added all files

app.py

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+import gradio as gr
+import pandas as pd
+from thirdai import bolt, licensing
+import os
+import time
+
+
+licensing.set_path("license.serialized")
+max_posts = 5
+df = pd.read_csv("processed_recipes_3.csv")
+model = bolt.UniversalDeepTransformer.load("1bn_name_ctg_keywords.bolt")
+
+recipe_id_to_row_num = {}
+
+for i in range(data.shape[0]):
+    recipe_id_to_row_num[data.iloc[i,0]] = i
+
+
+LIKE_TEXT = "👍"
+FEEDBACK_RECEIVED_TEXT = "Model updated 👌"
+SHOW_MORE = "Show more"
+SHOW_LESS = "Show less"
+
+
+def retrain(query, doc_id):
+    df = pd.DataFrame({
+        "Name": [query.replace('\n', ' ')],
+        "RecipeId": [str(doc_id)]
+    })
+
+    filename = f"temptrain{hash(query)}{hash(doc_id)}{time.time()}.csv"
+
+    df.to_csv(filename)
+
+    prediction = None
+
+    while prediction != doc_id:
+        model.train(filename, epochs=1)
+        prediction = model.predict(
+            {"Name": query.replace('\n', ' ')},
+            return_predicted_class=True)
+
+    os.remove(filename)
+
+    # sample = {"query": query.replace('\n', ' '), "id": str(doc_id)}
+    # batch = [sample]
+
+    # prediction = None
+
+    # while prediction != doc_id:
+    #     model.train_batch(batch, metrics=["categorical_accuracy"])
+    #     prediction = model.predict(sample, return_predicted_class=True)
+
+
+def search(query):
+    scores = model.predict({"Name": query.lower()})
+    K = min(2*max_posts, len(scores) - 1)
+    sorted_post_ids = scores.argsort()[-K:][::-1]
+    count = 0
+    relevant_posts = []
+    for pid in sorted_post_ids:
+        if pid in recipe_id_to_row_num:
+            relevant_posts.append(df.iloc[recipe_id_to_row_num[pid]])
+        ##
+        count += 1
+        if count==max_posts:
+            break
+    ##
+    header = [gr.Markdown.update(visible=True)]
+    boxes = [
+        gr.Box.update(visible=True)
+        for _ in relevant_posts
+    ]
+    titles = [
+        gr.Markdown.update(f"## {post['Name']}")
+        for post in relevant_posts
+    ]
+    toggles = [
+        gr.Button.update(
+            visible=True,
+            value=SHOW_MORE,
+            interactive=True,
+        )
+        for _ in relevant_posts
+    ]
+    matches = [
+        gr.Button.update(
+            value=LIKE_TEXT,
+            interactive=True,
+        )
+        for _ in relevant_posts
+    ]
+    bodies = [
+        gr.HTML.update(
+            visible=False,
+            value=f"<br/>"
+            f"<h2>Description:</h2>\n{post['Description']}\n\n"
+            "<hr class='solid'>"
+            f"<h2>Ingredients:</h2>\n{post['RecipeIngredientParts']}\n\n"
+            "<br/>"
+            f"<h2>Instructions:</h2>\n{post['RecipeInstructions']}\n\n"
+            "<br/>")
+        for post in relevant_posts
+    ]
+
+    return (
+        header +
+        boxes +
+        titles +
+        toggles +
+        matches +
+        bodies +
+        [sorted_post_ids]
+    )
+
+
+def handle_toggle(toggle):
+    if toggle == SHOW_MORE:
+        new_toggle_text = SHOW_LESS
+        visible = True
+    if toggle == SHOW_LESS:
+        new_toggle_text = SHOW_MORE
+        visible = False
+    return [
+        gr.Button.update(new_toggle_text),
+        gr.HTML.update(visible=visible),
+    ]
+
+
+def handle_feedback(button_id: int):
+    def register_feedback(doc_ids, query):
+        retrain(
+            query=query,
+            doc_id=doc_ids[button_id]
+        )
+        return gr.Button.update(
+            value=FEEDBACK_RECEIVED_TEXT,
+            interactive=False,
+        )
+
+    return register_feedback
+
+
+default_query = (
+    """
+    baby food
+    """
+)
+
+
+with gr.Blocks() as demo:
+    query = gr.Textbox(value=default_query, label="Question", lines=10)
+    submit = gr.Button(value="Search")
+
+    header = [gr.Markdown("# Relevant Recipes", visible=False)]
+    post_boxes = []
+    post_titles = []
+    toggle_buttons = []
+    match_buttons = []
+    post_bodies = []
+    post_ids = gr.State([])
+
+    for i in range(max_posts):
+        with gr.Box(visible=False) as box:
+            post_boxes.append(box)
+
+            with gr.Row():
+                with gr.Column(scale=5):
+                    title = gr.Markdown("")
+                    post_titles.append(title)
+                with gr.Column(scale=1, min_width=370):
+                    with gr.Row():
+                        with gr.Column(scale=3, min_width=170):
+                            toggle = gr.Button(SHOW_MORE)
+                            toggle_buttons.append(toggle)
+                        with gr.Column(scale=1, min_width=170):
+                            match = gr.Button(LIKE_TEXT)
+                            match.click(
+                                fn=handle_feedback(button_id=i),
+                                inputs=[post_ids, query],
+                                outputs=[match],
+                            )
+                            match_buttons.append(match)
+
+            body = gr.HTML("")
+            post_bodies.append(body)
+
+            toggle.click(
+                fn=handle_toggle,
+                inputs=[toggle],
+                outputs=[toggle, body],
+            )
+
+    allblocks = (
+        header +
+        post_boxes +
+        post_titles +
+        toggle_buttons +
+        match_buttons +
+        post_bodies +
+        [post_ids]
+    )
+
+    query.submit(
+        fn=search,
+        inputs=[query],
+        outputs=allblocks)
+    submit.click(
+        fn=search,
+        inputs=[query],
+        outputs=allblocks)
+
+
+demo.launch()

requirements.txt

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+thirdai==0.6.0
+pandas
+gradio