add daggr support

backend_api.py

@@ -45,6 +45,7 @@ try:
         get_gradio_system_prompt,  # Import the function to get dynamic prompt
         get_comfyui_system_prompt,  # Import the function to get dynamic ComfyUI prompt
         JSON_SYSTEM_PROMPT,
+        DAGGR_SYSTEM_PROMPT,
         GENERIC_SYSTEM_PROMPT
     )
     # Get the Gradio system prompt (includes full Gradio 6 documentation)
@@ -80,6 +81,7 @@ SYSTEM_PROMPT_CACHE = {
     "transformers.js": TRANSFORMERS_JS_SYSTEM_PROMPT,
     "react": REACT_SYSTEM_PROMPT,
     "comfyui": COMFYUI_SYSTEM_PROMPT,  # Use ComfyUI-specific prompt with documentation
+    "daggr": DAGGR_SYSTEM_PROMPT,
 }
 
 # Client connection pool for reuse (thread-safe)
@@ -114,7 +116,7 @@ AVAILABLE_MODELS = [
 MODEL_CACHE = {model["id"]: model for model in AVAILABLE_MODELS}
 print(f"[Startup] ✅ Performance optimizations loaded: {len(SYSTEM_PROMPT_CACHE)} cached prompts, {len(MODEL_CACHE)} cached models, client pooling enabled")
 
-LANGUAGE_CHOICES = ["html", "gradio", "transformers.js", "streamlit", "comfyui", "react"]
+LANGUAGE_CHOICES = ["html", "gradio", "transformers.js", "streamlit", "comfyui", "react", "daggr"]
 
 app = FastAPI(title="AnyCoder API", version="1.0.0")
 
@@ -710,7 +712,7 @@ def cleanup_generated_code(code: str, language: str) -> str:
                     code = code[:last_tag + 7].strip()
         
         # For Python: remove text after the last function/class definition or code block
-        elif language in ["gradio", "streamlit"]:
+        elif language in ["gradio", "streamlit", "daggr"]:
             # Find the last line that looks like actual code (not comments or blank)
             lines = code.split('\n')
             last_code_line = -1

backend_deploy.py

@@ -278,8 +278,8 @@ def is_streamlit_code(code: str) -> bool:
 
 
 def is_gradio_code(code: str) -> bool:
-    """Check if code is Gradio"""
-    return 'import gradio' in code or 'gr.' in code
+    """Check if code is Gradio or Daggr"""
+    return 'import gradio' in code or 'gr.' in code or 'import daggr' in code or 'from daggr' in code
 
 
 def detect_sdk_from_code(code: str, language: str) -> str:
@@ -294,7 +294,7 @@ def detect_sdk_from_code(code: str, language: str) -> str:
         return "docker"
     elif language == "streamlit" or is_streamlit_code(code):
         return "docker"
-    elif language == "gradio" or is_gradio_code(code):
+    elif language == "gradio" or language == "daggr" or is_gradio_code(code):
         return "gradio"
     else:
         return "gradio"  # Default
@@ -673,7 +673,7 @@ def deploy_to_huggingface_space(
                 # Fall through to normal React deployment below
         
         # For Gradio space updates (import/redesign), update .py files and upload all new files
-        if is_update and language == "gradio":
+        if is_update and language in ["gradio", "daggr"]:
             print(f"[Deploy] Gradio space update - updating .py files and uploading any new files")
             
             # Parse the code to get all files

backend_parsers.py

@@ -397,6 +397,7 @@ Instructions:
   * streamlit often needs: pandas, numpy, requests
   * opencv-python often needs: numpy, pillow
   * fastapi often needs: uvicorn, pydantic
+  * daggr often needs: daggr, gradio>=6.0, pydub
   * torch often needs: torchvision, torchaudio (if doing computer vision/audio)
 - Include packages for common file formats if relevant (openpyxl, python-docx, PyPDF2)
 - Do not include Python built-in modules

backend_prompts.py

@@ -537,6 +537,97 @@ IMPORTANT: Include "Built with anycoder - https://huggingface.co/spaces/akhaliq/
 """
 
