app.py

import gradio as gr
import pandas as pd

def data_pre_processing(file_responses):
    # 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
        # return file_responses
        return merged_dataset
        
    except Exception as e:
        return str(e)






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")

# def combined_text_processing(text):
#     # Basic NLP processing using SpaCy
#     doc = nlp(text)
#     lemmatized_text = ' '.join([token.lemma_ for token in doc])
    
#     # Advanced text representation using Hugging Face Transformers
#     inputs = tokenizer(lemmatized_text, return_tensors="pt", truncation=False, padding=True)
#     with torch.no_grad():
#         outputs = model(**inputs)
    
#     return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()



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')




# def combined_text_processing(text):
#     # Remove punctuation, numbers, URLs, and special characters
#     text = re.sub(r'[^\w\s]', '', text)  # Remove punctuation and special characters
#     text = re.sub(r'\d+', '', text)  # Remove numbers
#     text = re.sub(r'http\S+', '', text)  # Remove URLs
    
#     # Tokenize and remove stopwords
#     tokens = word_tokenize(text.lower())  # Convert to lowercase
#     stop_words = set(stopwords.words('english'))
#     tokens = [word for word in tokens if word not in stop_words]
    
#     # Lemmatize tokens using SpaCy
#     doc = nlp(' '.join(tokens))
#     lemmatized_text = ' '.join([token.lemma_ for token in doc])
    
#     # Apply Hugging Face Transformers
#     inputs = tokenizer(lemmatized_text, return_tensors="pt", truncation=False, padding=True)
#     with torch.no_grad():
#         outputs = model(**inputs)
    
#     return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()


def text_processing_for_domain(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])
    
    # Apply Hugging Face Transformers
    inputs = tokenizer(lemmatized_text, return_tensors="pt", truncation=False, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    
    return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()





# # 2. Clustering from ChatGPT
# # Libraries: scikit-learn, sentence-transformers
# # Use sentence embeddings and clustering algorithms to group similar project proposals.
# from bertopic import BERTopic
# def perform_clustering(texts, n_clusters):
#     topic_model = BERTopic(n_topics=n_clusters)
#     topics, _ = topic_model.fit_transform(texts)
#     return topics, topic_model
# # Clustering function call
# clustered_df, cluster_centers = clustering(processed_df)
# Method 1: Sentence Transformers + KMeans

# # 2. Clustering: from Claude
# # Use BERTopic for advanced topic modeling and clustering.
# from bertopic import BERTopic
# def perform_clustering(texts, n_clusters):
#     topic_model = BERTopic(n_topics=n_clusters)
#     topics, _ = topic_model.fit_transform(texts)
#     return topics, topic_model
# # Clustering function call
# problem_clusters, problem_model = perform_clustering(processed_df['Problem_Description'], n_clusters=10)
# location_clusters, location_model = perform_clustering(processed_df['Geographical_Location'], n_clusters=5)
# After this Method 2: BERTopic function, the following need to be done:
# processed_df['Problem_Cluster'] = problem_clusters



# 2. Meta AI Function: Sentence Transformers + Hierarchical Clustering + Silhouette Analysis
# Now this also includes:
# Topic Modeling using BERTopic: Integrated BERTopic to extract representative words for each cluster.
# Cluster Visualization: Added a simple visualization to display the top words in each cluster.
# Hyperparameter Tuning: Include a parameter to adjust the number of top words to display for each cluster.



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
import numpy as np


def extract_problem_domains(df, 
                            text_column='Problem_Description', 
                            cluster_range=(10, 50), 
                            top_words=17,
                            # method='sentence_transformers'
                            method='tfidf_kmeans'
                           ):




    if method == 'sentence_transformers':
        # 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)
        
    elif method == 'tfidf_kmeans':
        # TF-IDF Vectorization and K-Means approach
        vectorizer = TfidfVectorizer(stop_words='english', max_features=5000)
        X = vectorizer.fit_transform(df[text_column])
        
        # Perform K-Means clustering with Silhouette Analysis
        silhouette_scores = []
        for n_clusters in range(cluster_range[0], cluster_range[1] + 1):
            kmeans = KMeans(n_clusters=n_clusters, random_state=42)
            cluster_labels = kmeans.fit_predict(X)
            silhouette_avg = silhouette_score(X, 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 final clustering with optimal number of clusters
        kmeans = KMeans(n_clusters=optimal_n_clusters, random_state=42)
        cluster_labels = kmeans.fit_predict(X)
    
