Create app.py

app.py

@@ -0,0 +1,126 @@
+import streamlit as st
+from transformers import pipeline
+
+def main():
+    st.set_page_config(page_title="Unmasked the Target Customers", page_icon="🦜")
+    st.header("Turn the photos taken in the campaign to useful marketing insights")
+    uploaded_file = st.file_uploader("Select an Image...")
+    
+    def pipeline_1_final(image_lst):
+        pipe = pipeline("object-detection", model="hustvl/yolos-tiny")
+        preds = pipe(image)
+        person_count = 0
+        sub_image_lst = []
+        for pred in preds:
+            if pred['label'] == 'person':
+                person_count +=1
+                box = pred['box']
+                xmin, ymin, xmax, ymax = box.values()
+                sub_image = extract_subimage(image,xmin, xmax, ymin, ymax)
+                sub_image_lst += [sub_image]
+        return sub_image_lst, person_count
+    
+    def pipeline_2_final(image_lst):
+        age_lst = []
+        age_mapping = {"0-2": "lower than 10",
+                  "3-9": "lower than 10",
+                  "10-19":"10-19",
+                  "20-29":"20-29",
+                  "30-39":"30-39",
+                  "40-49":"40-49",
+                  "50-59":"50-59",
+                  "60-69":"60-69",
+                  "more than 70" : "70 or above"}
+        pipe = pipeline("image-classification", model="nateraw/vit-age-classifier")
+        for image in image_lst:
+            preds = pipe(image)
+            preds_age_range = preds[0]['label']
+            preds_age_range = age_mapping[preds_age_range]
+            age_lst +=[preds_age_range]
+        return age_lst
+    
+    def pipeline_3_final(image_lst):
+        gender_lst = []
+        pipe = pipeline("image-classification", model="mikecho/NTQAI_pedestrian_gender_recognition_v1")
+        for image in image_lst:
+            preds = pipe(image)
+            preds_gender = preds[0]['label']
+            gender_lst +=[preds_gender]
+        return gender_lst
+    
+    def gender_prediciton_model_NTQAI_pedestrian_gender_recognition(image_lst):
+        gender_lst = []
+        pipe = pipeline("image-classification", model="NTQAI/pedestrian_gender_recognition")
+        for image in image_lst:
+            preds = pipe(image)
+            preds_gender = preds[0]['label']
+            gender_lst +=[preds_gender]
+        return gender_lst
+
+    
+    def pipeline_4_final(image_lst):
+        start_time = time.time()
+        pipe = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
+        preds_lst = []
+        for image in image_lst:
+            preds = pipe(image)
+            preds_emotion = preds[0]['label']
+            preds_lst +=[preds_emotion]
+      return preds_lst
+    def generate_gender_tables(gender_list, age_list, emotion_list):
+        gender_count = {}
+        for gender, age, emotion in zip(gender_list, age_list, emotion_list):
+            if age not in gender_count:
+                gender_count[age] = {'male': 0, 'female': 0}
+                gender_count[age][gender] += 1
+        happiness_percentage = {}
+        for gender, age, emotion in zip(gender_list, age_list, emotion_list):
+            if age not in happiness_percentage:
+                happiness_percentage[age] = {'male': 0, 'female': 0}
+            if emotion == 'happiness':
+                happiness_percentage[age][gender] += 1
+    
+        table1 = []
+        for age, count in gender_count.items():
+            male_count = count['male']
+            female_count = count['female']
+            table1.append([age, male_count, female_count])
+    
+        table2 = []
+        for age, happiness in happiness_percentage.items():
+            male_count = gender_count[age]['male']
+            female_count = gender_count[age]['female']
+            male_percentage = (happiness['male'] / male_count) * 100 if male_count > 0 else 0
+            female_percentage = (happiness['female'] / female_count) * 100 if female_count > 0 else 0
+            table2.append([age, male_percentage, female_percentage])
+    
+        return table1, table2
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        bytes_data = uploaded_file.getvalue()
+        with open(uploaded_file.name, "wb") as file:
+            file.write(bytes_data)
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+
+        pipeline_1_out, person_count = pipeline_1_final(uploaded_file.name)
+        pipeline_2_age = pipeline_2_final(pipeline_1_out)
+        pipeline_3_gender = pipeline_3_final(pipeline_1_out)
+        pipeline_4_emotion = pipeline_3_final(pipeline_1_out)
+        table1, table2 = generate_gender_tables(pipeline_3_gender, pipeline_2_age, pipeline_4_emotion)
+        st.text('The detected number of person:', person_count)
+        st.text('\nGender and Age Group Distribution')
+        st.text('Age, Male, Female')
+        for row in table1:
+            print(row)
+        
+
+        st.text('\nShare of Happniess')
+        st.text('Age, Male, Female')
+        for row in table2:
+            print(row)
+
+
+if __name__ == "__main__":
+    main()