added autocad

app.py

@@ -1,70 +1,101 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+import json
+import logging
 
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+# Define model and checkpoint paths
+MODEL_PATH = "microsoft/CADFusion/v1_1"
+CHECKPOINT_PATH = "./exp/model_ckpt/v1_1"
 
-    messages = [{"role": "system", "content": system_message}]
+# Load model and tokenizer
+try:
+    logger.info("Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+    logger.info("Loading model...")
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    logger.info("Model and tokenizer loaded successfully.")
+except Exception as e:
+    logger.error(f"Error loading model or tokenizer: {e}")
+    raise e
 
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# Function to generate CAD model from text description
+def generate_cad_model(text_description):
+    try:
+        # Tokenize input
+        inputs = tokenizer(text_description, return_tensors="pt").to(model.device)
+        
+        # Generate output
+        outputs = model.generate(
+            **inputs,
+            max_length=512,
+            num_return_sequences=1,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+        
+        # Decode output
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Parse generated text to extract CAD model data (assuming JSON-like output)
+        try:
+            cad_data = json.loads(generated_text)
+            return json.dumps(cad_data, indent=2)
+        except json.JSONDecodeError:
+            return generated_text  # Return raw text if JSON parsing fails
+    except Exception as e:
+        logger.error(f"Error during generation: {e}")
+        return f"Error: {str(e)}"
 
+# Gradio interface
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# CADFusion: Text-to-CAD Generation")
+        gr.Markdown("Enter a textual description of the CAD model you want to generate. For example: 'A 3D model of a chair with four legs and a curved backrest.'")
+        
+        with gr.Row():
+            with gr.Column():
+                text_input = gr.Textbox(
+                    label="Text Description",
+                    placeholder="Enter your CAD model description here...",
+                    lines=5
+                )
+                submit_button = gr.Button("Generate CAD Model")
+            
+            with gr.Column():
+                output_text = gr.Textbox(
+                    label="Generated CAD Model (JSON or Text)",
+                    placeholder="Generated output will appear here...",
+                    lines=10
+                )
+        
+        submit_button.click(
+            fn=generate_cad_model,
+            inputs=text_input,
+            outputs=output_text
+        )
+        
+        gr.Markdown("""
+        **Note**: 
+        - CADFusion is for research purposes only. Generated models may not be technically accurate and require validation.
+        - Ensure descriptions are clear and specific for best results.
+        - For more details, visit the [CADFusion GitHub repo](https://github.com/microsoft/CADFusion).
+        """)
+    
+    return demo
 
+# Launch Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo = create_gradio_interface()
+    demo.launch()