Create app.py

app.py

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+import streamlit as st
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load your trained model and processor
+model_name = "mjpsm/confidence-image-classifier"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Confident",
+    1: "No Confidence",
+    2: "Somewhat Confident"
+}
+
+# Streamlit app title
+st.title("Confidence Detector 📸")
+
+st.write("Take a picture or upload one, and the AI will predict your confidence level!")
+
+# Upload or capture an image
+uploaded_file = st.camera_input("Take a picture")  # Opens your webcam
+# You could also use st.file_uploader("Upload a picture", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocess
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    
+    # Map prediction
+    predicted_label = id2label[predicted_class_idx]
+    
+    # Show result
+    st.markdown(f"## Prediction: **{predicted_label}** 🎯")