Delete modules

modules/churn_analysis.py

@@ -1,76 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import joblib, os
-
-script_dir = os.path.dirname(os.path.abspath(__file__))
-pipeline_path = os.path.join(script_dir, 'toolkit', 'pipeline.joblib')
-model_path = os.path.join(script_dir, 'toolkit', 'Random Forest Classifier.joblib')
-
-# Load transformation pipeline and model
-pipeline = joblib.load(pipeline_path)
-model = joblib.load(model_path)
-
-# Create a function to calculate TotalCharges
-def calculate_total_charges(tenure, monthly_charges):
-    return tenure * monthly_charges
-
-# Create a function that applies the ML pipeline and makes predictions
-def predict(SeniorCitizen, Partner, Dependents, tenure,
-            InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
-            StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,
-            MonthlyCharges):
-    
-    # Calculate TotalCharges
-    TotalCharges = calculate_total_charges(tenure, MonthlyCharges)
-
-    # Create a dataframe with the input data
-    input_df = pd.DataFrame({
-        'SeniorCitizen': [SeniorCitizen],
-        'Partner': [Partner],
-        'Dependents': [Dependents],
-        'tenure': [tenure],
-        'InternetService': [InternetService],
-        'OnlineSecurity': [OnlineSecurity],
-        'OnlineBackup': [OnlineBackup],
-        'DeviceProtection': [DeviceProtection],
-        'TechSupport': [TechSupport],
-        'StreamingTV': [StreamingTV],
-        'StreamingMovies': [StreamingMovies],
-        'Contract': [Contract],
-        'PaperlessBilling': [PaperlessBilling],
-        'PaymentMethod': [PaymentMethod],
-        'MonthlyCharges': [MonthlyCharges],
-        'TotalCharges': [TotalCharges]
-    })
-
-    # Selecting categorical and numerical columns separately
-    cat_cols = [col for col in input_df.columns if input_df[col].dtype == 'object']
-    num_cols = [col for col in input_df.columns if input_df[col].dtype != 'object']
-    
-    X_processed = pipeline.transform(input_df)
-
-    # Extracting feature names for categorical columns after one-hot encoding
-    cat_encoder = pipeline.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
-    cat_feature_names = cat_encoder.get_feature_names_out(cat_cols)
-
-    # Concatenating numerical and categorical feature names
-    feature_names = num_cols + list(cat_feature_names)
-
-    # Convert X_processed to DataFrame
-    final_df = pd.DataFrame(X_processed, columns=feature_names)
-
-    # Extract the first three columns and remaining columns, then merge
-    first_three_columns = final_df.iloc[:, :3]
-    remaining_columns = final_df.iloc[:, 3:]
-    final_df = pd.concat([remaining_columns, first_three_columns], axis=1)
-
-    # Make predictions using the model
-    prediction_probs = model.predict_proba(final_df)[0]
-    prediction_label = {
-        "Prediction: CHURN 🔴": prediction_probs[1],
-        "Prediction: STAY ✅": prediction_probs[0]
-    }
-
-    return prediction_label
-

modules/financial_analyst.py

@@ -1,105 +0,0 @@
-# import os
-# os.system("pip install gradio==4.44.1")
-# from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
-# import gradio as gr
-# import spacy
-# try:
-#     nlp = spacy.load("en_core_web_sm")
-# except OSError:
-#     from spacy.cli import download
-#     download("en_core_web_sm")
-#     nlp = spacy.load("en_core_web_sm")
-# nlp = spacy.load('en_core_web_sm')
-# nlp.add_pipe('sentencizer')
-
-# def split_in_sentences(text):
-#     doc = nlp(text)
-#     return [str(sent).strip() for sent in doc.sents]
-
-# def make_spans(text,results):
-#     results_list = []
-#     for i in range(len(results)):
-#         results_list.append(results[i]['label'])
-#     facts_spans = []
-#     facts_spans = list(zip(split_in_sentences(text),results_list))
-#     return facts_spans
-    
-# auth_token = os.environ.get("HF_Token")
-
-# ##Speech Recognition
-# asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-# def transcribe(audio):
-#     text = asr(audio)["text"]
-#     return text
-# def speech_to_text(speech):
-#     text = asr(speech)["text"]
-#     return text
-
-# ##Summarization 
-# summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
-# def summarize_text(text):
-#     resp = summarizer(text)
-#     stext = resp[0]['summary_text']
-#     return stext
-
-# ##Fiscal Tone Analysis
-# fin_model= pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
-# def text_to_sentiment(text):
-#     sentiment = fin_model(text)[0]["label"]
-#     return sentiment 
-
-# ##Company Extraction    
-# def fin_ner(text):
-#     api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token)
-#     replaced_spans = api(text)
-#     return replaced_spans    
-
-# ##Fiscal Sentiment by Sentence
-# def fin_ext(text):
-#     results = fin_model(split_in_sentences(text))
-#     return make_spans(text,results)
-    
-# ##Forward Looking Statement
-# def fls(text):
-# #    fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls")
-#     fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token)
-#     results = fls_model(split_in_sentences(text))
-#     return make_spans(text,results) 
-
-
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("## Financial Analyst AI")
-#     gr.Markdown("This project applies AI trained by our financial analysts to analyze earning calls and other financial documents.")
-#     with gr.Row():
-#         with gr.Column():
-#             audio_file = gr.Audio(type="filepath")
-
-#             with gr.Row():
-#                 b1 = gr.Button("Recognize Speech") 
-#             with gr.Row():
-#                 text = gr.Textbox(value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
-#                 b1.click(speech_to_text, inputs=audio_file, outputs=text)
-#             with gr.Row():
-#                 b2 = gr.Button("Summarize Text")
-#                 stext = gr.Textbox()
-#                 b2.click(summarize_text, inputs=text, outputs=stext)     
-#             with gr.Row():
-#                 b3 = gr.Button("Classify Financial Tone")
-#                 label = gr.Label()
-#                 b3.click(text_to_sentiment, inputs=stext, outputs=label)  
-#         with gr.Column():
-#             b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis")
-#             with gr.Row():
-#                 fin_spans = gr.HighlightedText()
-#                 b5.click(fin_ext, inputs=text, outputs=fin_spans)
-#             with gr.Row():
-#                 fls_spans = gr.HighlightedText()
-#                 b5.click(fls, inputs=text, outputs=fls_spans)
-#             with gr.Row():
-#                 b4 = gr.Button("Identify Companies & Locations")
-#                 replaced_spans = gr.HighlightedText()
-#                 b4.click(fin_ner, inputs=text, outputs=replaced_spans)
-    
-# if __name__ == "__main__":
-#     demo.launch()

