added products

app.py

@@ -2,22 +2,48 @@ import gradio as gr
 from fastai.vision.all import *
 import skimage
 import pathlib
+import pandas as pd
 
 plt = platform.system()
 if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
 
-learn = load_learner('export.pkl')
+# learn = load_learner('export.pkl')
 
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+# labels = learn.dls.vocab
+# def predict(img):
+#     img = PILImage.create(img)
+#     pred,pred_idx,probs = learn.predict(img)
+#     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 title = "Face condition Analyzer"
 description = "A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces."
 examples = [['harmonal_acne.jpg'],['forehead_wrinkles.jpg'],['oily_skin.jpg']]
 enable_queue=True
 
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,
-             description=description,examples=examples,enable_queue=enable_queue).launch()
+# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,
+#              description=description,examples=examples,enable_queue=enable_queue).launch()
+with gr.Blocks(title=title,description=description,examples=examples,enable_queue=enable_queue) as demo:
+    learn = load_learner('export.pkl')
+    labels = learn.dls.vocab
+    def predict(img):
+        img = PILImage.create(img)
+        pred,pred_idx,probs = learn.predict(img)
+        return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    gr.Markdown("# Face Skin Analyzer")
+    gr.Markdown("A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces. Kindly upload a photo of your face.")
+    with gr.Row():
+        inputs = gr.inputs.Image(shape=(512, 512))
+        outputs = gr.outputs.Label(num_top_classes=3)
+    btn = gr.Button("Predict")
+    btn.click(fn=predict, inputs=inputs, outputs=outputs)
+    
+    df=pd.read_excel("recommendation.xlsx")
+    classes = df['class'].unique()
+    with gr.Accordion("Find your skin condition using above analyzer and see the Recommended solutions",open=False):
+        for c in classes:
+            with gr.Accordion(c,open=False):
+                df_temp = df[df['class']==c]
+                for i,current_row in df_temp.iterrows():
+                    html_box = gr.HTML("<span><a href='{}'><img src ='{}'></a></span>".format(current_row['profit_link'],current_row['product_image']))
+
+demo.launch()