Update app.py

app.py

@@ -1,21 +1,30 @@
 import gradio as gr
-import pandas as pd
 import cv2
 import numpy as np
+import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
 
-# Load dataset function
-def load_dataset(file):
-    df = pd.read_excel(file)
-    return df.head()
+# Load dataset and train model
+df = pd.read_excel("your_dataset.xlsx")
+le = LabelEncoder()
+df["skin_tone"] = le.fit_transform(df["skin_tone"])
+
+X_train, X_test, y_train, y_test = train_test_split(df["image"], df["skin_tone"], test_size=0.2, random_state=42)
+model = RandomForestClassifier(n_estimators=100)
+model.fit(X_train, y_train)
+
+# Define Gradio function
+def predict_skin_tone(image):
+    img_resized = cv2.resize(image, (100, 100)).flatten().reshape(1, -1)
+    prediction = model.predict(img_resized)
+    return le.inverse_transform(prediction)[0]
 
-# Gradio interface
 iface = gr.Interface(
-    fn=load_dataset,
-    inputs=gr.File(label="Upload Excel Dataset"),
-    outputs=gr.Dataframe(label="Preview of Dataset"),
-    title="Skin Tone Detector"
+    fn=predict_skin_tone,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs="text",
+    title="Skin Tone Prediction"
 )
 
 iface.launch()