modules/personal_info_identifier.py

@@ -1,6 +0,0 @@
-# import gradio as gr
-
-# demo = gr.load("models/iiiorg/piiranha-v1-detect-personal-information")
-
-# if __name__ == "__main__":
-#     demo.launch()

modules/sentiment.py

@@ -1,39 +0,0 @@
-# from transformers import pipeline
-# import gradio as gr
-
-# classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment", framework="pt")
-
-# def sentiment_analysis(message, history):
-#     """
-#     Función para analizar el sentimiento de un mensaje.
-#     Retorna la etiqueta de sentimiento con su probabilidad.
-#     """
-#     result = classifier(message)
-#     return f"Sentimiento : {result[0]['label']} (Probabilidad: {result[0]['score']:.2f})"
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("""
-#     # Análisis de Sentimientos
-#     Esta aplicación utiliza un modelo de Machine Learning para analizar el sentimiento de los mensajes ingresados. 
-#     Puede detectar si un texto es positivo, negativo o neutral con su respectiva probabilidad.
-#     """)
-    
-#     chat = gr.ChatInterface(sentiment_analysis, type="messages")
-    
-#     gr.Markdown("""
-#     ---
-#     ### Conéctate conmigo:
-#     [Instagram 📸](https://www.instagram.com/srjosueaaron/)
-    
-#     [TikTok 🎵](https://www.tiktok.com/@srjosueaaron)
-    
-#     [YouTube 🎬](https://www.youtube.com/@srjosueaaron)
-
-#     ---
-#     Demostración de Análisis de Sentimientos usando el modelo de [CardiffNLP](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment).
-    
-#     Desarrollado con ❤️ por [@srjosueaaron](https://www.instagram.com/srjosueaaron/).
-#     """)
-
-# if __name__ == "__main__":
-#     demo.launch()

modules/translator.py

@@ -1,35 +0,0 @@
-import gradio as gr
-from pydantic import BaseModel, Field
-from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.output_parsers import PydanticOutputParser
-from langchain_openai import ChatOpenAI
-
-chat = ChatOpenAI()
-
-# Define the Pydantic Model (updated for Pydantic v2)
-class TextTranslator(BaseModel):
-    output: str = Field(description="Python string containing the output text translated in the desired language")
-
-# Use PydanticOutputParser (no need for response_schemas)
-output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
-
-def text_translator(input_text: str, language: str) -> str:
-    human_template = """Enter the text that you want to translate: 
-                {input_text}, and enter the language that you want it to translate to {language}."""
-    human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-    chat_prompt = ChatPromptTemplate.from_messages([human_message_prompt])
-    prompt = chat_prompt.format_prompt(input_text=input_text, language=language)
-    messages = prompt.to_messages()
-    response = chat(messages=messages)
-    
-    # Use output_parser to parse the response
-    output = output_parser.parse(response.content)
-    return output.output
-
-def text_translator_ui():
-    gr.Markdown("### Text Translator\nTranslate text into any language using AI.")
-    input_text = gr.Textbox(label="Enter the text that you want to translate")
-    input_lang = gr.Textbox(label="Enter the language that you want it to translate to", placeholder="Example: Hindi, French, Bengali, etc.")
-    output_text = gr.Textbox(label="Translated text")
-    translate_button = gr.Button("Translate")
-    translate_button.click(fn=text_translator, inputs=[input_text, input_lang], outputs=output_text)