Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""multilingual_Semantic_Search.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Wg8tD1NJqY0lnvSnsZQhB66pAvxSu65h
+
+# Multilingual  Semantic Search
+Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search. 
+
+![Searching an archive using sentence embeddings](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/basic-semantic-search-overview.png?3)
+
+In this notebook, we'll build a simple semantic search engine. The applications of semantic search go beyond building a web search engine. They can empower a private search engine for internal documents or records. It can also be used to power features like StackOverflow's "similar questions" feature.
+
+1. Get the archive of questions
+2. [Embed](https://docs.cohere.ai/embed-reference/) the archive
+3. Search using an index and nearest neighbor search
+4. Visualize the archive based on the embeddings
+"""
+
+# Install Cohere for embeddings, Umap to reduce embeddings to 2 dimensions, 
+# Altair for visualization, Annoy for approximate nearest neighbor search
+#!pip install cohere umap-learn altair annoy datasets tqdm
+
+"""Get your Cohere API key by [signing up here](https://os.cohere.ai/register). Paste it in the cell below."""
+
+#pip install umap
+
+#@title Import libraries (Run this cell to execute required code) {display-mode: "form"}
+
+import cohere
+import numpy as np
+import re
+import pandas as pd
+from tqdm import tqdm
+from datasets import load_dataset
+import umap
+import altair as alt
+from sklearn.metrics.pairwise import cosine_similarity
+from annoy import AnnoyIndex
+import warnings
+warnings.filterwarnings('ignore')
+pd.set_option('display.max_colwidth', None)
+
+"""You'll need your API key for this next cell. [Sign up to Cohere](https://os.cohere.ai/) and get one if you haven't yet."""
+
+# Paste your API key here. Remember to not share publicly
+api_key = 'twdqnY8kzEsMnu3N0bTX2JsqFUWybVczDDNZTjpd'
+
+# Create and retrieve a Cohere API key from os.cohere.ai
+co = cohere.Client(api_key)
+
+"""## 1. Get The Archive of Questions
+We'll use the [trec](https://www.tensorflow.org/datasets/catalog/trec) dataset which is made up of questions and their categories.
+"""
+
+# # Get dataset
+# dataset = load_dataset("trec", split="train")
+
+# # Import into a pandas dataframe, take only the first 1000 rows
+# df = pd.DataFrame(dataset)[:1000]
+
+# # Preview the data to ensure it has loaded correctly
+# df.head(10)
+
+import pandas as pd
+
+# Get dataset
+# dataset = load_dataset("trec", split="train")
+# https://www.shanelynn.ie/pandas-csv-error-error-tokenizing-data-c-error-eof-inside-string-starting-at-line/
+df = pd.read_excel("/content/news_articles_dataset.xlsx")
+
+df.head()
+
+df.columns
+
+# combine columns , 'summary'
+cols = ['Title ', 'News']
+df['text'] = df[cols].apply(lambda row: ' \n '.join(row.values.astype(str)), axis=1)
+df['text'].head()
+
+"""## 2. Embed the archive
+The next step is to embed the text of the questions.
+
+![embedding archive texts](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/semantic-search-embed-text-archive.png)
+
+To get a thousand embeddings of this length should take about fifteen seconds.
+"""
+
+# Get the embeddings
+embeds = co.embed(texts=list(df['text']),model="multilingual-22-12",truncate="LEFT").embeddings
+
+# Check the dimensions of the embeddings
+embeds = np.array(embeds)
+print(embeds.shape)
+print(embeds)
+print(df['text'][0])
+print(embeds[0])
+
+print(embeds.shape)
+
+"""## 3. Search using an index and nearest neighbor search
+![Building the search index from the embeddings](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/semantic-search-index.png)
+Let's now use [Annoy](https://github.com/spotify/annoy) to build an index that stores the embeddings in a way that is optimized for fast search. This approach scales well to a large number of texts (other options include [Faiss](https://github.com/facebookresearch/faiss), [ScaNN](https://github.com/google-research/google-research/tree/master/scann), and [PyNNDescent](https://github.com/lmcinnes/pynndescent)).
+
+After building the index, we can use it to retrieve the nearest neighbors either of existing questions (section 3.1), or of new questions that we embed (section 3.2).
+"""
+
+# Create the search index, pass the size of embedding
+search_index = AnnoyIndex(embeds.shape[1], 'angular')
+print(search_index)
+# Add all the vectors to the search index
+for i in range(len(embeds)):
+    search_index.add_item(i, embeds[i])
+    print(search_index)
+
+
+search_index.build(10) # 10 trees
+search_index.save('test.ann')
+
+"""### 3.1. Find the neighbors of an example from the dataset
+If we're only interested in measuring the distance between the questions in the dataset (no outside queries), a simple way is to calculate the distance between every pair of embeddings we have.
