Version 1.9RC

app.py

@@ -1,5 +1,9 @@
 import os
 import sys
+from dotenv import load_dotenv
+
+# Charge les variables d'environnement depuis le fichier .env
+load_dotenv()
 
 # Ajoute le dossier courant au path pour pouvoir importer src
 sys.path.append(os.path.dirname(__file__))

src/core/builder/proposal_generator.py

@@ -23,7 +23,12 @@ class ProposalGenerator:
             
         print(f"🤖 Appel LLM avec Modèle: {model}, Provider: {provider}")
         
-        client = InferenceClient(model=model, token=self.token, provider=provider)
+        # Use current environment variable if available (supports UI updates), otherwise fallback to init token
+        current_token = os.environ.get("HF_TOKEN", self.token)
+        
+        client = InferenceClient(model=model, token=current_token, provider=provider)
+        
+        # print(self.token) # Avoid printing token in logs
         
         messages = [
             {

src/mcp_server/server.py

@@ -15,14 +15,11 @@ from src.core.builder.proposal_generator import proposal_generator
 # Modèles simplifiés et performants pour le code
 COMMON_MODELS = [
     "openai/gpt-oss-120b",
-    "moonshotai/Kimi-K2-Instruct-0905",
-    "Qwen/Qwen3-Coder-30B-A3B-Instruct",
-    "Qwen/Qwen2.5-Coder-32B-Instruct", # Backup éprouvé
+    "moonshotai/Kimi-K2-Instruct-0905"
 ]
 
 PROVIDER_MODELS = {
     "together": COMMON_MODELS,
-    "sambanova": COMMON_MODELS,
     "hyperbolic": COMMON_MODELS,
     "None": COMMON_MODELS,
     # Fallback pour les autres
@@ -34,7 +31,9 @@ PROVIDER_MODELS = {
 
 def step_1_initialisation_and_proposal(project_name, description, model_id, provider_id):
     """
-    STEP 1: Starts a new tool project and uses AI to propose code.
+    STEP 1: Starts a new tool project and uses AI to propose draft code.
+    
+    Call this AFTER `step_0...`. It initializes the project and sets the optional HF_TOKEN.
     
     This is the entry point for creating a new MCP tool. It returns a draft_id and a code proposal based on the description.
     
@@ -42,7 +41,7 @@ def step_1_initialisation_and_proposal(project_name, description, model_id, prov
         project_name: The technical name of the tool (e.g., 'weather-fetcher').
         description: A natural language description of what the tool should do, or a raw Swagger/OpenAPI JSON specification.
         model_id: The LLM model to use for code generation (default: Qwen/Qwen2.5-Coder-32B-Instruct).
-        provider_id: The inference provider to use. Options: 'together', 'fal-ai', 'replicate', 'sambanova', 'hyperbolic', or None (auto).
+        provider_id: The inference provider to use. Options: 'together', 'hyperbolic'.
     """
     # 1. Initialisation du projet (type 'adhoc' par défaut)
     init_result = tools.init_project(project_name, description, type="adhoc")
@@ -117,13 +116,62 @@ def step_3_deployment(draft_id):
     # Simplification: Toujours public, toujours new (écrase/crée), nom du space = nom du projet
     result = tools.deploy_to_space(draft_id, visibility="public", space_target="new", target_space_name=None)
     
+    status_msg = ""
+    space_url_val = ""
+    mcp_url_val = ""
+    claude_config_val = ""
+    
     if "error" not in result:
-        gr.Info(f"Déploiement réussi ! URL : {result.get('url')}")
+        space_url_val = result.get('url', '')
+        gr.Info(f"Déploiement réussi ! URL : {space_url_val}")
+        
+        status_msg = "### 🚀 Déploiement réussi !"
+        
+        # Construction de l'URL MCP
+        mcp_url_val = space_url_val
+        tool_name = "my-tool"
+        
+        try:
+            if "huggingface.co/spaces/" in space_url_val:
+                parts = space_url_val.split("huggingface.co/spaces/")
+                if len(parts) > 1:
+                    path = parts[1].strip("/")
+                    if "/" in path:
+                        user, space = path.split("/", 1)
+                        tool_name = space
+                        # Format direct url : https://user-space.hf.space
+                        # Note: pour mcp-remote on utilise le endpoint /gradio_api/mcp/
+                        mcp_url_val = f"https://{user}-{space}.hf.space/gradio_api/mcp/"
+        except Exception:
+            pass
+            
+        # Construction de la config Claude
+        config_dict = {
+            "mcpServers": {
+                tool_name: {
+                    "command": "npx",
+                    "args": [
+                        "mcp-remote",
+                        mcp_url_val,
+                        "--transport",
+                        "streamable-http"
+                    ]
+                }
+            }
+        }
+        claude_config_val = json.dumps(config_dict, indent=2)
+
     else:
         gr.Info(f"Échec du déploiement : {result.get('error')}")
+        status_msg = f"### ❌ Échec du déploiement\n\nErreur : {result.get('error')}"
     
