import csv import sys # Increase CSV field size limit csv.field_size_limit(sys.maxsize) import gradio as gr import pandas as pd def data_pre_processing(file_responses): console_messages.append("Starting data pre-processing...") # Financial Weights can be anything (ultimately the row-wise weights are aggregated and the corresponding fractions are obtained from that rows' total tax payed) try: # Define the columns to be processed # Developing Numeric Columns # Convert columns to numeric and fill NaN values with 0 file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Latest estimated Tax payment?'] = pd.to_numeric(file_responses['Latest estimated Tax payment?'], errors='coerce').fillna(0) # Adding a new column 'TotalWeightageAllocated' by summing specific columns by their names file_responses['TotalWeightageAllocated'] = file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] + file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] + file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] # Creating Datasets (we assume everything has been provided to us in English, or the translations have been done already) # Renaming the datasets into similar column headings initial_dataset_1 = file_responses.rename(columns={ 'Personal_TaxDirection_1_Wish': 'Problem_Description', 'Personal_TaxDirection_1_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_1_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] initial_dataset_2 = file_responses.rename(columns={ 'Personal_TaxDirection_2_Wish': 'Problem_Description', 'Personal_TaxDirection_2_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_2_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] initial_dataset_3 = file_responses.rename(columns={ 'Personal_TaxDirection_3_Wish': 'Problem_Description', 'Personal_TaxDirection_3_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_3_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] # Calculating the actual TaxAmount to be allocated against each WISH (by overwriting the newly created columns) initial_dataset_1['Financial_Weight'] = file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] initial_dataset_2['Financial_Weight'] = file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] initial_dataset_3['Financial_Weight'] = file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] # Removing useless rows # Drop rows where Problem_Description is NaN or an empty string initial_dataset_1 = initial_dataset_1.dropna(subset=['Problem_Description'], axis=0) initial_dataset_2 = initial_dataset_2.dropna(subset=['Problem_Description'], axis=0) initial_dataset_3 = initial_dataset_3.dropna(subset=['Problem_Description'], axis=0) # Convert 'Problem_Description' column to string type initial_dataset_1['Problem_Description'] = initial_dataset_1['Problem_Description'].astype(str) initial_dataset_2['Problem_Description'] = initial_dataset_2['Problem_Description'].astype(str) initial_dataset_3['Problem_Description'] = initial_dataset_3['Problem_Description'].astype(str) # Merging the Datasets # Vertically concatenating (merging) the 3 DataFrames merged_dataset = pd.concat([initial_dataset_1, initial_dataset_2, initial_dataset_3], ignore_index=True) # Different return can be used to check the processing console_messages.append("Data pre-processing completed.") # return file_responses return merged_dataset except Exception as e: console_messages.append(f"Error during data pre-processing: {str(e)}") # return str(e), console_messages return None import spacy from transformers import AutoTokenizer, AutoModel import torch # Load SpaCy model # Install the 'en_core_web_sm' model if it isn't already installed try: nlp = spacy.load('en_core_web_sm') except OSError: # Instead of this try~catch, we could also include this < https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0.tar.gz > in the requirements.txt to directly load it from spacy.cli import download download('en_core_web_sm') nlp = spacy.load('en_core_web_sm') # Load Hugging Face Transformers model tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2") model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2") import re import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize # Download necessary NLTK data nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') import numpy as np import sentencepiece as sp from transformers import pipeline # Load a summarization model summarizer = pipeline("summarization") def Summarized_text(passed_text): try: # Summarization summarize_text = summarizer(passed_text, max_length=70, min_length=30, do_sample=False)[0]['summary_text'] return summarize_text except Exception as e: print(f"Summarization failed: {e}") return passed_text ###### Will uncomment Summarization during final deployment... as it takes a lot of time def Lemmatize_text(text): # Text Cleaning text = re.sub(r'[^\w\s]', '', text) text = re.sub(r'\d+', '', text) text = re.sub(r'http\S+', '', text) # Remove https URLs text = re.sub(r'www\.