+"""
+
+# Choose an example (we'll retrieve others similar to it)
+example_id = 5
+
+# Retrieve nearest neighbors
+similar_item_ids = search_index.get_nns_by_item(example_id,10,
+                                                include_distances=True)
+# Format and print the text and distances
+results = pd.DataFrame(data={'texts': df.iloc[similar_item_ids[0]]['text'], 
+                             'distance': similar_item_ids[1]}).drop(example_id)
+
+print(f"Question:'{df.iloc[example_id]['text']}'\nNearest neighbors:")
+results
+
+"""### 3.2. Find the neighbors of a user query
+We're not limited to searching using existing items. If we get a query, we can embed it and find its nearest neighbors from the dataset.
+"""
+
+# query = "skin care ayurveda"
+# query = "how much money did skin care ayurveda raise"
+# query = "semelso wife arrest"
+# query = "avatar 2 movie collection"
+# query = "బాలయ్య మాస్ ట్రీట్"
+
+def multilingual_semantic_search(query):
+  # query = "is messi the best footballer of all time?"
+
+  # Get the query's embedding
+  query_embed = co.embed(texts=[query],
+                    model="multilingual-22-12",
+                    truncate="LEFT").embeddings
+
+  # Retrieve the nearest neighbors
+  similar_item_ids = search_index.get_nns_by_vector(query_embed[0],10,
+                                                  include_distances=True)
+  # Format the results
+  # results = pd.DataFrame(data={'texts': df.iloc[similar_item_ids[0]]['text'], 
+  #                              'distance': similar_item_ids[1]})
+
+  results = pd.DataFrame(data={'title': df.iloc[similar_item_ids[0]]['Title '], 
+                              'news': df.iloc[similar_item_ids[0]]['News'],
+                              'distance': similar_item_ids[1]})
+
+  response = {}
+
+  # JSON response
+  # for i in similar_item_ids[0]:
+  #   # print(i)
+  #   response[i] = \
+  #             {     \
+  #                 "title": df.iloc[i]['Title '], \
+  #                 "news": df.iloc[i]['News'] 
+  #             }
+
+  response = """ """
+  for i in similar_item_ids[0]:
+  # print(i)
+    response += "Title: " + df.iloc[i]['Title '] + " \n " +"Short News: "+ df.iloc[i]['News'] + "\n\n"
+
+  # print(similar_item_ids)
+  # print(similar_item_ids[0])
+  # print(similar_item_ids[1])
+
+  # print(f"Query:'{query}'\nNearest neighbors:")
+  # print(results)
+  # print("----------------------")
+  # print(type(response))
+
+  print(response)
+  return response
+
+multilingual_semantic_search("is messi the best footballer of all time?")
+
+#!pip install gradio
+import gradio as gr
+# demo = gr.Interface(fn=multilingual_semantic_search, inputs="text", outputs="text")
+with gr.Blocks() as demo:
+    gr.Markdown("🌍 This app uses a multilingual semantic model from COhere to 🚀 revolutionize the media and news industry in multilingual markets like India, allowing anyone to track 📰 regional news in real-time without the need for translation or understanding of other regional languages. 🙌")
+    name = gr.Textbox(label="*Semantic search enable! Search for a news...")
+    output = gr.Textbox(label="Semantic search results")
+    greet_btn = gr.Button("Search")
+    theme="darkpeach"
+    greet_btn.click(fn=multilingual_semantic_search, inputs=name, outputs=output)
+demo.launch()
+
+#!pip install gradio
+
+"""## 4. Visualizing the archive
+Finally, let's plot out all the questions onto a 2D chart so you're able to visualize the semantic similarities of this dataset!
+"""
+
+#@title Plot the archive {display-mode: "form"}
+
+# UMAP reduces the dimensions from 1024 to 2 dimensions that we can plot
+reducer = umap.UMAP(n_neighbors=20) 
+umap_embeds = reducer.fit_transform(embeds)
+# Prepare the data to plot and interactive visualization
+# using Altair
+df_explore = pd.DataFrame(data={'text': df['text']})
+df_explore['x'] = umap_embeds[:,0]
+df_explore['y'] = umap_embeds[:,1]
+
+# Plot
+chart = alt.Chart(df_explore).mark_circle(size=60).encode(
+    x=#'x',
+    alt.X('x',
+        scale=alt.Scale(zero=False)
+    ),
+    y=
+    alt.Y('y',
+        scale=alt.Scale(zero=False)
+    ),
+    tooltip=['text']
+).properties(
+    width=700,
+    height=400
+)
+chart.interactive()
+
+"""Hover over the points to read the text. Do you see some of the patterns in clustered points? Similar questions, or questions asking about similar topics?
+
+This concludes this introductory guide to semantic search using sentence embeddings. As you continue the path of building a search product additional considerations arise (like dealing with long texts, or finetuning to better improve the embeddings for a specific use case). 
+
+
+We can’t wait to see what you start building! Share your projects or find support at [community.cohere.ai](https://community.cohere.ai).
+
+"""