-    # Retourne une string JSON pour compatibilité avec gr.Code
-    return json.dumps(result, indent=2)
+    # Retourne : 
+    # 1. JSON result (pour out_deploy)
+    # 2. Markdown status
+    # 3. Space URL
+    # 4. MCP URL
+    # 5. Claude Config Code
+    return json.dumps(result, indent=2), status_msg, space_url_val, mcp_url_val, claude_config_val
 
 # Récupération des handlers du playground
 reload_tools_handler, chat_response_handler = get_playground_ui_handlers()
@@ -135,7 +183,7 @@ def step_0_configuration(hf_user: str = None, hf_token: str = None, default_spac
     """
     STEP 0: Configures the Meta-MCP server environment.
     
-    This step is optional but recommended to set up the Hugging Face environment.
+    This step is needed to set up the Hugging Face environment.
     
     Args:
         hf_user: The Hugging Face username or organization (namespace) where Spaces will be deployed.
@@ -220,8 +268,8 @@ def util_get_tool_code(space_name: str, tool_name: str):
 
 # --- Construction de l'interface ---
 
-with gr.Blocks(title="Meta-MCP Fractal") as demo:
-    gr.Markdown("# 🏭 Méta-MCP Fractal Factory")
+with gr.Blocks(title="MCEPTION") as demo:
+    gr.Markdown("# 🏭 MCEPTION is the MCP of your MCPs")
     gr.Markdown("Ce serveur permet de créer et déployer d'autres serveurs MCP sur Hugging Face Spaces.")
 
     with gr.Tab("0. Setup & How-to"):
@@ -286,7 +334,7 @@ with gr.Blocks(title="Meta-MCP Fractal") as demo:
     with gr.Tab("1. Initialisation"):
         gr.Markdown("Commencez par initialiser un nouveau projet.")
         
-        project_name = gr.Textbox(label="Nom du projet (ex: strawberry-counter, ratp-api-client)")
+        project_name = gr.Textbox(label="ex: Nom du projet (ex: strawberry-counter, town-weather)...")
         
         project_desc = gr.Textbox(
             label="Description de l'outil ou Spécification (Swagger/OpenAPI JSON)",
@@ -317,20 +365,30 @@ with gr.Blocks(title="Meta-MCP Fractal") as demo:
 
             provider_id.change(update_models, inputs=[provider_id], outputs=[model_id])
 
-        btn_init = gr.Button("Initialiser le projet & Générer le code (IA)")
+        btn_init = gr.Button("Initialiser le projet & proposer le code (IA)")
         out_init = gr.JSON(label="Résultat (Copiez le draft_id)")
         
 
     with gr.Tab("2. Précision de la logique"):
         gr.Markdown("Vérifiez et précisez le code Python et l'interface de votre outil.")
-        with gr.Row():
-            draft_id_logic = gr.Textbox(label="Draft ID")
-            python_code = gr.Code(language="python", label="Code Python (ex: def count_r(word): ...)")
+        
+        # Afficher le rappel du draft_id en lecture seule pour assurer la propagation
+        draft_id_logic = gr.Textbox(label="Draft ID", interactive=False)
         
