Update app.py

app.py

@@ -1,16 +1,36 @@
-import streamlit as st
+import gradio as gr
 from deepface import DeepFace
+from PIL import Image
+import numpy as np
 
-st.title("DeepFace Face Analysis")
+# Function to analyze the uploaded image
+def analyze_face(image):
+    # Convert the image to a numpy array
+    image_array = np.array(image)
 
-uploaded_file = st.file_uploader("Upload an image for face analysis:", type=["jpg", "png", "jpeg"])
-
-if uploaded_file is not None:
     # Perform DeepFace analysis
-    analysis_result = DeepFace.analyze(img_path=uploaded_file, actions=['age', 'gender', 'race', 'emotion'])
-    
-    # Display analysis results
-    st.write("Age:", analysis_result['age'])
-    st.write("Gender:", analysis_result['gender'])
-    st.write("Race:", analysis_result['dominant_race'])
-    st.write("Emotion:", analysis_result['dominant_emotion'])
+    analysis_result = DeepFace.analyze(img_path=image_array, actions=['age', 'gender', 'race', 'emotion'])
+
+    # Extract results
+    age = analysis_result['age']
+    gender = analysis_result['gender']
+    race = analysis_result['dominant_race']
+    emotion = analysis_result['dominant_emotion']
+    emotions_detail = analysis_result['emotion']
+
+    # Return the results
+    return age, gender, race, emotion, emotions_detail
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=analyze_face,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Number(label="Age"), 
+             gr.Text(label="Gender"), 
+             gr.Text(label="Race"), 
+             gr.Text(label="Dominant Emotion"),
+             gr.JSON(label="Emotion Breakdown")]
+)
+
+# Launch the Gradio app
+iface.launch()