Finalize th rest of the files

app.py

@@ -372,8 +372,7 @@ def create_agent2robot_interface():
                 
                 with gr.Row():
                     current_design_specs_output = gr.JSON(
-                        label="⚙️ Current Design Specs Being Tested",
-                        interactive=False
+                        label="⚙️ Current Design Specs Being Tested"
                     )
                     
                     progress_bar_output = gr.Slider(

llm_interface_enhanced.py

@@ -1,23 +1,28 @@
 import json
 import re
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import torch
+# from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+# import torch
 
 # Initialize the LLM pipeline (using a free model from Hugging Face)
-model_name = "microsoft/DialoGPT-medium"  # Fallback to a smaller model if needed
-try:
-    # Try to use a more capable model if available
-    model_name = "microsoft/DialoGPT-large"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    # Temporarily disable LLM pipeline to use improved fallback logic
-    llm_pipeline = None  # pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
-except:
-    # Fallback to a simpler approach
-    try:
-        llm_pipeline = None  # pipeline("text-generation", model="gpt2", device=0 if torch.cuda.is_available() else -1)
-    except:
-        llm_pipeline = None
+# Commented out to avoid large model downloads during Space startup
+# model_name = "microsoft/DialoGPT-medium"  # Fallback to a smaller model if needed
+# try:
+#     # Try to use a more capable model if available
+#     model_name = "microsoft/DialoGPT-large"
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     model = AutoModelForCausalLM.from_pretrained(model_name)
+#     # Temporarily disable LLM pipeline to use improved fallback logic
+#     llm_pipeline = None  # pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+# except:
+#     # Fallback to a simpler approach
+#     try:
+#         llm_pipeline = None  # pipeline("text-generation", model="gpt2", device=0 if torch.cuda.is_available() else -1)
+#     except:
+#         llm_pipeline = None
+
+# Disable local model loading to prevent large downloads during Space startup
+# Using fallback logic instead for demonstration purposes
+llm_pipeline = None
 
 def generate_initial_robot_design_prompt():
     """Generate the initial prompt for LLM robot design"""

requirements.txt

@@ -1,8 +1,8 @@
 pybullet>=3.2.5
-gradio>=4.0.0
+gradio==4.44.1
 imageio>=2.20.0
-transformers>=4.21.0
-torch>=1.12.0
+# transformers>=4.21.0
+# torch>=1.12.0
 Pillow>=9.0.0
 numpy>=1.21.0
 requests>=2.28.0