         with gr.Row():
-            # MODIFICATION: Utilisation de gr.Code au lieu de gr.JSON pour une meilleure compatibilité type string
-            inputs_dict = gr.Code(language="json", label="Inputs (JSON)", value='{"word": "text"}')
-            with gr.Column():
+            # Colonne de gauche : Code
+            with gr.Column(scale=2):
+                python_code = gr.Code(language="python", label="Code Python (ex: def count_r(word): ...)")
+            
+            # Colonne de droite : Requirements, Inputs, Outputs
+            with gr.Column(scale=1):
+                # 1. Requirements
+                requirements_box = gr.Code(language="json", label="Requirements (JSON List)", value='[]')
+                
+                # 2. Inputs
+                inputs_dict = gr.Code(language="json", label="Inputs (JSON)", value='{"word": "text"}')
+                
+                # 3. Outputs
                 output_desc = gr.Textbox(label="Description de la sortie")
                 output_component_ui = gr.Dropdown(
                     label="Type de sortie (Composant Gradio)", 
@@ -338,11 +396,8 @@ with gr.Blocks(title="Meta-MCP Fractal") as demo:
                     value="text",
                     interactive=True
                 )
-        
-        # MODIFICATION: Utilisation de gr.Code
-        requirements_box = gr.Code(language="json", label="Requirements (JSON List)", value='[]')
             
-        btn_logic = gr.Button("Générer le code")
+        btn_logic = gr.Button("Valider le code")
         out_logic = gr.JSON(label="Résultat")
             
         btn_logic.click(
@@ -382,44 +437,27 @@ with gr.Blocks(title="Meta-MCP Fractal") as demo:
             """
 
         btn_deploy = gr.Button("Déployer sur Spaces", variant="primary")
-        out_deploy = gr.Code(language="json", label="Résultat du déploiement")
+        
+        out_status = gr.Markdown("")
+        
+        with gr.Row():
+            # Utilisation de gr.Code car gr.Textbox(show_copy_button=True) n'est pas supporté dans cette version de Gradio
+            out_space_url = gr.Code(language=None, label="URL du Space Hugging Face", interactive=False, lines=1)
+            out_mcp_url = gr.Code(language=None, label="URL du Point d'accès MCP", interactive=False, lines=1)
+            
+        out_claude_config = gr.Code(language="json", label="Configuration Claude Desktop (à ajouter à claude_desktop_config.json)")
+        
+        with gr.Accordion("Détails JSON (Debug)", open=False):
+            out_deploy = gr.Code(language="json", label="Résultat Brut")
         
         # Mise à jour du résumé quand le draft_id change
         draft_id_deploy.change(update_deployment_summary, inputs=[draft_id_deploy], outputs=[deployment_summary])
         
         # Fonction pour extraire l'URL MCP directe et préremplir le playground
-        def auto_fill_playground(deploy_result_str: str):
-            if not deploy_result_str:
-                return gr.update()
-                
-            try:
-                deploy_result = json.loads(deploy_result_str)
-            except:
-                return gr.update()
-
-            if "url" not in deploy_result:
+        def auto_fill_playground(mcp_url_val: str):
+            if not mcp_url_val:
                 return gr.update()
-            
-            # L'URL retournée est de la forme https://huggingface.co/spaces/USER/SPACE
-            # On veut https://USER-SPACE.hf.space/gradio_api/mcp/
-            hf_url = deploy_result["url"]
-            
-            try:
-                # Extraction user et space
-                if "huggingface.co/spaces/" in hf_url:
-                    parts = hf_url.split("huggingface.co/spaces/")
-                    if len(parts) > 1:
-                        path = parts[1].strip("/")
-                        if "/" in path:
-                            user, space = path.split("/", 1)
-                            # Format direct url : https://user-space.hf.space
-                            direct_url = f"https://{user}-{space}.hf.space/gradio_api/mcp/"
-                            return direct_url
-            except:
-                pass
-            
-            # Fallback si parsing échoue
-            return hf_url
+            return mcp_url_val
 
     # Câblage global des événements (une fois tous les composants définis)
     # 1. Init -> Remplissage auto de l'onglet 2 (Logic) et copie de l'ID vers onglet 3 (Deploy)
@@ -480,11 +518,11 @@ with gr.Blocks(title="Meta-MCP Fractal") as demo:
     btn_deploy.click(
         step_3_deployment, 
         inputs=[draft_id_deploy], 
-        outputs=out_deploy,
+        outputs=[out_deploy, out_status, out_space_url, out_mcp_url, out_claude_config],
         api_name="step_3_deployment"
     ).then(
         fn=auto_fill_playground,
-        inputs=[out_deploy],
+        inputs=[out_mcp_url],
         outputs=[mcp_url_input]
     )