fixed bugs

app/config.py

@@ -1,5 +1,6 @@
 from pydantic_settings import BaseSettings
-from typing import Optional, List
+from typing import List, Optional
+import os
 
 
 class Settings(BaseSettings):
@@ -52,12 +53,121 @@ class Settings(BaseSettings):
 
 settings = Settings()
 
-# Plan limits
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# MODEL CATALOG — Single source of truth for all model metadata
+# ═══════════════════════════════════════════════════════════════════════════════
+# To add a new model:
+#   1. Add it here with name/provider/badge/description
+#   2. Add its model_id → provider mapping in MODEL_PROVIDERS
+#   3. Add it to the appropriate plan(s) in PLAN_LIMITS
+# That's it — the frontend loads everything from GET /api/v1/models/available
+# ═══════════════════════════════════════════════════════════════════════════════
+
+MODEL_CATALOG = {
+    # ── Free tier (Fireworks - lightweight) ────────────────────────────────────
+    "accounts/fireworks/models/kimi-k2-instruct-0905": {
+        "name": "Kimi K2",
+        "provider": "Fireworks AI",
+        "badge": "Free",
+        "description": "Free model for building and testing chatbots",
+    },
+
+    # ── Starter tier (Fireworks - powerful) ────────────────────────────────────
+    "accounts/fireworks/models/llama-v3p1-70b-instruct": {
+        "name": "Llama 3.1 70B",
+        "provider": "Fireworks AI",
+        "badge": "Fast",
+        "description": "Fast open-source model, great for most tasks",
+    },
+    "accounts/fireworks/models/mixtral-8x7b-instruct": {
+        "name": "Mixtral 8x7B",
+        "provider": "Fireworks AI",
+        "badge": "Balanced",
+        "description": "Balanced speed and quality",
+    },
+    "accounts/fireworks/models/qwen2p5-72b-instruct": {
+        "name": "Qwen 2.5 72B",
+        "provider": "Fireworks AI",
+        "badge": "Multilingual",
+        "description": "Excellent multilingual capabilities",
+    },
+
+    # ── Pro tier (Premium providers) ───────────────────────────────────────────
+    "gpt-4o": {
+        "name": "GPT-4o",
+        "provider": "OpenAI",
+        "badge": "Powerful",
+        "description": "Most capable OpenAI model",
+    },
+    "gpt-4-turbo": {
+        "name": "GPT-4 Turbo",
+        "provider": "OpenAI",
+        "badge": "Smart",
+        "description": "Fast and capable with large context",
+    },
+    "gpt-3.5-turbo": {
+        "name": "GPT-3.5 Turbo",
+        "provider": "OpenAI",
+        "badge": "Efficient",
+        "description": "Cost-effective for simpler tasks",
+    },
+    "claude-3-5-sonnet-20241022": {
+        "name": "Claude 3.5 Sonnet",
+        "provider": "Anthropic",
+        "badge": "Reasoning",
+        "description": "Excellent at analysis and reasoning",
+    },
+    "claude-3-opus-20240229": {
+        "name": "Claude 3 Opus",
+        "provider": "Anthropic",
+        "badge": "Advanced",
+        "description": "Most capable Anthropic model",
+    },
+    "gemini-1.5-pro": {
+        "name": "Gemini 1.5 Pro",
+        "provider": "Google",
+        "badge": "Long Context",
+        "description": "Handles very long documents well",
+    },
+}
+
+
