Create check-in

check-in

@@ -0,0 +1,55 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Map model labels to human-readable labels
+LABEL_MAP = {
+    "LABEL_0": "Bad",
+    "LABEL_1": "Mediocre",
+    "LABEL_2": "Good"
+}
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("mjpsm/check-ins-classifier")
+    model = AutoModelForSequenceClassification.from_pretrained("mjpsm/check-ins-classifier")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+st.title("Check-In Classifier")
+st.write("Enter your check-in so I can see if it's **Good**, **Mediocre**, or **Bad**.")
+
+# User input
+user_input = st.text_area("💬 Your Check-In Message:", height=50)
+
+if st.button("🔍 Analyze"):
+    if user_input.strip() == "":
+        st.warning("Please enter some text first!")
+    else:
+        # Tokenize input
+        inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
+
+        # Run inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        logits = outputs.logits
+        probs = F.softmax(logits, dim=1)
+
+        # Get prediction
+        predicted_class = torch.argmax(probs, dim=1).item()
+        label_key = model.config.id2label[predicted_class]
+        human_label = LABEL_MAP.get(label_key, label_key)
+        confidence = torch.max(probs).item()
+
+        st.success(f"🧾 Prediction: **{human_label}** (Confidence: {confidence:.2%})")
+
+        # Show all class probabilities with human-readable labels
+        st.subheader("📊 Class Probabilities:")
+        for idx, prob in enumerate(probs[0]):
+            label_key = model.config.id2label.get(idx, f"LABEL_{idx}")
+            label_name = LABEL_MAP.get(label_key, label_key)
+            st.write(f"{label_name}: {prob:.2%}")