    # # BERTopic approach (commented out)
    # topic_model = BERTopic()
    # topics, _ = topic_model.fit_transform(df[text_column].tolist())
    # topic_model.reduce_topics(df[text_column].tolist(), nr_topics=optimal_n_clusters)
    # cluster_labels = topics
    
    # Get representative words for each cluster
    if method == 'sentence_transformers':
        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)]
    elif method == 'tfidf_kmeans':
        feature_names = vectorizer.get_feature_names_out()
        cluster_representations = {}
        for i in range(optimal_n_clusters):
            center = kmeans.cluster_centers_[i]
            top_word_indices = center.argsort()[-top_words:][::-1]
            top_words = [feature_names[index] for index in top_word_indices]
            cluster_representations[i] = 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]
    
    return df, optimal_n_clusters
    

# Usage
# clustered_df, optimal_n_clusters = optimal_Problem_clustering(processed_df)
# print(f'Optimal number of clusters: {optimal_n_clusters}')













def nlp_pipeline(original_df):
    # Data Preprocessing
    processed_df = data_pre_processing(original_df) # merged_dataset
    

    # Starting the Pipeline for Domain Extraction
    # Apply the text_processing_for_domain function to the DataFrame
    processed_df['Processed_ProblemDescription_forDomainExtraction'] = processed_df['Problem_Description'].apply(text_processing_for_domain)
    # Domain Clustering
    domain_df, optimal_n_clusters = extract_problem_domains(processed_df)

    
    # problem_clusters, problem_model = perform_clustering(processed_df['Problem_Description'], n_clusters=10)
    # location_clusters, location_model = perform_clustering(processed_df['Geographical_Location'], n_clusters=5)
    
    
    
    
    # return processed_df
    return domain_df
    

def process_excel(file):
    try:
        # 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)
        
        # Process 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)
        
        return output_file  # 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
        return error_message  # Return the error message to the user


        




# example_files = ['#TaxDirection (Responses)_BasicExample.xlsx',
#                  '#TaxDirection (Responses)_IntermediateExample.xlsx',
#                  '#TaxDirection (Responses)_UltimateExample.xlsx'
#                 ]

example_files = ['a.xlsx',]


import random
a_random_object = random.choice(["&rArr;", "&rarrtl;", "&Rarr;", "&rarr;"])



# 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 Processed Excel File"),  # File download output
    outputs=gr.File(label="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"),  # File download output
    
    
    # title="Excel File Uploader",
    # title="Upload Excel file containing #TaxDirections &rarr; Download HyperLocal Project Proposals\n",
    
    title = (
        "<p style='font-weight: bold; font-size: 25px; text-align: center;'>"
        "<span style='color: blue;'>Upload Excel file containing #TaxDirections</span> "
        
        # "<span style='color: brown; font-size: 35px;'>&rarr; </span>"
        # "<span style='color: brown; font-size: 35px;'>&rArr;  &rarrtl; &Rarr; </span>"
        "<span style='color: brown; font-size: 35px;'> " +a_random_object +" </span>"
        
        "<span style='color: green;'>Download HyperLocal Project Proposals</span>"
        "</p>\n"
    ),



    description=(
        "<p style='font-size: 12px; color: gray; text-align: center'>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 <a href='https://santanban.github.io/TaxDirection/' target='_blank'>#TaxDirection weblink</a>.</p>"
        
        "<p style='font-weight: bold; font-size: 16px; color: blue;'>Upload an Excel file to process and download the result or use the Example files:</p>"
        "<p style='font-weight: bold; font-size: 15px; color: blue;'>(click on any of them to directly process the file and Download the result)</p>"
        
        "<p style='font-weight: bold; font-size: 14px; color: green; text-align: right;'>Processed output contains a Project Proposal for each Location~Problem paired combination (i.e. each cell).</p>"
        "<p style='font-weight: bold; font-size: 13px; color: green; text-align: right;'>Corresponding Budget Allocation and estimated Project Completion Time are provided in different sheets.</p>"

        
        "<p style='font-size: 12px; color: gray; text-align: center'>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 <a href='https://www.change.org/p/democracy-evolution-ensuring-humanity-s-eternal-existence-through-taxdirection' target='_blank'>support</a>.</p>"
        
    )  # Solid description with right-aligned second sentence

)



# Launch the interface
if __name__ == "__main__":
    interface.launch()