app.py

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):
    consoleMessage_and_Print("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
        consoleMessage_and_Print("Data pre-processing completed.")
        return merged_dataset
        
    except Exception as e:
        consoleMessage_and_Print(f"Error during data pre-processing: {str(e)}")
        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=(6, 8), 
                            top_words=7):
    consoleMessage_and_Print("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]
    
    consoleMessage_and_Print("Problem Domain Extraction completed. Returning from Problem Domain Extraction function.")
    return df, optimal_n_clusters, cluster_representations


















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_column1='Processed_LocationText_forClustering', # Extracted through NLP
                              text_column2='Geographical_Location', # User Input
                              cluster_range=(3, 5),
                              top_words=3):
    # Combine the two text columns
    text_column = "Combined_Location_Text"
    df[text_column] = df[text_column1] + ' ' + df[text_column2]
    
    consoleMessage_and_Print("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]

    df = df.drop(text_column, axis=1)
    consoleMessage_and_Print("Location Clustering completed.")    
    return df, optimal_n_clusters, cluster_representations






















def create_cluster_dataframes(processed_df):
    # Create a dataframe for Financial Weights
    budget_cluster_df = processed_df.pivot_table(
        values='Financial_Weight', 
        index='Location_Cluster', 
        columns='Problem_Cluster', 
        aggfunc='sum', 
        fill_value=0)

    # Create a dataframe for Problem Descriptions
    problem_cluster_df = processed_df.groupby(['Location_Cluster', 'Problem_Cluster'])['Problem_Description'].apply(list).unstack()

    return budget_cluster_df, problem_cluster_df

from transformers import GPTNeoForCausalLM, GPT2Tokenizer
def generate_project_proposal(prompt):
    print("Trying to access gpt-neo-1.3B")
    print("prompt: \t", prompt)
    
    # Generate the proposal
    model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
    tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
    
        
    try:
        # input_ids = tokenizer.encode(prompt, return_tensors="pt")
        # Truncate the prompt to fit within the model's input limits
        max_input_length = 2048  # Adjust as per your model's limit
        input_ids = tokenizer.encode(prompt, return_tensors="pt", truncation=True, max_length=max_input_length)
        
        
        print("Input IDs shape:", input_ids.shape)
        output = model.generate(
            input_ids,
            # max_length=300,
            max_new_tokens=500,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            temperature=0.5,
            pad_token_id=tokenizer.eos_token_id  # Ensure padding with EOS token
            )
        print("Output shape:", output.shape)
        
        proposal = tokenizer.decode(output[0], skip_special_tokens=True)
        if "Project Proposal:" in proposal:
            proposal = proposal.split("Project Proposal:", 1)[1].strip()
        else:
            proposal = proposal.strip()
    
        # print("Successfully accessed gpt-neo-1.3B and returning")
        print("Generated Proposal: ", proposal)
        return proposal
    except Exception as e:
        print("Error generating proposal:", str(e))
        return "Hyper-local Sustainability Projects would lead to Longevity of the self and Prosperity of the community. Therefore UNSDGs coupled with Longevity initiatives should be focused upon."




import copy



def create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters):
    consoleMessage_and_Print("\n Starting function: create_project_proposals")
    proposals = {}

    sanban_debug = False
    
    for loc in budget_cluster_df.index:
        consoleMessage_and_Print(f"\n loc: {loc}")
        
        for prob in budget_cluster_df.columns:
            consoleMessage_and_Print(f"\n prob: {prob}")
            
            location = ", ".join([item.strip() for item in location_clusters[loc] if item])  # Clean and join
            problem_domain = ", ".join([item.strip() for item in problem_clusters[prob] if item])  # Clean and join
            shuffled_descriptions = copy.deepcopy(problem_cluster_df.loc[loc, prob])
            # Create a deep copy of the problem descriptions, shuffle it, and join the first 10

            
            print("location: ", location)
            print("problem_domain: ", problem_domain)
            print("problem_descriptions: ", shuffled_descriptions)
            
            # Check if problem_descriptions is valid (not NaN and not an empty list)
            if isinstance(shuffled_descriptions, list) and shuffled_descriptions:
                # print(f"\nGenerating proposal for location: {location}, problem domain: {problem_domain}")
                consoleMessage_and_Print(f"Generating PP")
                
                random.shuffle(shuffled_descriptions)
                # Prepare the prompt
                # problems_summary = "; \n".join(problem_descriptions)  # Join all problem descriptions
                # problems_summary = "; \n".join(problem_descriptions[:3])  # Limit to first 3 for brevity
                problems_summary = "; \n".join(shuffled_descriptions[:3])
                
