Update app.py

app.py

@@ -1,24 +1,62 @@
-from transformers import pipeline
+from deepface import DeepFace
+import pandas as pd
 import gradio as gr
+import matplotlib.pyplot as plt
+import tempfile
+import os
 
-def PromptGenerator(prompt):
-  pipe = pipeline("text-generation", model="Gustavosta/MagicPrompt-Stable-Diffusion")
+def faceAnalyzer(image_path):
+    def analyze(image_path, attribute):
+        analysis = DeepFace.analyze(img_path=image_path, actions=['gender', 'race', 'emotion', 'age'])
+        df = pd.DataFrame(analysis[0])
+        plot = df[attribute].plot(kind='line', figsize=(9, 5), title=attribute).get_figure()
+        _, temp_filename = tempfile.mkstemp(suffix=".png")
+        plot.savefig(temp_filename, dpi=600)
+        plt.close(plot)
+        return temp_filename
 
-  prompt = pipe(prompt)
+    attributes = ['gender', 'race', 'emotion']
+    images = [analyze(image_path, attribute) for attribute in attributes]
 
-  prompt = prompt[0]
+    return [gr.Image(image) for attribute, image in zip(attributes, images)]
 
-  prompt = prompt["generated_text"]
 
-  return prompt
+def faceAnalyzer2(image_path, attribute):
 
-app = gr.Interface(PromptGenerator,
-                   inputs = "text",
-                   outputs = "text",
-                   theme = gr.themes.Soft(primary_hue="blue",secondary_hue="stone"),
-                   title = "AI Prompt Generator",
-                   examples = ["Landscape of","Pixar style little girl","A racecar driving","Fireflies at night"],
-                   css = ".gradio-container {background: url('file=boy 2.jpeg')}"
-)
+  analysis = DeepFace.analyze(img_path=image_path, actions=['age', 'gender', 'race', 'emotion'])
+    # convert the resulting dictionary to a DataFrame
+  df = pd.DataFrame(analysis[0])
+
+  if attribute == "gender":
+    gender = df['gender'].plot(kind = 'line', figsize = (9, 5), title = 'Gender').get_figure()
+    return gender
+
+  elif attribute == "race":
+    race = df['race'].plot(kind = 'line', figsize = (9, 5), title = 'Race').get_figure()
+    return race
+  
+  elif attribute == "emotion":
+    emotion = df['emotion'].plot(kind = 'line', figsize = (9, 5), title = 'Emotion').get_figure()
+    return emotion
+
+
+app1 = gr.Interface(faceAnalyzer,
+                   inputs=gr.Image(label="Upload Photo"),
+                   outputs=[gr.Image(label="Gender Analysis"),
+                            gr.Image(label="Race Analysis"),
+                            gr.Image(label="Emotion Analysis")],
+                   theme=gr.themes.Soft())
+
+app2 = gr.Interface(faceAnalyzer2,
+                    inputs=[gr.Image(label="Upload Photo"),gr.Radio(choices=["gender","race","emotion"],
+                                                                    value="gender",
+                                                                    label="Attributes",
+                                                                    info="Select an attribute")],
+                    outputs=gr.Plot(label="Analysis Output"),
+                    theme=gr.themes.Soft())
+
+application = gr.TabbedInterface([app1,app2],["Full Analysis","Select Analysis"],theme=gr.themes.Soft())
+
+
+application.launch()
 
-app.launch()