Update app.py

app.py

@@ -1,64 +1,114 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import pandas as pd
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def get_data_product_id_from_table(evt: gr.SelectData):
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    id=evt.value
 
-    messages.append({"role": "user", "content": message})
+    return get_data_product_id(id)
 
-    response = ""
+def get_data_product_id(id):
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    print(id)
 
-        response += token
-        yield response
+    image_path_front = dataset_merged_df.loc[dataset_merged_df['ID'] == id, 'Front photo'].values[0]
+    image_path_ingredients = dataset_merged_df.loc[dataset_merged_df['ID'] == id, 'Ingredients photo'].values[0]
+    image_path_nutritionals = dataset_merged_df.loc[dataset_merged_df['ID'] == id, 'Nutritionals photo'].values[0]
 
+    features = ['brand', 'product_name', 'ingredients', 'energy_kj', 'energy_kcal', 'fat', 'saturated_fat', 'carbohydrates', 'sugars', 'fibers', 'proteins', 'salt']
 
+    data = []
+
+    for feature in features:
+        product_values = dataset_merged_df.loc[dataset_merged_df['ID'] == id, [f'Reference_{feature}',f'Predicted_{feature}',f'accuracy_score_{feature}']]
+        product_values_list = product_values.values.flatten().tolist()
+        data.append([feature]+product_values_list)
+
+    data = pd.DataFrame(data, columns=['Feature', 'Reference value', 'Predicted value', 'Accuracy score'])
+    gradients = 1-data['Accuracy score']
+
+    data = data.map(lambda x: f'{x:g}' if isinstance(x, float) else x)
+    data = data.style.background_gradient(axis=0, gmap=gradients, cmap='summer', vmin=0, vmax=1)
+
+    plots = [image_path_front, image_path_ingredients, image_path_nutritionals]
+
+    return {data_df: data,
+            data_plot: plots,
+            }
+
+def load_data(filepath):
+
+    global dataset_merged_df
+    global dataset_metadata
+
+    dataset_merged_df = pd.read_csv(f"{filepath}")
+    dataset_merged_df['mean_accuracy_score'] = dataset_merged_df.filter(regex='^accuracy_score').mean(axis=1)
+
+    dataset_df  = dataset_merged_df[['ID', 'Reference_brand', 'Reference_product_name', 'mean_accuracy_score']].copy()
+    dataset_df = dataset_df.style.background_gradient(axis=0, gmap=1-dataset_df['mean_accuracy_score'], cmap='summer', vmin=0, vmax=1)
+
+    return dataset_df
+
+def toggle_row_visibility(show):
+    if show:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
+# Custom CSS to set max height for the rows
+custom_css = """
+.dataframe-wrap {
+    max-height: 300px;  /* Set the desired height */
+    overflow-y: scroll;
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+with gr.Blocks(css=custom_css) as demo:
+
+    gr.HTML("<div align='center'><h1>Euroconsumers Food Data Lake</h1>")
+    gr.HTML("<div align='center'><h2>Food data processing</h2>")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload CSV File", type="filepath")
+
+    with gr.Row(visible=False) as dataset_block:
+        with gr.Column():
+            gr.HTML("<h2>Dataset summary</h2>")
+            with gr.Row():
+                gr.HTML("Click on a product ID (FIRST COLUMN) in the table to view product details")
+
+            # Display summary of the dataset - ID, Reference_brand, Reference_product_name, mean_accuracy_score
+            with gr.Row(elem_classes="dataframe-wrap"):
+                dataframe_component = gr.DataFrame()
+
+    with gr.Row(visible=False) as product_detail_block:
+        with gr.Column():
+            # Section for product details
+            gr.HTML("<h2>Product details</h2>")
+
+            # Display product photos
+            data_plot = gr.Gallery(label="Product photos", show_label=True, elem_id="gallery"
+                                    , columns=[3], rows=[1], object_fit="contain", height="auto")
+            # Display product data
+            # https://github.com/gradio-app/gradio/pull/5894
+            data_df = gr.Dataframe(label="Product data", scale=2,
+                               column_widths=["10%", "40%", "40%", "10%"],
+                                wrap=True)
+
+    ### Control functions
+
+    # Linking the select_dataset change event to update both the gradio DataFrame and product_ids dropdown
+    file_input.change(load_data, inputs=file_input, outputs=dataframe_component)
+    # Toggle visibility of the dataset block
+    file_input.change(toggle_row_visibility, inputs=file_input, outputs=dataset_block)
+
+    # Update the product data and plots when a product ID is clicked in the dataframe
+    dataframe_component.select(fn=get_data_product_id_from_table, outputs=[data_df, data_plot])
+    # Toggle visibility of the product detail block
+    dataframe_component.select(toggle_row_visibility, inputs=file_input, outputs=product_detail_block)
+
+demo.launch(debug=True)
+
+