                
                # prompt = f"Generate a solution oriented project proposal for the following:\n\nLocation: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
                # prompt = f"Generate a solution-oriented project proposal for the following public problem (only output the proposal):\n\n Geographical/Digital Location: {location}\nProblem Category: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
                prompt = f"Generate a single solution-oriented project proposal bespoke to the following Location~Domain cluster of public problems:\n\n Geographical/Digital Location: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal: <only output this proposal>"

                proposal = generate_project_proposal(prompt)
                # Check if proposal is valid
                if isinstance(proposal, str) and proposal.strip():  # Valid string that's not empty
                    proposals[(loc, prob)] = proposal
                    
                    sanban_debug = True
                    break
                    
            else:
                print(f"Skipping empty problem descriptions for location: {location}, problem domain: {problem_domain}")
        
        if sanban_debug:
            break
    
    return proposals
        

# def create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters):
#     print("\n Starting function: create_project_proposals")
#     console_messages.append("\n Starting function: create_project_proposals")
#     proposals = {}
#     for loc in budget_cluster_df.index:
#         print("\n loc: ", loc)
#         console_messages.append(f"\n loc: {loc}")
        
#         for prob in budget_cluster_df.columns:
#             console_messages.append(f"\n prob: {prob}")
#             print("\n prob: ", prob)
            
#             location = ", ".join([item.strip() for item in location_clusters[loc] if item])  # Clean and join
#             problem_domain = ", ".join([item.strip() for item in problem_clusters[prob] if item])  # Clean and join
#             problem_descriptions = problem_cluster_df.loc[loc, prob]
            
#             print("location: ",location)
#             print("problem_domain: ",problem_domain)
#             print("problem_descriptions: ",problem_descriptions)
            
#             if problem_descriptions:# and not pd.isna(problem_descriptions):
                
#                 print(f"\nGenerating proposal for location: {location}, problem domain: {problem_domain}")
                
#                 # console_messages.append(f"\nGenerating proposal for location: {location}, problem domain: {problem_domain}")
                
#                 # Prepare the prompt
#                 problems_summary = "; \n".join(problem_descriptions[:3])  # Limit to first 3 for brevity                
#                 # problems_summary = "; ".join(problem_descriptions)
#                 # prompt = f"Generate a project proposal for the following:\n\nLocation: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\nBudget: ${financial_weight:.2f}\n\nProject Proposal:"
#                 prompt = f"Generate a solution oriented project proposal for the following:\n\nLocation: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
                
#                 proposal = generate_project_proposal(prompt)
#                 proposals[(loc, prob)] = proposal
#                 print("Generated Proposal: ", proposal)
                
#             else:
#                 print(f"Skipping empty problem descriptions for location: {location}, problem domain: {problem_domain}")
    
#     return proposals



    

# def create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters):
#     print("\n Starting function: create_project_proposals")
#     console_messages.append("\n Starting function: create_project_proposals")
#     proposals = {}
#     for loc in budget_cluster_df.index:
#         for prob in budget_cluster_df.columns:
#             location = ", ".join(location_clusters[loc])
#             problem_domain = ", ".join(problem_clusters[prob])
#             problem_descriptions = problem_cluster_df.loc[loc, prob]
            
#             if problem_descriptions:
#                 proposal = generate_project_proposal(
#                     problem_descriptions,
#                     location, 
#                     problem_domain)
#                 proposals[(loc, prob)] = proposal
#     console_messages.append("\n Exiting function: create_project_proposals")
#     return proposals



















def nlp_pipeline(original_df):
    consoleMessage_and_Print("Starting NLP pipeline...")
    
    # Data Preprocessing
    processed_df = data_pre_processing(original_df) # merged_dataset

    # Starting the Pipeline for Domain Extraction
    consoleMessage_and_Print("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)
    
    consoleMessage_and_Print("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, problem_clusters = extract_problem_domains(processed_df)
        consoleMessage_and_Print(f"Optimal clusters for Domain extraction: {optimal_n_clusters}")
    except Exception as e:
        consoleMessage_and_Print(f"Error in extract_problem_domains: {str(e)}")
    consoleMessage_and_Print("NLP pipeline for Problem Domain extraction completed.")

    
    consoleMessage_and_Print("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, location_clusters = extract_location_clusters(processed_df)
        consoleMessage_and_Print(f"Optimal clusters for Location extraction: {optimal_n_clusters}")
    except Exception as e:
        consoleMessage_and_Print(f"Error in extract_location_clusters: {str(e)}")
    consoleMessage_and_Print("NLP pipeline for location extraction completed.")
    
    
    # Create cluster dataframes
    budget_cluster_df, problem_cluster_df = create_cluster_dataframes(processed_df)

    print("Clustering Done...")
    # return processed_df, budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters

    print("\n location_clusters_1: ", location_clusters)
    print("\n problem_clusters_1: ", problem_clusters)
    # # Generate project proposals
    # location_clusters = dict(enumerate(processed_df['Location_Category_Words'].unique()))
    # problem_clusters = dict(enumerate(processed_df['Problem_Category_Words'].unique()))
    # print("\n location_clusters_2: ", location_clusters)
    # print("\n problem_clusters_2: ", problem_clusters)
    project_proposals = create_project_proposals(budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters)

    consoleMessage_and_Print("NLP pipeline completed.")
    return processed_df, budget_cluster_df, problem_cluster_df, project_proposals, location_clusters, problem_clusters
    




