+            "Qwen/Qwen-Agent-Chat",
+            "Qwen/Qwen-VL-Chat",
         ],
         "DeepSeek Models": [
-            "deepseek-ai/deepseek-llm-67b-chat", "deepseek-ai/deepseek-llm-7b-chat",
-            "deepseek-ai/deepseek-coder-33b-instruct", "deepseek-ai/deepseek-coder-7b-instruct",
-            "deepseek-ai/deepseek-coder-6.7b-instruct", "deepseek-ai/deepseek-coder-1.3b-instruct",
-            "deepseek-ai/DeepSeek-V2-Chat", "deepseek-ai/DeepSeek-V2-Lite-Chat",
-            "deepseek-ai/deepseek-math-7b-instruct", "deepseek-ai/deepseek-moe-16b-chat",
-            "deepseek-ai/deepseek-vl-7b-chat", "deepseek-ai/deepseek-vl-1.3b-chat",
-            "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
-            "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
-            "deepseek-ai/DeepSeek-Reasoner-R1"
-        ]
+            "deepseek-ai/deepseek-llm-67b-chat",
+            "deepseek-ai/deepseek-llm-7b-chat",
+            "deepseek-ai/deepseek-coder-33b-instruct",
+            "deepseek-ai/deepseek-coder-7b-instruct",
+            "deepseek-ai/deepseek-coder-6.7b-instruct",
+            "deepseek-ai/deepseek-coder-1.3b-instruct",
+            "deepseek-ai/DeepSeek-V2-Chat",
+            "deepseek-ai/DeepSeek-V2-Lite-Chat",
+            "deepseek-ai/deepseek-math-7b-instruct",
+            "deepseek-ai/deepseek-moe-16b-chat",
+            "deepseek-ai/deepseek-vl-7b-chat",
+            "deepseek-ai/deepseek-vl-1.3b-chat",
+            "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+            "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
+            "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
+            "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
+            "deepseek-ai/DeepSeek-Reasoner-R1",
+        ],
     },
     "Image Processing": {
         "Image Generation": [
-            "black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-pro",
-            "runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-xl-base-1.0",
-            "stabilityai/stable-diffusion-3-medium-diffusers", "stabilityai/sd-turbo",
-            "kandinsky-community/kandinsky-2-2-decoder", "playgroundai/playground-v2.5-1024px-aesthetic",
-            "midjourney/midjourney-v6"
+            "black-forest-labs/FLUX.1-dev",
+            "black-forest-labs/FLUX.1-schnell",
+            "black-forest-labs/FLUX.1-pro",
+            "runwayml/stable-diffusion-v1-5",
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            "stabilityai/stable-diffusion-3-medium-diffusers",
+            "stabilityai/sd-turbo",
+            "kandinsky-community/kandinsky-2-2-decoder",
+            "playgroundai/playground-v2.5-1024px-aesthetic",
+            "midjourney/midjourney-v6",
         ],
         "Image Editing": [
-            "timbrooks/instruct-pix2pix", "runwayml/stable-diffusion-inpainting",
-            "stabilityai/stable-diffusion-xl-refiner-1.0", "lllyasviel/control_v11p_sd15_inpaint",
-            "SG161222/RealVisXL_V4.0", "ByteDance/SDXL-Lightning", "segmind/SSD-1B",
-            "segmind/Segmind-Vega", "playgroundai/playground-v2-1024px-aesthetic",
-            "stabilityai/stable-cascade"
+            "timbrooks/instruct-pix2pix",
+            "runwayml/stable-diffusion-inpainting",
+            "stabilityai/stable-diffusion-xl-refiner-1.0",
+            "lllyasviel/control_v11p_sd15_inpaint",
+            "SG161222/RealVisXL_V4.0",
+            "ByteDance/SDXL-Lightning",
+            "segmind/SSD-1B",
+            "segmind/Segmind-Vega",
+            "playgroundai/playground-v2-1024px-aesthetic",
+            "stabilityai/stable-cascade",
         ],
         "Face Processing": [
-            "InsightFace/inswapper_128.onnx", "deepinsight/insightface", "TencentARC/GFPGAN",
-            "sczhou/CodeFormer", "xinntao/Real-ESRGAN", "ESRGAN/ESRGAN"
-        ]
+            "InsightFace/inswapper_128.onnx",
+            "deepinsight/insightface",
+            "TencentARC/GFPGAN",
+            "sczhou/CodeFormer",
+            "xinntao/Real-ESRGAN",
+            "ESRGAN/ESRGAN",
+        ],
     },
     "Audio Processing": {
         "Text-to-Speech": [
-            "microsoft/speecht5_tts", "facebook/mms-tts-eng", "facebook/mms-tts-ara",
-            "coqui/XTTS-v2", "suno/bark", "parler-tts/parler-tts-large-v1",
-            "microsoft/DisTTS", "facebook/fastspeech2-en-ljspeech", "espnet/kan-bayashi_ljspeech_vits",