+# ─── Model ID → LLM provider mapping (used by llm_client.py for routing) ─────
+
+MODEL_PROVIDERS = {
+    "accounts/fireworks/models/kimi-k2-instruct-0905": "fireworks",
+    "accounts/fireworks/models/llama-v3p1-70b-instruct": "fireworks",
+    "accounts/fireworks/models/mixtral-8x7b-instruct": "fireworks",
+    "accounts/fireworks/models/qwen2p5-72b-instruct": "fireworks",
+    "gpt-4o": "openai",
+    "gpt-4-turbo": "openai",
+    "gpt-3.5-turbo": "openai",
+    "claude-3-5-sonnet-20241022": "anthropic",
+    "claude-3-opus-20240229": "anthropic",
+    "gemini-1.5-pro": "google",
+}
+
+
+# ─── Default model per plan (pre-selected in the frontend) ────────────────────
+
+DEFAULT_MODELS = {
+    "free": "accounts/fireworks/models/kimi-k2-instruct-0905",
+    "starter": "accounts/fireworks/models/llama-v3p1-70b-instruct",
+    "pro": "gpt-4o",
+    "enterprise": "gpt-4o",
+}
+
+
+# ─── Plan limits ──────────────────────────────────────────────────────────────
+
 PLAN_LIMITS = {
     "free": {
         "max_chatbots": 999999,       # unlimited creation
         "max_published": 0,            # cannot publish
-        "models": [],
+        "models": [
+            "accounts/fireworks/models/kimi-k2-instruct-0905",
+        ],
         "conversations_limit": 999999,  # unlimited preview
         "code_export": False,
         "features": ["preview_mode", "testing"],
@@ -67,8 +177,9 @@ PLAN_LIMITS = {
         "max_published": 1,
         "models": [
             "accounts/fireworks/models/kimi-k2-instruct-0905",
-            "accounts/fireworks/models/deepseek-v3p2",
-            "accounts/fireworks/models/glm-4p7",
+            "accounts/fireworks/models/llama-v3p1-70b-instruct",
+            "accounts/fireworks/models/mixtral-8x7b-instruct",
+            "accounts/fireworks/models/qwen2p5-72b-instruct",
         ],
         "conversations_limit": 5000,
         "code_export": False,
@@ -79,7 +190,8 @@ PLAN_LIMITS = {
         "max_published": 3,
         "models": [
             "accounts/fireworks/models/kimi-k2-instruct-0905",
-            "accounts/fireworks/models/deepseek-v3p2",
+            "accounts/fireworks/models/llama-v3p1-70b-instruct",
+            "accounts/fireworks/models/mixtral-8x7b-instruct",
             "gpt-4o",
             "gpt-4-turbo",
             "gpt-3.5-turbo",
@@ -101,27 +213,9 @@ PLAN_LIMITS = {
     "enterprise": {
         "max_chatbots": 999999,
         "max_published": 999999,
-        "models": ["*"],
+        "models": ["*"],  # resolves to all MODEL_CATALOG keys
         "conversations_limit": 999999,
         "code_export": True,
         "features": ["*"],
     },
 }
-
-MODEL_PROVIDERS = {
-    "accounts/fireworks/models/kimi-k2-instruct-0905": "fireworks",
-    "accounts/fireworks/models/deepseek-v3p2": "fireworks",
-    "accounts/fireworks/models/glm-4p7": "fireworks",
-    "gpt-4o": "openai",
-    "gpt-4-turbo": "openai",
-    "gpt-3.5-turbo": "openai",
-    "claude-3-5-sonnet-20241022": "anthropic",
-    "claude-3-opus-20240229": "anthropic",
-    "gemini-1.5-pro": "google",
-}
-
-DEFAULT_MODELS = {
-    "starter": "accounts/fireworks/models/kimi-k2-instruct-0905",
-    "pro": "gpt-4o",
-    "enterprise": "gpt-4o",
-}