\S+', '', text) # Remove www URLs # Tokenize and remove stopwords tokens = word_tokenize(text.lower()) stop_words = set(stopwords.words('english')) custom_stopwords = {'example', 'another'} # Add custom stopwords tokens = [word for word in tokens if word not in stop_words and word not in custom_stopwords] # NER - Remove named entities doc = nlp(' '.join(tokens)) tokens = [token.text for token in doc if not token.ent_type_] # POS Tagging (optional) pos_tags = nltk.pos_tag(tokens) tokens = [word for word, pos in pos_tags if pos in ['NN', 'NNS']] # Filter nouns # Lemmatize tokens using SpaCy doc = nlp(' '.join(tokens)) lemmatized_text = ' '.join([token.lemma_ for token in doc]) return lemmatized_text # Return the cleaned and lemmatized text from random import random def text_processing_for_domain(text): # First, get the summarized text summarized_text = "" # summarized_text = Summarized_text(text) # Then, lemmatize the original text lemmatized_text = "" lemmatized_text = Lemmatize_text(text) if lemmatized_text and summarized_text: # Join both the summarized and lemmatized text if random() > 0.5: combined_text = summarized_text + " " + lemmatized_text else: combined_text = lemmatized_text + " " + summarized_text return combined_text elif summarized_text: return summarized_text elif lemmatized_text: return lemmatized_text else: return "Sustainability and Longevity" # Default FailSafe from sentence_transformers import SentenceTransformer from sklearn.cluster import AgglomerativeClustering, KMeans from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import silhouette_score from bertopic import BERTopic from collections import Counter def extract_problem_domains(df, text_column='Processed_ProblemDescription_forDomainExtraction', cluster_range=(5, 15), top_words=10): console_messages.append("Extracting Problem Domains...") # Sentence Transformers approach model = SentenceTransformer('all-mpnet-base-v2') embeddings = model.encode(df[text_column].tolist()) # Perform hierarchical clustering with Silhouette Analysis silhouette_scores = [] for n_clusters in range(cluster_range[0], cluster_range[1] + 1): clustering = AgglomerativeClustering(n_clusters=n_clusters) cluster_labels = clustering.fit_predict(embeddings) silhouette_avg = silhouette_score(embeddings, cluster_labels) silhouette_scores.append(silhouette_avg) # Determine the optimal number of clusters optimal_n_clusters = cluster_range[0] + silhouette_scores.index(max(silhouette_scores)) # Perform clustering with the optimal number of clusters clustering = AgglomerativeClustering(n_clusters=optimal_n_clusters) cluster_labels = clustering.fit_predict(embeddings) # Get representative words for each cluster cluster_representations = {} for i in range(optimal_n_clusters): cluster_words = df.loc[cluster_labels == i, text_column].str.cat(sep=' ').split() cluster_representations[i] = [word for word, _ in Counter(cluster_words).most_common(top_words)] # Map cluster labels to representative words df["Problem_Cluster"] = cluster_labels df['Problem_Category_Words'] = [cluster_representations[label] for label in cluster_labels] # console_messages.append("Returning from Problem Domain Extraction function.") console_messages.append("Problem Domain Extraction completed.") return df, optimal_n_clusters import spacy from geopy.geocoders import Nominatim from geopy.exc import GeocoderTimedOut, GeocoderUnavailable import pandas as pd nlp = spacy.load('en_core_web_sm') geolocator = Nominatim(user_agent="my_agent") def extract_and_geocode_locations(text, user_locations): # Extract locations from text doc = nlp(text) extracted_locations = [ent.text for ent in doc.ents if ent.label_ in ['GPE', 'LOC']] # Combine extracted locations with user-provided locations all_locations = list(set(extracted_locations + user_locations.split(', '))) geocoded_locations = [] for loc in all_locations: try: location = geolocator.geocode(loc) if location: geocoded_locations.append({ 'name': loc, 'latitude': location.latitude, 'longitude': location.longitude, 'country': location.raw.get('display_name', '').split(', ')[-1] }) else: # If geocoding fails, add the location without coordinates geocoded_locations.append({ 'name': loc, 'latitude': None, 'longitude': None, 'country': None }) except (GeocoderTimedOut, GeocoderUnavailable): print(f"Geocoding failed for {loc}") # Add the location without coordinates geocoded_locations.append({ 'name': loc, 'latitude': None, 'longitude': None, 'country': None }) return geocoded_locations def text_processing_for_location(row): locations = extract_and_geocode_locations(row['Problem_Description'], row['Geographical_Location']) location_text = ' '.join([loc['name'] for loc in locations]) processed_text = Lemmatize_text(location_text) return processed_text, locations def extract_location_clusters(df, text_column='Processed_LocationText_forClustering', cluster_range=(3, 10), top_words=5): console_messages.append("Extracting Location Clusters...") # Sentence Transformers approach for embeddings model = SentenceTransformer('all-mpnet-base-v2') embeddings = model.encode(df[text_column].tolist()) # Perform hierarchical clustering with Silhouette Analysis silhouette_scores = [] for n_clusters in range(cluster_range[0], cluster_range[1] + 1): clustering = AgglomerativeClustering(n_clusters=n_clusters) cluster_labels = clustering.fit_predict(embeddings) silhouette_avg = silhouette_score(embeddings, cluster_labels) silhouette_scores.append(silhouette_avg) # Determine the optimal number of clusters optimal_n_clusters = cluster_range[0] + silhouette_scores.index(max(silhouette_scores)) # Perform clustering with the optimal number of clusters clustering = AgglomerativeClustering(n_clusters=optimal_n_clusters) cluster_labels = clustering.fit_predict(embeddings) # Get representative words for each cluster cluster_representations = {} for i in range(optimal_n_clusters): cluster_words = df.loc[cluster_labels == i, text_column].str.cat(sep=' ').split() cluster_representations[i] = [word for word, _ in Counter(cluster_words).most_common(top_words)] # Map cluster labels to representative words df["Location_Cluster"] = cluster_labels df['Location_Category_Words'] = [cluster_representations[label] for label in cluster_labels] console_messages.append("Location Clustering completed.") return df, optimal_n_clusters # def Extract_Location(text): # doc = nlp(text) # locations = [ent.text for ent in doc.ents if ent.label_ in ['GPE', 'LOC']] # return ' '.join(locations) # def text_processing_for_location(text): # # Extract locations # locations_text = Extract_Location(text) # # Perform further text cleaning if necessary # processed_locations_text = Lemmatize_text(locations_text) # # Remove special characters, digits, and punctuation # processed_locations_text = re.sub(r'[^a-zA-Z\s]', '', processed_locations_text) # # Tokenize and remove stopwords # tokens = word_tokenize(processed_locations_text.lower()) # stop_words = set(stopwords.words('english')) # tokens = [word for word in tokens if word not in stop_words] # # Join location words into a single string # final_locations_text = ' '.join(tokens) # return final_locations_text if final_locations_text else "India" # def extract_location_clusters(df, # text_column='Processed_LocationText_forClustering', # cluster_range=(3, 10), # top_words=5): # console_messages.append("Extracting Location Clusters...") # # Sentence Transformers approach for embeddings # model = SentenceTransformer('all-mpnet-base-v2') # embeddings = model.encode(df[text_column].tolist()) # # Perform hierarchical clustering with Silhouette Analysis # silhouette_scores = [] # for n_clusters in range(cluster_range[0], cluster_range[1] + 1): # clustering = AgglomerativeClustering(n_clusters=n_clusters) # cluster_labels = clustering.fit_predict(embeddings) # silhouette_avg = silhouette_score(embeddings, cluster_labels) # silhouette_scores.append(silhouette_avg) # # Determine the optimal number of clusters # optimal_n_clusters = cluster_range[0] + silhouette_scores.index(max(silhouette_scores)) # # Perform clustering with the optimal number of clusters # clustering = AgglomerativeClustering(n_clusters=optimal_n_clusters) # cluster_labels = clustering.fit_predict(embeddings) # # Get representative words for each cluster # cluster_representations = {} # for i in range(optimal_n_clusters): # cluster_words = df.loc[cluster_labels == i, text_column].str.cat(sep=' ').split() # cluster_representations[i] = [word for word, _ in Counter(cluster_words).most_common(top_words)] # # Map cluster labels to representative words # df["Location_Cluster"] = cluster_labels # df['Location_Category_Words'] = [cluster_representations[label] for label in cluster_labels] # console_messages.append("Location Clustering completed.") # return df, optimal_n_clusters def nlp_pipeline(original_df): console_messages.append("Starting NLP pipeline...") # Data Preprocessing processed_df = data_pre_processing(original_df) # merged_dataset # Starting the Pipeline for Domain Extraction console_messages.append("Executing Text processing function for Domain identification") # Apply the text_processing_for_domain function to the DataFrame processed_df['Processed_ProblemDescription_forDomainExtraction'] = processed_df['Problem_Description'].apply(text_processing_for_domain) console_messages.append("Removing entries which could not be allocated to any Problem Domain") # processed_df = processed_df.dropna(subset=['Processed_ProblemDescription_forDomainExtraction'], axis=0) # Drop rows where 'Processed_ProblemDescription_forDomainExtraction' contains empty arrays processed_df = processed_df[processed_df['Processed_ProblemDescription_forDomainExtraction'].apply(lambda x: len(x) > 0)] # Domain Clustering try: processed_df, optimal_n_clusters = extract_problem_domains(processed_df) console_messages.append(f"Optimal clusters for Domain extraction: {optimal_n_clusters}") except Exception as e: console_messages.append(f"Error in extract_problem_domains: {str(e)}") console_messages.append("NLP pipeline for Problem Domain extraction completed.") console_messages.append("Starting NLP pipeline for Location extraction with text processing.") # Apply the text_processing_for_location function to the DataFrame # processed_df['Processed_LocationText_forClustering'] = processed_df['Problem_Description'].apply(text_processing_for_location) processed_df['Processed_LocationText_forClustering'], processed_df['Extracted_Locations'] = zip(*processed_df.apply(text_processing_for_location, axis=1)) # Location Clustering try: processed_df, optimal_n_clusters = extract_location_clusters(processed_df) console_messages.append(f"Optimal clusters for Location extraction: {optimal_n_clusters}") except Exception as e: console_messages.append(f"Error in extract_location_clusters: {str(e)}") console_messages.append("NLP pipeline for location extraction completed.") console_messages.append("NLP pipeline completed.") return processed_df console_messages = [] def process_excel(file): console_messages.append("Processing starts. Reading the uploaded Excel file...") # Ensure the file path is correct file_path = file.name if hasattr(file, 'name') else file # Read the Excel file df = pd.read_excel(file_path) try: # Process the DataFrame console_messages.append("Processing the DataFrame...") result_df = nlp_pipeline(df) # output_file = "Output_ProjectProposals.xlsx" output_file = "Output_Proposals.xlsx" result_df.to_excel(output_file, index=False) console_messages.append("Processing completed. Ready for download.") return output_file, "\n".join(console_messages) # Return the processed DataFrame as Excel file except Exception as e: # return str(e) # Return the error message # error_message = f"Error processing file: {str(e)}" # print(error_message) # Log the error console_messages.append(f"Error during processing: {str(e)}") # return error_message, "Santanu Banerjee" # Return the error message to the user return None, "\n".join(console_messages) # example_files = ['#TaxDirection (Responses)_BasicExample.xlsx', # '#TaxDirection (Responses)_IntermediateExample.xlsx', # '#TaxDirection (Responses)_UltimateExample.xlsx' # ] example_files = ['#TaxDirection (Responses)_BasicExample.xlsx', '#TaxDirection (Responses)_IntermediateExample.xlsx', ] import random a_random_object = random.choice(["⇒", "↣", "↠", "→"]) # Define the Gradio interface interface = gr.Interface( fn=process_excel, # The function to process the uploaded file inputs=gr.File(type="filepath", label="Upload Excel File here. \t Be sure to check that the column headings in your upload are the same as in the Example files below. \t (Otherwise there will be Error during the processing)"), # File upload input examples=example_files, # Add the example files outputs=[ gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"), # File download output gr.Textbox(label="Console Messages", lines=15, interactive=False) # Console messages output ], # title="Excel File Uploader", # title="Upload Excel file containing #TaxDirections → Download HyperLocal Project Proposals\n", title = ( "

" "Upload Excel file containing #TaxDirections " # "→ " # "⇒ ↣ ↠ " " " +a_random_object +" " "Download HyperLocal Project Proposals" "

\n" ), description=( "

This tool allows for the systematic evaluation and proposal of solutions tailored to specific location-problem pairs, ensuring efficient resource allocation and project planning. For more information, visit #TaxDirection weblink.

" "

Upload an Excel file to process and download the result or use the Example files:

" "

(click on any of them to directly process the file and Download the result)

" "

Processed output contains a Project Proposal for each Location~Problem paired combination (i.e. each cell).

" "

Corresponding Budget Allocation and estimated Project Completion Time are provided in different sheets.

" "

Note: The example files provided above are for demonstration purposes. Feel free to upload your own Excel files to see the results. If you have any questions, refer to the documentation-links or contact support.

" ) # Solid description with right-aligned second sentence ) # Launch the interface if __name__ == "__main__": interface.launch()