-            "facebook/tts_transformer-en-ljspeech", "microsoft/SpeechT5", "Voicemod/fastspeech2-en-male1",
-            "facebook/mms-tts-spa", "facebook/mms-tts-fra", "facebook/mms-tts-deu"
+            "microsoft/speecht5_tts",
+            "facebook/mms-tts-eng",
+            "facebook/mms-tts-ara",
+            "coqui/XTTS-v2",
+            "suno/bark",
+            "parler-tts/parler-tts-large-v1",
+            "microsoft/DisTTS",
+            "facebook/fastspeech2-en-ljspeech",
+            "espnet/kan-bayashi_ljspeech_vits",
+            "facebook/tts_transformer-en-ljspeech",
+            "microsoft/SpeechT5",
+            "Voicemod/fastspeech2-en-male1",
+            "facebook/mms-tts-spa",
+            "facebook/mms-tts-fra",
+            "facebook/mms-tts-deu",
         ],
         "Speech-to-Text": [
-            "openai/whisper-large-v3", "openai/whisper-large-v2", "openai/whisper-medium",
-            "openai/whisper-small", "openai/whisper-base", "openai/whisper-tiny",
-            "facebook/wav2vec2-large-960h", "facebook/wav2vec2-base-960h",
-            "microsoft/unispeech-sat-large", "nvidia/stt_en_conformer_ctc_large",
-            "speechbrain/asr-wav2vec2-commonvoice-en", "facebook/mms-1b-all", "facebook/seamless-m4t-v2-large",
-            "distil-whisper/distil-large-v3", "distil-whisper/distil-medium.en"
-        ]
+            "openai/whisper-large-v3",
+            "openai/whisper-large-v2",
+            "openai/whisper-medium",
+            "openai/whisper-small",
+            "openai/whisper-base",
+            "openai/whisper-tiny",
+            "facebook/wav2vec2-large-960h",
+            "facebook/wav2vec2-base-960h",
+            "microsoft/unispeech-sat-large",
+            "nvidia/stt_en_conformer_ctc_large",
+            "speechbrain/asr-wav2vec2-commonvoice-en",
+            "facebook/mms-1b-all",
+            "facebook/seamless-m4t-v2-large",
+            "distil-whisper/distil-large-v3",
+            "distil-whisper/distil-medium.en",
+        ],
     },
     "Multimodal AI": {
         "Vision-Language": [
-            "microsoft/DialoGPT-large", "microsoft/blip-image-captioning-large",
-            "microsoft/blip2-opt-6.7b", "microsoft/blip2-flan-t5-xl",
-            "salesforce/blip-vqa-capfilt-large", "dandelin/vilt-b32-finetuned-vqa",
-            "google/pix2struct-ai2d-base", "microsoft/git-large-coco", "microsoft/git-base-vqa",
-            "liuhaotian/llava-v1.6-34b", "liuhaotian/llava-v1.6-vicuna-7b"
+            "microsoft/DialoGPT-large",
+            "microsoft/blip-image-captioning-large",
+            "microsoft/blip2-opt-6.7b",
+            "microsoft/blip2-flan-t5-xl",
+            "salesforce/blip-vqa-capfilt-large",
+            "dandelin/vilt-b32-finetuned-vqa",
+            "google/pix2struct-ai2d-base",
+            "microsoft/git-large-coco",
+            "microsoft/git-base-vqa",
+            "liuhaotian/llava-v1.6-34b",
+            "liuhaotian/llava-v1.6-vicuna-7b",
         ],
         "Talking Avatars": [
-            "microsoft/SpeechT5-TTS-Avatar", "Wav2Lip-HD", "First-Order-Model",
-            "LipSync-Expert", "DeepFaceLive", "FaceSwapper-Live", "RealTime-FaceRig",
-            "AI-Avatar-Generator", "TalkingHead-3D"
-        ]
+            "microsoft/SpeechT5-TTS-Avatar",
+            "Wav2Lip-HD",
+            "First-Order-Model",
+            "LipSync-Expert",
+            "DeepFaceLive",
+            "FaceSwapper-Live",
+            "RealTime-FaceRig",
+            "AI-Avatar-Generator",
+            "TalkingHead-3D",
+        ],
     },
     "Arabic-English Models": [
-        "aubmindlab/bert-base-arabertv2", "aubmindlab/aragpt2-base", "aubmindlab/aragpt2-medium",
-        "CAMeL-Lab/bert-base-arabic-camelbert-mix", "asafaya/bert-base-arabic",
-        "UBC-NLP/MARBERT", "UBC-NLP/ARBERTv2", "facebook/nllb-200-3.3B",
-        "facebook/m2m100_1.2B", "Helsinki-NLP/opus-mt-ar-en", "Helsinki-NLP/opus-mt-en-ar",
-        "microsoft/DialoGPT-medium-arabic"
-    ]
+        "aubmindlab/bert-base-arabertv2",
+        "aubmindlab/aragpt2-base",
+        "aubmindlab/aragpt2-medium",
+        "CAMeL-Lab/bert-base-arabic-camelbert-mix",
+        "asafaya/bert-base-arabic",
+        "UBC-NLP/MARBERT",
+        "UBC-NLP/ARBERTv2",
+        "facebook/nllb-200-3.3B",
+        "facebook/m2m100_1.2B",
+        "Helsinki-NLP/opus-mt-ar-en",
+        "Helsinki-NLP/opus-mt-en-ar",
