Upload folder using huggingface_hub

app.py

@@ -8,17 +8,27 @@ from pathlib import Path
 from huggingface_hub import InferenceClient
 
 # Initialize HuggingFace Inference Client for real AI responses
-HF_TOKEN = os.getenv("HF_TOKEN", "")  # Set in HuggingFace Space Settings -> Repository Secrets
+HF_TOKEN = os.getenv(
+    "HF_TOKEN", ""
+)  # Set in HuggingFace Space Settings -> Repository Secrets
 inference_client = InferenceClient(token=HF_TOKEN if HF_TOKEN else None)
 
 # Cloudflare configuration - credentials from wrangler.toml and CLI
 CLOUDFLARE_CONFIG = {
     "api_token": os.getenv("CLOUDFLARE_API_TOKEN", ""),
-    "account_id": os.getenv("CLOUDFLARE_ACCOUNT_ID", "62af59a7ac82b29543577ee6800735ee"),
-    "d1_database_id": os.getenv("CLOUDFLARE_D1_DATABASE_ID", "6d887f74-98ac-4db7-bfed-8061903d1f6c"),
+    "account_id": os.getenv(
+        "CLOUDFLARE_ACCOUNT_ID", "62af59a7ac82b29543577ee6800735ee"
+    ),
+    "d1_database_id": os.getenv(
+        "CLOUDFLARE_D1_DATABASE_ID", "6d887f74-98ac-4db7-bfed-8061903d1f6c"
+    ),
     "r2_bucket_name": os.getenv("CLOUDFLARE_R2_BUCKET_NAME", "openmanus-storage"),
-    "kv_namespace_id": os.getenv("CLOUDFLARE_KV_NAMESPACE_ID", "87f4aa01410d4fb19821f61006f94441"),
-    "kv_namespace_cache": os.getenv("CLOUDFLARE_KV_CACHE_ID", "7b58c88292c847d1a82c8e0dd5129f37"),
+    "kv_namespace_id": os.getenv(
+        "CLOUDFLARE_KV_NAMESPACE_ID", "87f4aa01410d4fb19821f61006f94441"
+    ),
+    "kv_namespace_cache": os.getenv(
+        "CLOUDFLARE_KV_CACHE_ID", "7b58c88292c847d1a82c8e0dd5129f37"
+    ),
     "durable_objects_sessions": "AGENT_SESSIONS",
     "durable_objects_chatrooms": "CHAT_ROOMS",
 }
@@ -452,17 +462,47 @@ def use_ai_model(model_name, input_text, user_session="guest"):
 
     # Determine model category for specialized handling
     category = "text"
-    if any(x in model_lower for x in ["codellama", "starcoder", "codegen", "replit", "polycoder", "coder"]):
+    if any(
+        x in model_lower
+        for x in ["codellama", "starcoder", "codegen", "replit", "polycoder", "coder"]
+    ):
         category = "software_engineer"
-    elif any(x in model_lower for x in ["flux", "diffusion", "stable-diffusion", "sdxl", "kandinsky"]):
+    elif any(
+        x in model_lower
+        for x in ["flux", "diffusion", "stable-diffusion", "sdxl", "kandinsky"]
+    ):
         category = "image_gen"
-    elif any(x in model_lower for x in ["pix2pix", "inpaint", "controlnet", "photomaker", "instantid"]):
+    elif any(
+        x in model_lower
+        for x in ["pix2pix", "inpaint", "controlnet", "photomaker", "instantid"]
+    ):
         category = "image_edit"
-    elif any(x in model_lower for x in ["math", "teacher", "education", "translate", "wizard"]) and "coder" not in model_lower:
+    elif (
+        any(
+            x in model_lower
+            for x in ["math", "teacher", "education", "translate", "wizard"]
+        )
+        and "coder" not in model_lower
+    ):
         category = "education"
-    elif any(x in model_lower for x in ["tts", "speech", "audio", "whisper", "wav2vec", "bark"]):
+    elif any(
+        x in model_lower
+        for x in ["tts", "speech", "audio", "whisper", "wav2vec", "bark"]
+    ):
         category = "audio"
-    elif any(x in model_lower for x in ["face", "avatar", "talking", "wav2lip", "vl", "blip", "vision", "llava"]):
+    elif any(
+        x in model_lower
+        for x in [
+            "face",
+            "avatar",
+            "talking",
+            "wav2lip",
+            "vl",
+            "blip",
+            "vision",
+            "llava",
+        ]
+    ):
         category = "multimodal"
 
