Delete gradio-dashboard.py

gradio-dashboard.py

@@ -1,117 +0,0 @@
-import pandas as pd
-import numpy as np
-from dotenv import load_dotenv
-
-from langchain.schema import Document
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_chroma import Chroma
-
-import gradio as gr
-
-load_dotenv()
-
-books = pd.read_csv("books_with_emotions.csv")
-books["large_thumbnail"] = books["thumbnail"] + "&fife=w800"
-books["large_thumbnail"] = np.where(
-    books["large_thumbnail"].isna(),
-    "cover-not-found.jpg",
-    books["large_thumbnail"],
-)
-
-# Create documents directly from DataFrame instead of loading from file
-documents = []
-for _, row in books.iterrows():
-    content = f"{row['isbn13']} {row['description']}"
-    documents.append(Document(page_content=content))
-
-# Create the vector database using HuggingFace embeddings
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-db_books = Chroma.from_documents(documents, embeddings)
-
-
-def retrieve_semantic_recommendations(
-        query: str,
-        category: str = None,
-        tone: str = None,
-        initial_top_k: int = 50,
-        final_top_k: int = 16,
-) -> pd.DataFrame:
-
-    recs = db_books.similarity_search(query, k=initial_top_k)
-    books_list = [int(float(rec.page_content.strip('"').split()[0])) for rec in recs]
-    book_recs = books[books["isbn13"].isin(books_list)].head(initial_top_k)
-
-    if category != "All":
-        book_recs = book_recs[book_recs["simple_categories"] == category].head(final_top_k)
-    else:
-        book_recs = book_recs.head(final_top_k)
-
-    # Only sort by emotion if the columns exist
-    if tone == "Happy" and "joy" in book_recs.columns:
-        book_recs = book_recs.sort_values(by="joy", ascending=False)
-    elif tone == "Surprising" and "surprise" in book_recs.columns:
-        book_recs = book_recs.sort_values(by="surprise", ascending=False)
-    elif tone == "Angry" and "anger" in book_recs.columns:
-        book_recs = book_recs.sort_values(by="anger", ascending=False)
-    elif tone == "Suspenseful" and "fear" in book_recs.columns:
-        book_recs = book_recs.sort_values(by="fear", ascending=False)
-    elif tone == "Sad" and "sadness" in book_recs.columns:
-        book_recs = book_recs.sort_values(by="sadness", ascending=False)
-
-    return book_recs
-
-
-def recommend_books(
-        query: str,
-        category: str,
-        tone: str
-):
-    recommendations = retrieve_semantic_recommendations(query, category, tone)
-    results = []
-
-    for _, row in recommendations.iterrows():
-        description = row["description"]
-        truncated_desc_split = description.split()
-        truncated_description = " ".join(truncated_desc_split[:30]) + "..."
-
-        authors_split = row["authors"].split(";")
-        if len(authors_split) == 2:
-            authors_str = f"{authors_split[0]} and {authors_split[1]}"
-        elif len(authors_split) > 2:
-            authors_str = f"{', '.join(authors_split[:-1])}, and {authors_split[-1]}"
-        else:
-            authors_str = row["authors"]
-
-        caption = f"{row['title']} by {authors_str}: {truncated_description}"
-        results.append((row["large_thumbnail"], caption))
-    return results
-
-# Fix: Filter out NaN values before sorting
-categories = ["All"] + sorted(books["simple_categories"].dropna().unique())
-
-# Only include emotion tones if the emotion columns exist
-emotion_columns = ["joy", "surprise", "anger", "fear", "sadness"]
-emotion_labels = ["Happy", "Surprising", "Angry", "Suspenseful", "Sad"]
-available_emotions = [label for col, label in zip(emotion_columns, emotion_labels) if col in books.columns]
-tones = ["All"] + available_emotions
-
-with gr.Blocks(theme = gr.themes.Glass()) as dashboard:
-    gr.Markdown("# Semantic book recommender")
-
-    with gr.Row():
-        user_query = gr.Textbox(label = "Please enter a description of a book:",
-                                placeholder = "e.g., A story about forgiveness")
-        category_dropdown = gr.Dropdown(choices = categories, label = "Select a category:", value = "All")
-        tone_dropdown = gr.Dropdown(choices = tones, label = "Select an emotional tone:", value = "All")
-        submit_button = gr.Button("Find recommendations")
-
-    gr.Markdown("## Recommendations")
-    output = gr.Gallery(label = "Recommended books", columns = 8, rows = 2)
-
-    submit_button.click(fn = recommend_books,
-                        inputs = [user_query, category_dropdown, tone_dropdown],
-                        outputs = output)
-
-
-if __name__ == "__main__":
-    dashboard.launch()