add checks for images without faces in app

app.py

@@ -210,9 +210,11 @@ def adj_example(adj):
     return example_analysis(prompt)
 def example_analysis(prompt):
     pil_img = pipe(prompt).images[0]
-    # pil_img = Image.open('./a_abrupt_person_photorealistic.png')
+    # pil_img = Image.open('./this-is-fine.0.jpg')
     df = process_image_pil(pil_img,prompt)
-    rgb_tup = df["skin color"][0]
+    rgb_tup = (128,128,128)
+    if "skin color" in df:
+        rgb_tup = df["skin color"][0]
 
     def clamp(x):
         return max(0, min(int(x), 255))
@@ -222,8 +224,8 @@ def example_analysis(prompt):
     intensity_val = rgb_intensity(rgb_tup)
     intense_hex = str(hex(int(intensity_val)))
     intense_hex = f"#{intense_hex}{intense_hex}{intense_hex}"
-    gender_w = float(df["gender.Woman"][0])
-    gender_m = float(df["gender.Man"][0])
+    gender_w = float(df["gender.Woman"][0]) if "gender.Woman" in df else -1
+    gender_m = float(df["gender.Man"][0]) if "gender.Man" in df else -1
     gender_str = f"Male ({gender_m}%)" if gender_m>gender_w else f"Female({gender_w}%)"
     return pil_img,gender_str,rgb_hex,intense_hex,score_prompt(prompt)
 
@@ -249,7 +251,7 @@ def cached_results(model):
 
 if __name__=='__main__':
     disclaimerString = ""
-    # example_analysis("a abrupt person")
+    example_analysis("a abrupt person")
     with gr.Blocks() as demo:
         gr.Markdown("# Facial Adjectival Color and Income Auditor")
         gr.Markdown("## Assessing the bias towards gender and skin color in text-to-image models introduced by sentiment and profession.")