    
    
    
console_messages = []
def consoleMessage_and_Print(some_text = ""):
    console_messages.append(some_text)
    print(some_text)




def process_excel(file):
    consoleMessage_and_Print("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
        consoleMessage_and_Print("Processing the DataFrame...")
        processed_df, budget_cluster_df, problem_cluster_df, project_proposals, location_clusters, problem_clusters = nlp_pipeline(df)
        # processed_df, budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters  = nlp_pipeline(df)

        consoleMessage_and_Print("Error was here")
        #This code first converts the dictionary to a DataFrame with a single column for the composite key.
        #Then, it splits the composite key into separate columns for Location_Cluster and Problem_Cluster.
        #Finally, it reorders the columns and writes the DataFrame to an Excel sheet.
        try: # Meta AI Solution
            # Convert project_proposals dictionary to DataFrame
            project_proposals_df = pd.DataFrame(list(project_proposals.items()), columns=['Location_Cluster_Problem_Cluster', 'Solutions Proposed'])
            # consoleMessage_and_Print("CheckPoint 1")
            
            # Split the composite key into separate columns
            project_proposals_df[['Location_Cluster', 'Problem_Cluster']] = project_proposals_df['Location_Cluster_Problem_Cluster'].apply(pd.Series)
            # consoleMessage_and_Print("CheckPoint 2")
            
            # Drop the composite key column
            project_proposals_df.drop('Location_Cluster_Problem_Cluster', axis=1, inplace=True)
            # consoleMessage_and_Print("CheckPoint 3")
    
            # Reorder the columns
            project_proposals_df = project_proposals_df[['Location_Cluster', 'Problem_Cluster', 'Solutions Proposed']]
            # consoleMessage_and_Print("CheckPoint 4")
            
        except Exception as e:
            consoleMessage_and_Print("Meta AI Solution did not work, trying CHATGPT solution")
            try:
                
                # Convert project_proposals dictionary to DataFrame
                project_proposals_df = pd.DataFrame.from_dict(
                    proposals, orient='index', columns=['Solutions Proposed']
                )
                
                # If the index is a tuple, it automatically becomes a MultiIndex, so we handle naming correctly:
                if isinstance(project_proposals_df.index, pd.MultiIndex):
                    project_proposals_df.index.names = ['Location_Cluster', 'Problem_Cluster']
                else:
                    # If for some reason it's not a MultiIndex, we name it appropriately
                    project_proposals_df.index.name = 'Cluster'
                
                # Reset index to have Location_Cluster and Problem_Cluster as columns
                project_proposals_df.reset_index(inplace=True)
            
            except Exception as e:
                print(e)

            






        # ### Convert project_proposals dictionary to DataFrame
        # project_proposals_df = pd.DataFrame.from_dict(project_proposals, orient='index', columns=['Solutions Proposed'])
        # project_proposals_df.index.names = ['Location_Cluster', 'Problem_Cluster']
        # project_proposals_df.reset_index(inplace=True)

        




    

        consoleMessage_and_Print("Creating the Excel file.")
        output_filename = "OutPut_PPs.xlsx"
        with pd.ExcelWriter(output_filename) as writer:
            processed_df.to_excel(writer, sheet_name='Input_Processed', index=False)
            budget_cluster_df.to_excel(writer, sheet_name='Financial_Weights')
            problem_cluster_df.to_excel(writer, sheet_name='Problem_Descriptions')
            try:
                project_proposals_df.to_excel(writer, sheet_name='Project_Proposals', index=False)
            except Exception as e:
                consoleMessage_and_Print("Error during Project Proposal excelling at the end")
            


                
            # Ensure location_clusters and problem_clusters are in DataFrame format
            if isinstance(location_clusters, pd.DataFrame):
                location_clusters.to_excel(writer, sheet_name='Location_Clusters', index=False)
            else:
                consoleMessage_and_Print("Converting Location Clusters to df")
                pd.DataFrame(location_clusters).to_excel(writer, sheet_name='Location_Clusters', index=False)
            
            if isinstance(problem_clusters, pd.DataFrame):
                problem_clusters.to_excel(writer, sheet_name='Problem_Clusters', index=False)
            else:
                consoleMessage_and_Print("Converting Problem Clusters to df")
                pd.DataFrame(problem_clusters).to_excel(writer, sheet_name='Problem_Clusters', index=False)


        
        consoleMessage_and_Print("Processing completed. Ready for download.")
        return output_filename, "\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
        consoleMessage_and_Print(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 = []
# example_files.append('#TaxDirection (Responses)_BasicExample.xlsx')
example_files.append('#TaxDirection (Responses)_IntermediateExample.xlsx')
# example_files.append('#TaxDirection (Responses)_UltimateExample.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 the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"),  # File download output
        gr.Textbox(label="Console Messages", lines=10, interactive=False)  # Console messages 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()