+        "microsoft/DialoGPT-medium-arabic",
+    ],
 }
 
+
 def init_database():
     """Initialize SQLite database for authentication"""
     db_path = Path("openmanus.db")
     conn = sqlite3.connect(db_path)
     cursor = conn.cursor()
-    
+
     # Create users table
-    cursor.execute("""
+    cursor.execute(
+        """
     CREATE TABLE IF NOT EXISTS users (
         id INTEGER PRIMARY KEY AUTOINCREMENT,
         mobile_number TEXT UNIQUE NOT NULL,
@@ -122,10 +208,12 @@ def init_database():
         last_login TIMESTAMP,
         is_active BOOLEAN DEFAULT 1
     )
-    """)
-    
+    """
+    )
+
     # Create sessions table
-    cursor.execute("""
+    cursor.execute(
+        """
     CREATE TABLE IF NOT EXISTS sessions (
         id TEXT PRIMARY KEY,
         user_id INTEGER NOT NULL,
@@ -135,10 +223,12 @@ def init_database():
         user_agent TEXT,
         FOREIGN KEY (user_id) REFERENCES users (id)
     )
-    """)
-    
+    """
+    )
+
     # Create model usage table
-    cursor.execute("""
+    cursor.execute(
+        """
     CREATE TABLE IF NOT EXISTS model_usage (
         id INTEGER PRIMARY KEY AUTOINCREMENT,
         user_id INTEGER,
@@ -150,228 +240,236 @@ def init_database():
         processing_time REAL,
         FOREIGN KEY (user_id) REFERENCES users (id)
     )
-    """)
-    
+    """
+    )
+
     conn.commit()
     conn.close()
     return True
 
+
 def hash_password(password):
     """Hash password using SHA-256"""
     return hashlib.sha256(password.encode()).hexdigest()
 
+
 def signup_user(mobile, name, password, confirm_password):
     """User registration with mobile number"""
     if not all([mobile, name, password, confirm_password]):
         return "❌ Please fill in all fields"
-    
+
     if password != confirm_password:
         return "❌ Passwords do not match"
-    
+
     if len(password) < 6:
         return "❌ Password must be at least 6 characters"
-    
+
     # Validate mobile number
     if not mobile.replace("+", "").replace("-", "").replace(" ", "").isdigit():
         return "❌ Please enter a valid mobile number"
-    
+
     try:
         conn = sqlite3.connect("openmanus.db")
         cursor = conn.cursor()
-        
+
         # Check if mobile number already exists
         cursor.execute("SELECT id FROM users WHERE mobile_number = ?", (mobile,))
         if cursor.fetchone():
             conn.close()
             return "❌ Mobile number already registered"
-        
+
         # Create new user
         password_hash = hash_password(password)
-        cursor.execute("""
+        cursor.execute(
+            """
         INSERT INTO users (mobile_number, full_name, password_hash)
         VALUES (?, ?, ?)
-        """, (mobile, name, password_hash))
-        
+        """,
+            (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()
-        
+
         # Verify credentials
         password_hash = hash_password(password)
-        cursor.execute("""
-        SELECT id, full_name FROM users 
+        cursor.execute(
+            """
+        SELECT id, full_name FROM users
         WHERE mobile_number = ? AND password_hash = ? AND is_active = 1
-        """, (mobile, password_hash))
-        
+        """,
+            (mobile, password_hash),
+        )
+
         user = cursor.fetchone()
         if user:
             # Update last login
-            cursor.execute("""
+            cursor.execute(
+                """
             UPDATE users SET last_login = CURRENT_TIMESTAMP WHERE id = ?
-            """, (user[0],))
+            """,
+                (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"):
     """Simulate AI model usage"""
     if not input_text.strip():
         return "Please enter some text for the AI model to process."
-    
+
     # Simulate model processing
     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": f"🖼️ {model_name} would generate/edit an image based on: '{input_text}'\n\n📸 Output: Image processing complete (simulated)",
         "audio": f"🎵 {model_name} audio processing for: '{input_text}'\n\n🔊 Output: Audio generated/processed (simulated)",
-        "multimodal": f"🤖 {model_name} multimodal processing: '{input_text}'\n\n🎯 Output: Combined AI analysis complete (simulated)"
+        "multimodal": f"🤖 {model_name} multimodal processing: '{input_text}'\n\n🎯 Output: Combined AI analysis complete (simulated)",
     }
-    
+
     # Determine response type based on model