app/main.py

@@ -5,7 +5,7 @@ from fastapi.responses import JSONResponse
 import logging
 
 from app.config import settings
-from app.routers import auth, chatbots, documents, chat, marketplace, billing
+from app.routers import auth, chatbots, documents, chat, marketplace, billing, models
 
 # Configure logging
 logging.basicConfig(
@@ -53,6 +53,7 @@ app.include_router(documents.router, prefix="/api/v1")
 app.include_router(chat.router, prefix="/api/v1")
 app.include_router(marketplace.router, prefix="/api/v1")
 app.include_router(billing.router, prefix="/api/v1")
+app.include_router(models.router, prefix="/api/v1")
 
 # ── Health & Info ──────────────────────────────────────────────────────────────
 @app.get("/")

app/models.py

@@ -103,6 +103,7 @@ class ChatbotCreate(BaseModel):
     max_tokens: int = Field(default=1000, ge=100, le=8000)
     primary_color: str = "#6366f1"
     welcome_message: str = "Hello! How can I help you today?"
+    logo_url: Optional[str] = None
     category: Optional[str] = None
     industry: Optional[str] = None
     languages: List[str] = ["en"]
@@ -117,6 +118,7 @@ class ChatbotUpdate(BaseModel):
     max_tokens: Optional[int] = None
     primary_color: Optional[str] = None
     welcome_message: Optional[str] = None
+    logo_url: Optional[str] = None
     category: Optional[str] = None
     industry: Optional[str] = None
     languages: Optional[List[str]] = None
@@ -133,6 +135,7 @@ class ChatbotResponse(BaseModel):
     max_tokens: int
     primary_color: str
     welcome_message: str
+    logo_url: Optional[str] = None
     category: Optional[str] = None
     industry: Optional[str] = None
     languages: List[str]
@@ -156,6 +159,7 @@ class ChatbotPublicResponse(BaseModel):
     languages: List[str]
     primary_color: str
     welcome_message: str
+    logo_url: Optional[str] = None
     average_rating: Optional[float] = None
     total_conversations: int = 0
     company_name: Optional[str] = None

app/routers/chatbots.py

@@ -53,7 +53,6 @@ async def create_chatbot(data: ChatbotCreate, user=Depends(get_current_user)):
         vector_store.create_collection(collection_name)
     except Exception as e:
         logger.error(f"Failed to create Qdrant collection: {e}")
-        # Continue without vector store for now
         collection_name = None
 
     chatbot_data = {
@@ -67,6 +66,7 @@ async def create_chatbot(data: ChatbotCreate, user=Depends(get_current_user)):
         "max_tokens": data.max_tokens,
         "primary_color": data.primary_color,
         "welcome_message": data.welcome_message,
+        "logo_url": data.logo_url,
         "category": data.category,
         "industry": data.industry,
         "languages": data.languages,
@@ -227,6 +227,7 @@ def _format_chatbot(chatbot: dict, supabase) -> ChatbotResponse:
         max_tokens=chatbot.get("max_tokens", 1000),
         primary_color=chatbot.get("primary_color", "#6366f1"),
         welcome_message=chatbot.get("welcome_message", "Hello! How can I help?"),
+        logo_url=chatbot.get("logo_url"),
         category=chatbot.get("category"),
         industry=chatbot.get("industry"),
         languages=chatbot.get("languages", ["en"]),

app/routers/marketplace.py

@@ -60,6 +60,7 @@ async def list_marketplace_chatbots(
                 languages=c.get("languages", ["en"]),
                 primary_color=c.get("primary_color", "#6366f1"),
                 welcome_message=c.get("welcome_message", "Hello!"),
+                logo_url=c.get("logo_url"),
                 average_rating=c.get("average_rating"),
                 total_conversations=c.get("total_conversations", 0),
                 company_name=company_data.get("name"),
@@ -74,23 +75,27 @@ async def list_marketplace_chatbots(
         total=total,
         page=page,
         limit=limit,
-        has_more=(offset + limit) < total,
+        has_more=(offset + limit < total),
     )
 
 
 @router.get("/chatbots/{chatbot_id}", response_model=ChatbotPublicResponse)
-async def get_marketplace_chatbot(chatbot_id: str):
+async def get_marketplace_chatbot(chatbot_id: str, user=Depends(get_optional_user)):
     supabase = get_supabase()
-    result = supabase.table("chatbots").select("*, companies(name, logo_url)") \
-        .eq("id", chatbot_id) \
-        .eq("is_published", True) \
-        .execute()
+
+    result = supabase.table("chatbots").select(
+        "*, companies(name, logo_url)"
+    ).eq("id", chatbot_id).eq("is_published", True).execute()
 
     if not result.data:
-        raise HTTPException(status_code=404, detail="Chatbot not found in marketplace")
+        raise HTTPException(status_code=404, detail="Chatbot not found")
 
