Update app.py

app.py

@@ -1,64 +1,89 @@
+# app.py
+
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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,
-    ):
-        token = message.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
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
+import re
+from dataclasses import dataclass
+from pathlib import Path
+import spaces
+
+@spaces.GPU
+@dataclass
+class SymbolicConfig:
+    repo_id: str = "AbstractPhil/bert-beatrix-2048"
+    symbolic_roles: list = (
+        "<subject>", "<subject1>", "<subject2>", "<pose>", "<emotion>",
+        "<surface>", "<lighting>", "<material>", "<accessory>", "<footwear>",
+        "<upper_body_clothing>", "<hair_style>", "<hair_length>", "<headwear>",
+        "<texture>", "<pattern>", "<grid>", "<zone>", "<offset>",
+        "<object_left>", "<object_right>", "<relation>", "<intent>", "<style>",
+        "<fabric>", "<jewelry>"
+    )
+
+config = SymbolicConfig()
+model_dir = snapshot_download(repo_id=config.repo_id)
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForMaskedLM.from_pretrained(model_dir).eval().cuda()
+
+
+MASK_TOKEN = tokenizer.mask_token or "[MASK]"
+
+def mask_and_predict(text: str, selected_roles: list[str]):
+    results = []
+    masked_text = text
+    token_ids = tokenizer.encode(text, return_tensors="pt").cuda()
+
+    for role in selected_roles:
+        role_pattern = re.escape(role)
+        masked_text = re.sub(role_pattern, MASK_TOKEN, masked_text)
+
+    masked_ids = tokenizer.encode(masked_text, return_tensors="pt").cuda()
+    with torch.no_grad():
+        outputs = model(input_ids=masked_ids)
+        logits = outputs.logits[0]
+        predictions = torch.argmax(logits, dim=-1)
+
+    original_ids = tokenizer.convert_ids_to_tokens(token_ids[0])
+    predicted_ids = tokenizer.convert_ids_to_tokens(predictions)
+    masked_ids_tokens = tokenizer.convert_ids_to_tokens(masked_ids[0])
+
+    for i, token in enumerate(masked_ids_tokens):
+        if token == MASK_TOKEN:
+            results.append({
+                "Position": i,
+                "Masked Token": MASK_TOKEN,
+                "Predicted": predicted_ids[i],
+                "Original": original_ids[i] if i < len(original_ids) else "",
+                "Match": "✅" if predicted_ids[i] == original_ids[i] else "❌"
+            })
+
+    accuracy = sum(1 for r in results if r["Match"] == "✅") / max(len(results), 1)
+    return results, f"Accuracy: {accuracy:.1%}"
+
+def build_interface():
+    role_checkboxes = [gr.Checkbox(label=role, value=False) for role in config.symbolic_roles]
+
+    with gr.Blocks() as demo:
+        gr.Markdown("## 🔎 Symbolic BERT Inference Test")
+        with gr.Row():
+            with gr.Column():
+                input_text = gr.Textbox(label="Symbolic Input Caption", lines=3)
+                selected_roles = gr.CheckboxGroup(
+                    choices=config.symbolic_roles,
+                    label="Mask these symbolic roles"
+                )
+                run_btn = gr.Button("Run Mask Inference")
+            with gr.Column():
+                output_table = gr.Dataframe(headers=["Position", "Masked Token", "Predicted", "Original", "Match"], interactive=False)
+                accuracy_score = gr.Textbox(label="Mask Accuracy")
+
+        run_btn.click(fn=mask_and_predict, inputs=[input_text, selected_roles], outputs=[output_table, accuracy_score])
+
+    return demo
 
 
 if __name__ == "__main__":
+    demo = build_interface()
     demo.launch()