fix daggr version

backend_deploy.py

@@ -880,6 +880,8 @@ def deploy_to_huggingface_space(
                             (temp_path / "requirements.txt").write_text("gradio>=4.0.0\n", encoding='utf-8')
                         elif language == "streamlit":
                             (temp_path / "requirements.txt").write_text("streamlit>=1.30.0\n", encoding='utf-8')
+                        elif language == "daggr":
+                            (temp_path / "requirements.txt").write_text("daggr>=0.5.4\ngradio>=6.0.2\n", encoding='utf-8')
                 
                 # Create Dockerfile if needed
                 if sdk == "docker":
@@ -938,7 +940,10 @@ def deploy_to_huggingface_space(
                         print(f"[Deploy] Generated requirements.txt with {len(requirements_content.splitlines())} lines")
                     else:
                         # Fallback to minimal requirements if no app file found
-                        (temp_path / "requirements.txt").write_text("gradio>=4.0.0\n", encoding='utf-8')
+                        if language == "daggr":
+                            (temp_path / "requirements.txt").write_text("daggr>=0.5.4\ngradio>=6.0.2\n", encoding='utf-8')
+                        else:
+                            (temp_path / "requirements.txt").write_text("gradio>=4.0.0\n", encoding='utf-8')
             
             # Don't create README - HuggingFace will auto-generate it
             # We'll add the anycoder tag after deployment

backend_parsers.py

@@ -387,6 +387,7 @@ Instructions:
 - IMPORTANT: For diffusers, ALWAYS use: git+https://github.com/huggingface/diffusers
 - IMPORTANT: For transformers, ALWAYS use: git+https://github.com/huggingface/transformers
 - IMPORTANT: If diffusers is installed, also include transformers and sentencepiece as they usually go together
+- IMPORTANT: For daggr, ALWAYS use: daggr>=0.5.4
 - Examples of comprehensive dependencies:
   * diffusers often needs: git+https://github.com/huggingface/transformers, sentencepiece, accelerate, torch, tokenizers
   * transformers often needs: accelerate, torch, tokenizers, datasets
@@ -397,7 +398,7 @@ 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
+  * daggr often needs: daggr>=0.5.4, 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

@@ -607,7 +607,7 @@ summarizer = FnNode(
 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).
+1. ALWAYS generate a complete `app.py` and `requirements.txt` (via imports). In `requirements.txt`, ALWAYS use `daggr>=0.5.4` and `gradio>=6.0.2`.
 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.