     try:
@@ -472,14 +512,20 @@ def use_ai_model(model_name, input_text, user_session="guest"):
             response += f"📸 Prompt: '{input_text}'\n\n"
             response += f"ℹ️ Image generation models require special handling. "
             response += f"The model '{model_name}' will create an image based on your prompt.\n\n"
-            response += f"💡 To view the generated image, use the Image Generation interface."
+            response += (
+                f"💡 To view the generated image, use the Image Generation interface."
+            )
             return response
 
         elif category == "audio":
             response = f"🎵 {model_name} audio processing...\n\n"
             response += f"Input: '{input_text}'\n\n"
-            response += f"ℹ️ Audio models require audio file input or special parameters. "
-            response += f"Please use the Audio Processing interface for full functionality."
+            response += (
+                f"ℹ️ Audio models require audio file input or special parameters. "
+            )
+            response += (
+                f"Please use the Audio Processing interface for full functionality."
+            )
             return response
 
         else:
@@ -487,25 +533,53 @@ def use_ai_model(model_name, input_text, user_session="guest"):
             messages = []
 
             if category == "software_engineer":
-                messages.append({"role": "system", "content": "You are an expert software engineer. Provide production-ready code with best practices, error handling, and clear documentation."})
+                messages.append(
+                    {
+                        "role": "system",
+                        "content": "You are an expert software engineer. Provide production-ready code with best practices, error handling, and clear documentation.",
+                    }
+                )
             elif category == "education":
-                messages.append({"role": "system", "content": "You are an expert AI teacher. Provide clear, step-by-step explanations with examples to help students understand."})
+                messages.append(
+                    {
+                        "role": "system",
+                        "content": "You are an expert AI teacher. Provide clear, step-by-step explanations with examples to help students understand.",
+                    }
+                )
             elif category == "multimodal":
-                messages.append({"role": "system", "content": "You are a multimodal AI assistant capable of understanding and describing visual content and complex queries."})
+                messages.append(
+                    {
+                        "role": "system",
+                        "content": "You are a multimodal AI assistant capable of understanding and describing visual content and complex queries.",
+                    }
+                )
 
             messages.append({"role": "user", "content": input_text})
 
             # Call HuggingFace Inference API
             full_response = ""
             try:
-                for message in inference_client.chat_completion(model=model_name, messages=messages, max_tokens=2000, temperature=0.7, stream=True):
+                for message in inference_client.chat_completion(
+                    model=model_name,
+                    messages=messages,
+                    max_tokens=2000,
+                    temperature=0.7,
+                    stream=True,
+                ):
                     if message.choices and message.choices[0].delta.content:
                         full_response += message.choices[0].delta.content
 
                 if not full_response:
-                    full_response = "Model response was empty. Try rephrasing your prompt."
+                    full_response = (
+                        "Model response was empty. Try rephrasing your prompt."
+                    )
 
-                icons = {"software_engineer": "💻", "education": "🎓", "multimodal": "🤖", "text": "🧠"}
+                icons = {
+                    "software_engineer": "💻",
+                    "education": "🎓",
+                    "multimodal": "🤖",
+                    "text": "🧠",
+                }
                 icon = icons.get(category, "✨")
 
                 return f"{icon} **{model_name}**\n\n{full_response}"
@@ -538,14 +612,20 @@ def get_cloudflare_status():
         services.append("⚙️ R2 Storage (Configure CLOUDFLARE_R2_BUCKET_NAME)")
 
     if CLOUDFLARE_CONFIG["kv_namespace_id"]:
-        services.append("✅ KV Cache Connected")
+        services.append("✅ KV Sessions Connected")
     else:
-        services.append("⚙️ KV Cache (Configure CLOUDFLARE_KV_NAMESPACE_ID)")
+        services.append("⚙️ KV Sessions (Configure CLOUDFLARE_KV_NAMESPACE_ID)")
 
-    if CLOUDFLARE_CONFIG["durable_objects_id"]:
-        services.append("✅ Durable Objects Connected")
+    if CLOUDFLARE_CONFIG["kv_namespace_cache"]:
+        services.append("✅ KV Cache Connected")
     else:
-        services.append("⚙️ Durable Objects (Configure CLOUDFLARE_DURABLE_OBJECTS_ID)")
+        services.append("⚙️ KV Cache (Configure CLOUDFLARE_KV_CACHE_ID)")
+
+    if CLOUDFLARE_CONFIG["durable_objects_sessions"]:
+        services.append("✅ Durable Objects (Agent Sessions)")
+    
+    if CLOUDFLARE_CONFIG["durable_objects_chatrooms"]:
+        services.append("✅ Durable Objects (Chat Rooms)")
 
     return "\n".join(services)