-    if any(x in model_name.lower() for x in ["image", "flux", "diffusion", "face", "avatar"]):
+    if any(
+        x in model_name.lower()
+        for x in ["image", "flux", "diffusion", "face", "avatar"]
+    ):
         response_type = "image"
-    elif any(x in model_name.lower() for x in ["tts", "speech", "audio", "whisper", "wav2vec"]):
-        response_type = "audio" 
+    elif any(
+        x in model_name.lower()
+        for x in ["tts", "speech", "audio", "whisper", "wav2vec"]
+    ):
+        response_type = "audio"
     elif any(x in model_name.lower() for x in ["vl", "blip", "vision", "talking"]):
         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") 
+        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)
 
+
 # Initialize database
 init_database()
 
 # Create Gradio interface
 with gr.Blocks(
-    title="OpenManus - Complete AI Platform", 
+    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:
-    
+
     # Header
-    gr.HTML("""
+    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():
         # Authentication Section
         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", 
+                    label="Mobile Number",
                     placeholder="+1234567890",
-                    info="Enter your mobile number with country code"
+                    info="Enter your mobile number with country code",
                 )
                 signup_name = gr.Textbox(
-                    label="Full Name", 
-                    placeholder="Your full name"
+                    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"
+                    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
+                    label="Registration Status", interactive=False, lines=2
                 )
-                
+
                 signup_btn.click(
                     signup_user,
                     [signup_mobile, signup_name, signup_password, signup_confirm],
-                    signup_result
+                    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"
+                    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
+                    label="Login Status", interactive=False, lines=2
                 )
-                
+
                 login_btn.click(
-                    login_user,
-                    [login_mobile, login_password],
-                    login_result
+                    login_user, [login_mobile, login_password], login_result
                 )
-        
-        # AI Models Section  
+
+        # AI Models Section
         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():
@@ -379,41 +477,43 @@ with gr.Blocks(
                         qwen_model = gr.Dropdown(
                             choices=AI_MODELS["Text Generation"]["Qwen Models"],
                             label="Select Qwen Model",
-                            value="Qwen/Qwen2.5-72B-Instruct"
+                            value="Qwen/Qwen2.5-72B-Instruct",
                         )
                         qwen_input = gr.Textbox(
                             label="Input Text",
                             placeholder="Enter your prompt for Qwen...",
-                            lines=3
+                            lines=3,
                         )
                         qwen_btn = gr.Button("Generate with Qwen")
                         qwen_output = gr.Textbox(
-                            label="Qwen Response",
-                            lines=5,
-                            interactive=False
+                            label="Qwen Response", lines=5, interactive=False
                         )
-                        qwen_btn.click(use_ai_model, [qwen_model, qwen_input], qwen_output)
-                    
+                        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"
+                            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
+                            lines=3,
                         )
                         deepseek_btn = gr.Button("Generate with DeepSeek")
                         deepseek_output = gr.Textbox(
-                            label="DeepSeek Response",
-                            lines=5,
-                            interactive=False
+                            label="DeepSeek Response", lines=5, interactive=False
                         )
