Test

app.py

@@ -1,62 +1,78 @@
+# import gradio as gr
+# import pandas as pd
+# import numpy as np
+
+# import pickle, json
+# from src.utils import *
+
+# ##### Start #####
+
+# # load operation data
+# path1 = "data/brand_belong_category_dict.json"
+# path2 = "data/product_upper_category_dict.json"
+# path3 = "data/offered_brands.pkl"
+# path4 = "data/offer_retailer.csv"
+
+# with open(path1, 'r') as f:
+#     brand_belong_category_dict = json.load(f)
+
+# with open(path2, 'rb') as f:
+#     category_dict = json.load(f)
+
+# with open(path3, 'rb') as f:
+#     offered_brands = pickle.load(f)
+
+# df_offers_brand_retailer = pd.read_csv(path4)
+
+# examples = [
+#     ["Simply Spiked Lemonade 12 pack at Walmart"],
+#     ["Back to the Roots Garden Soil, 1 cubic foot, at Lowe's Home Improvement"],
+#     ["Costco Member subscription"],
+#     ["Apple watch coupon at Best Buy"],
+#     ["A giraffe at Lincoln Park Zoo"]
+# ]
+
+# def main(sentence: str, score_type: str, threshold_cosine: float, threshold_jaccard: float):
+#     threshold = threshold_cosine if score_type == "cosine" else threshold_jaccard    
+#     results = search_offers(sentence, df_offers_brand_retailer, category_dict, brand_belong_category_dict, score_type, threshold)
+#     message, processed_results = process_output(results)
+#     return message, processed_results
+
+# def process_output(output):
+#     """Function to process the output"""
+#     if output is None or output.empty:
+#         return "We couldn't find your results, please try our examples or search again", None
+#     else:
+#         return "We found some great offers!", output
+
+# iface = gr.Interface(
+#     fn=main,
+#     inputs=[
+#         gr.Textbox(lines=1, placeholder="Type here..."),
+#         gr.Dropdown(choices=["cosine", "jaccard"], label="Score Type"),
+#         gr.Slider(minimum=0, maximum=1, step=0.1, label="Threshold for Cosine Similarity"),
+#         gr.Slider(minimum=0, maximum=1, step=0.1, label="Threshold for Jaccard Similarity")
+#     ],
+#     outputs=[gr.Textbox(placeholder="Message..."), gr.Dataframe()],
+#     examples=examples,
+#     live=False,
+# )
+
+
+# iface.launch(share=True)
+
 import gradio as gr
-import pandas as pd
-import numpy as np
-
-import pickle, json
-from src.utils import *
-
-##### Start #####
-
-# load operation data
-path1 = "data/brand_belong_category_dict.json"
-path2 = "data/product_upper_category_dict.json"
-path3 = "data/offered_brands.pkl"
-path4 = "data/offer_retailer.csv"
-
-with open(path1, 'r') as f:
-    brand_belong_category_dict = json.load(f)
-
-with open(path2, 'rb') as f:
-    category_dict = json.load(f)
-
-with open(path3, 'rb') as f:
-    offered_brands = pickle.load(f)
-
-df_offers_brand_retailer = pd.read_csv(path4)
-
-examples = [
-    ["Simply Spiked Lemonade 12 pack at Walmart"],
-    ["Back to the Roots Garden Soil, 1 cubic foot, at Lowe's Home Improvement"],
-    ["Costco Member subscription"],
-    ["Apple watch coupon at Best Buy"],
-    ["A giraffe at Lincoln Park Zoo"]
-]
-
-def main(sentence: str, score_type: str, threshold_cosine: float, threshold_jaccard: float):
-    threshold = threshold_cosine if score_type == "cosine" else threshold_jaccard    
-    results = search_offers(sentence, df_offers_brand_retailer, category_dict, brand_belong_category_dict, score_type, threshold)
-    message, processed_results = process_output(results)
-    return message, processed_results
-
-def process_output(output):
-    """Function to process the output"""
-    if output is None or output.empty:
-        return "We couldn't find your results, please try our examples or search again", None
-    else:
-        return "We found some great offers!", output
-
-iface = gr.Interface(
-    fn=main,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Type here..."),
-        gr.Dropdown(choices=["cosine", "jaccard"], label="Score Type"),
-        gr.Slider(minimum=0, maximum=1, step=0.1, label="Threshold for Cosine Similarity"),
-        gr.Slider(minimum=0, maximum=1, step=0.1, label="Threshold for Jaccard Similarity")
-    ],
-    outputs=[gr.Textbox(placeholder="Message..."), gr.Dataframe()],
-    examples=examples,
-    live=False,
-)
-
-
-iface.launch(share=True)
+from transformers import pipeline
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+def predict(image):
+    predictions = pipeline(image)
+    return {p["label"]: p["score"] for p in predictions}
+
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="Hot Dog? Or Not?",
+).launch()