 
+# Daggr system prompt - for building DAG-based AI workflows
+DAGGR_SYSTEM_PROMPT = """You are an expert Daggr developer. Create a complete, working Daggr workflow application based on the user's request. 
+
+`daggr` is a Python library for building AI workflows that connect Gradio apps, ML models, and custom Python functions. It automatically generates a visual canvas for inspecting intermediate outputs and preserves state.
+
+## Core Concepts
+- **Nodes**: Computation units (GradioSpace, Inference call, or Python function).
+- **Ports**: Input and Output data flows between nodes.
+- **Graph**: The container for all nodes.
+
+## Node Types
+### 1. `GradioNode`
+Calls a Gradio Space API endpoint.
+```python
+from daggr import GradioNode
+import gradio as gr
+
+image_gen = GradioNode(
+    space_or_url="black-forest-labs/FLUX.1-schnell",
+    api_name="/infer",
+    inputs={
+        "prompt": gr.Textbox(label="Prompt"),
+        "seed": 42,
+        "width": 1024,
+        "height": 1024,
+    },
+    outputs={
+        "image": gr.Image(label="Generated Image"),
+    },
+)
+```
+
+### 2. `InferenceNode`
+Calls a model via Hugging Face Inference Providers.
+```python
+from daggr import InferenceNode
+import gradio as gr
+
+llm = InferenceNode(
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    inputs={"prompt": gr.Textbox(label="Prompt")},
+    outputs={"response": gr.Textbox(label="Response")},
+)
+```
+
+### 3. `FnNode`
+Runs a Python function. Input ports discovered from signature.
+```python
+from daggr import FnNode
+import gradio as gr
+
+def summarize(text: str) -> str:
+    return text[:100] + "..."
+
+summarizer = FnNode(
+    fn=summarize,
+    inputs={"text": gr.Textbox(label="Input")},
+    outputs={"summary": gr.Textbox(label="Summary")},
+)
+```
+
+## Advanced Features
+- **Scatter/Gather**: Use `.each` to scatter a list output and `.all()` to gather.
+- **Choice Nodes**: Use `|` to offer alternatives (e.g., `node_v1 | node_v2`).
+- **Postprocessing**: Use `postprocess=lambda original, target: target` in `GradioNode` or `InferenceNode` to extract specific outputs.
+
+## Deployment & Hosting
+Daggr apps launch with `graph.launch()`. For deployment to Spaces, they act like standard Gradio apps.
+
+## Requirements:
+1. ALWAYS generate a complete `app.py` and `requirements.txt` (via imports).
+2. Organize workflow logically with clear node names.
+3. Use `GradioNode` or `InferenceNode` when possible for parallel execution.
+4. Always include "Built with anycoder" in the header.
+
+=== app.py ===
+import gradio as gr
+from daggr import GradioNode, FnNode, InferenceNode, Graph
+
+# Define nodes...
+# ...
+
+graph = Graph(name="My Workflow", nodes=[node1, node2])
+graph.launch()
+
+**🚨 CRITICAL: DO NOT Generate README.md Files**
+- NEVER generate README.md files under any circumstances
+- A template README.md is automatically provided and will be overridden by the deployment system
+"""
+
+
 GENERIC_SYSTEM_PROMPT = """You are an expert {language} developer. Write clean, idiomatic, and runnable {language} code for the user's request. If possible, include comments and best practices. Generate complete, working code that can be run immediately. If the user provides a file or other context, use it as a reference. If the code is for a script or app, make it as self-contained as possible.
 
 **🚨 CRITICAL: DO NOT Generate README.md Files**

frontend/src/components/ControlPanel.tsx

@@ -84,6 +84,7 @@ export default function ControlPanel({
     if (lang === 'html') return 'HTML';
     if (lang === 'transformers.js') return 'Transformers.js';
     if (lang === 'comfyui') return 'ComfyUI';
+    if (lang === 'daggr') return 'Daggr';
     return lang.charAt(0).toUpperCase() + lang.slice(1);
   };
 

frontend/src/components/LandingPage.tsx

@@ -277,6 +277,7 @@ export default function LandingPage({
     if (lang === 'html') return 'HTML';
     if (lang === 'transformers.js') return 'Transformers.js';
     if (lang === 'comfyui') return 'ComfyUI';
+    if (lang === 'daggr') return 'Daggr';
     return lang.charAt(0).toUpperCase() + lang.slice(1);
   };
 

frontend/src/types/index.ts

@@ -54,7 +54,7 @@ export interface DeploymentRequest {
   requirements?: string;
   existing_repo_id?: string;  // For updating existing spaces
   commit_message?: string;
-  history?: Array<{role: string; content: string}>;  // Chat history for tracking
+  history?: Array<{ role: string; content: string }>;  // Chat history for tracking
 }
 
 export interface DeploymentResponse {
@@ -65,5 +65,5 @@ export interface DeploymentResponse {
   repo_id?: string;
 }
 
-export type Language = 'html' | 'gradio' | 'transformers.js' | 'streamlit' | 'comfyui' | 'react';
+export type Language = 'html' | 'gradio' | 'transformers.js' | 'streamlit' | 'comfyui' | 'react' | 'daggr';