-                        deepseek_btn.click(use_ai_model, [deepseek_model, deepseek_input], deepseek_output)
-            
+                        deepseek_btn.click(
+                            use_ai_model,
+                            [deepseek_model, deepseek_input],
+                            deepseek_output,
+                        )
+
             with gr.Tab("Image Processing"):
                 with gr.Row():
                     with gr.Column():
@@ -421,41 +521,41 @@ with gr.Blocks(
                         img_gen_model = gr.Dropdown(
                             choices=AI_MODELS["Image Processing"]["Image Generation"],
                             label="Select Image Model",
-                            value="black-forest-labs/FLUX.1-dev"
+                            value="black-forest-labs/FLUX.1-dev",
                         )
                         img_prompt = gr.Textbox(
                             label="Image Prompt",
                             placeholder="Describe the image you want to generate...",
-                            lines=2
+                            lines=2,
                         )
                         img_gen_btn = gr.Button("Generate Image")
                         img_gen_output = gr.Textbox(
-                            label="Generation Status",
-                            lines=4,
-                            interactive=False
+                            label="Generation Status", lines=4, interactive=False
+                        )
+                        img_gen_btn.click(
+                            use_ai_model, [img_gen_model, img_prompt], img_gen_output
                         )
-                        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"
+                            value="InsightFace/inswapper_128.onnx",
                         )
                         face_input = gr.Textbox(
                             label="Face Processing Task",
                             placeholder="Describe face swap or enhancement task...",
-                            lines=2
+                            lines=2,
                         )
                         face_btn = gr.Button("Process Face")
                         face_output = gr.Textbox(
-                            label="Processing Status",
-                            lines=4,
-                            interactive=False
+                            label="Processing Status", lines=4, interactive=False
+                        )
+                        face_btn.click(
+                            use_ai_model, [face_model, face_input], face_output
                         )
-                        face_btn.click(use_ai_model, [face_model, face_input], face_output)
-            
+
             with gr.Tab("Audio Processing"):
                 with gr.Row():
                     with gr.Column():
@@ -463,41 +563,37 @@ with gr.Blocks(
                         tts_model = gr.Dropdown(
                             choices=AI_MODELS["Audio Processing"]["Text-to-Speech"],
                             label="Select TTS Model",
-                            value="microsoft/speecht5_tts"
+                            value="microsoft/speecht5_tts",
                         )
                         tts_text = gr.Textbox(
                             label="Text to Speak",
                             placeholder="Enter text to convert to speech...",
-                            lines=3
+                            lines=3,
                         )
                         tts_btn = gr.Button("Generate Speech")
                         tts_output = gr.Textbox(
-                            label="TTS Status",
-                            lines=4,
-                            interactive=False
+                            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"
+                            value="openai/whisper-large-v3",
                         )
                         stt_input = gr.Textbox(
                             label="Audio Description",
                             placeholder="Describe audio file to transcribe...",
-                            lines=3
+                            lines=3,
                         )
                         stt_btn = gr.Button("Transcribe Audio")
                         stt_output = gr.Textbox(
-                            label="STT Status",
-                            lines=4,
-                            interactive=False
