First commit

app.py

@@ -0,0 +1,61 @@
+import torch
+import gradio as gr
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+)
+from typing import Dict
+
+FOUNDATIONS = ["authority", "care", "fairness", "loyalty", "sanctity"]
+tokenizer = AutoTokenizer.from_pretrained("joshnguyen/mformer-authority")
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+MODELS = {}
+for foundation in FOUNDATIONS:
+    model = AutoModelForSequenceClassification.from_pretrained(
+        pretrained_model_name_or_path=f"joshnguyen/mformer-{foundation}",
+    )
+    MODELS[foundation] = model.to(DEVICE)
+
+
+def classify_text(text: str) -> Dict[str, float]:
+    # Encode the prompt
+    inputs = tokenizer([text],
+                       padding=True,
+                       truncation=True,
+                       return_tensors='pt').to(DEVICE)
+    scores = {}
+    for foundation in FOUNDATIONS:
+        model = MODELS[foundation]
+        outputs = model(**inputs)
+        outputs = torch.softmax(outputs.logits, dim=1)
+        outputs = outputs[:, 1]
+        score = outputs.detach().cpu().numpy()[0]
+        scores[foundation] = score
+    return scores
+
+
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=[
+        # Prompt
+        gr.Textbox(
+            label="Input text",
+            container=False,
+            show_label=True,
+            placeholder="Enter some text...",
+            lines=10,
+            scale=10,
+        ),
+    ],
+    outputs=[
+        gr.Label(
+            label="Moral foundations scores",
+            container=False,
+            show_label=True,
+            scale=10,
+            lines=10,
+        )
+    ],
+)
+
+demo.queue(max_size=20).launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio==3.37.0
+protobuf==3.20.3
+scipy==1.11.1
+sentencepiece==0.1.99
+torch==2.0.1
+transformers==4.31.0