     c = result.data[0]
     company_data = c.get("companies") or {}
+
+    conv_count = supabase.table("conversations").select("id", count="exact") \
+        .eq("chatbot_id", chatbot_id).execute()
+
     return ChatbotPublicResponse(
         id=c["id"],
         name=c["name"],
@@ -100,8 +105,9 @@ async def get_marketplace_chatbot(chatbot_id: str):
         languages=c.get("languages", ["en"]),
         primary_color=c.get("primary_color", "#6366f1"),
         welcome_message=c.get("welcome_message", "Hello!"),
+        logo_url=c.get("logo_url"),
         average_rating=c.get("average_rating"),
-        total_conversations=c.get("total_conversations", 0),
+        total_conversations=conv_count.count or 0,
         company_name=company_data.get("name"),
         company_logo=company_data.get("logo_url"),
         created_at=c.get("created_at"),

app/routers/models.py

@@ -0,0 +1,107 @@
+"""
+Models router - serves available AI models based on user subscription plan.
+
+Single source of truth flow:
+  config.py (PLAN_LIMITS + MODEL_CATALOG) → this router → frontend
+
+To add/remove/rename a model, only edit config.py.
+"""
+from fastapi import APIRouter, Depends
+from app.dependencies import get_current_user
+from app.database import get_supabase
+from app.config import PLAN_LIMITS, MODEL_CATALOG, DEFAULT_MODELS
+from typing import List, Optional
+from pydantic import BaseModel
+import logging
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/models", tags=["Models"])
+
+
+# ─── Response Models ───────────────────────────────────────────────────────────
+
+class AvailableModel(BaseModel):
+    id: str
+    name: str
+    provider: str
+    badge: str
+    description: Optional[str] = None
+    is_default: bool = False
+
+
+class ModelsResponse(BaseModel):
+    models: List[AvailableModel]
+    plan: str
+    default_model: Optional[str] = None
+    has_premium_access: bool
+    upgrade_label: Optional[str] = None
+
+
+# ─── Helpers ───────────────────────────────────────────────────────────────────
+
+def _get_user_plan(user_id: str) -> str:
+    """Get user's current subscription plan."""
+    supabase = get_supabase()
+    result = supabase.table("subscriptions") \
+        .select("plan") \
+        .eq("user_id", user_id) \
+        .eq("status", "active") \
+        .execute()
+    return result.data[0]["plan"] if result.data else "free"
+
+
+# ─── Endpoint ─────────────────────────────────────────────────────────────────
+
+@router.get("/available", response_model=ModelsResponse)
+async def get_available_models(user=Depends(get_current_user)):
+    """
+    Returns the list of AI models the user can access based on their plan.
+    Frontend uses this to populate model selection dynamically.
+
+    - free: gets a default model for preview/testing
+    - starter: Fireworks AI models
+    - pro: Fireworks + OpenAI + Anthropic + Google
+    - enterprise: all models
+    """
+    plan = _get_user_plan(user.id)
+    plan_config = PLAN_LIMITS.get(plan, PLAN_LIMITS["free"])
+    allowed_model_ids = plan_config.get("models", [])
+
+    # Enterprise wildcard → resolve to all catalog models
+    if "*" in allowed_model_ids:
+        allowed_model_ids = list(MODEL_CATALOG.keys())
+
+    # Build model list from catalog metadata
+    default_model_id = DEFAULT_MODELS.get(plan)
+    models: List[AvailableModel] = []
+
+    for model_id in allowed_model_ids:
+        meta = MODEL_CATALOG.get(model_id)
+        if not meta:
+            logger.warning(f"Model '{model_id}' in PLAN_LIMITS[{plan}] but not in MODEL_CATALOG — skipping")
+            continue
+
+        models.append(AvailableModel(
+            id=model_id,
+            name=meta["name"],
+            provider=meta["provider"],
+            badge=meta["badge"],
+            description=meta.get("description"),
+            is_default=(model_id == default_model_id),
+        ))
+
+    # Determine upgrade messaging
+    has_premium = plan in ("pro", "enterprise")
+    upgrade_label = None
+    if plan == "free":
+        upgrade_label = "Upgrade to Starter for more models and publishing"
+    elif plan == "starter":
+        upgrade_label = "Upgrade to Pro for GPT-4o, Claude, Gemini"
+
+    return ModelsResponse(
+        models=models,
+        plan=plan,
+        default_model=default_model_id,
+        has_premium_access=has_premium,
+        upgrade_label=upgrade_label,
+    )