+                            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():
@@ -505,66 +601,64 @@ with gr.Blocks(
                         vl_model = gr.Dropdown(
                             choices=AI_MODELS["Multimodal AI"]["Vision-Language"],
                             label="Select VL Model",
-                            value="liuhaotian/llava-v1.6-34b"
+                            value="liuhaotian/llava-v1.6-34b",
                         )
                         vl_input = gr.Textbox(
                             label="Vision-Language Task",
                             placeholder="Describe image analysis or VQA task...",
-                            lines=3
+                            lines=3,
                         )
                         vl_btn = gr.Button("Process with VL Model")
                         vl_output = gr.Textbox(
-                            label="VL Response",
-                            lines=4,
-                            interactive=False
+                            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"
+                            value="Wav2Lip-HD",
                         )
                         avatar_input = gr.Textbox(
                             label="Avatar Generation Task",
                             placeholder="Describe talking avatar or lip-sync task...",
-                            lines=3
+                            lines=3,
                         )
                         avatar_btn = gr.Button("Generate Avatar")
                         avatar_output = gr.Textbox(
-                            label="Avatar Status",
-                            lines=4,
-                            interactive=False
+                            label="Avatar Status", lines=4, interactive=False
+                        )
+                        avatar_btn.click(
+                            use_ai_model, [avatar_model, avatar_input], avatar_output
                         )
-                        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"
+                    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
+                    lines=4,
                 )
                 arabic_btn = gr.Button("Process Arabic-English")
                 arabic_output = gr.Textbox(
-                    label="Processing Result",
-                    lines=6,
-                    interactive=False
+                    label="Processing Result", lines=6, interactive=False
+                )
+                arabic_btn.click(
+                    use_ai_model, [arabic_model, arabic_input], arabic_output
                 )
-                arabic_btn.click(use_ai_model, [arabic_model, arabic_input], arabic_output)
-    
+
     # Services Status Section
     with gr.Row():
         with gr.Column(elem_classes="section"):
             gr.Markdown("## ☁️ Cloudflare Services Integration")
-            
+
             with gr.Row():
                 with gr.Column():
                     gr.Markdown("### Services Status")
@@ -572,32 +666,34 @@ with gr.Blocks(
                         label="Cloudflare Services",
                         value=get_cloudflare_status(),
                         lines=6,
-                        interactive=False
+                        interactive=False,
                     )
                     refresh_btn = gr.Button("Refresh Status")
                     refresh_btn.click(
-                        lambda: get_cloudflare_status(),
-                        outputs=services_status
+                        lambda: get_cloudflare_status(), outputs=services_status
                     )
-                
+
                 with gr.Column():
                     gr.Markdown("### Configuration")
-                    gr.HTML("""
+                    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_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>
-                    """)
-    
+                    """
+                    )
+
     # Footer Status
-    gr.HTML("""
+    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;">
@@ -612,7 +708,8 @@ with gr.Blocks(
         </div>
         <p><em>Complete AI Platform successfully deployed on HuggingFace Spaces with Docker!</em></p>
     </div>
-    """)
+    """
+    )
 
 if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860)
+    app.launch(server_name="0.0.0.0", server_port=7860)