app.py

import gradio as gr
import joblib
import re
import pandas as pd
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

# 1. Translator
class TextTranslator(BaseModel):
    output: str = Field(description="Python string containing the output text translated in the desired language")
    
output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
format_instructions = output_parser.get_format_instructions()

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}. {format_instructions}"""
    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, format_instructions = format_instructions)
    
    messages = prompt.to_messages()
    
    response = chat(messages = messages)
    
    output = output_parser.parse(response.content)
    
    output_text = output.output
    
    return output_text

# 2. Sentiment Analysis
classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")

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})"

# 3. Financial Analyst (LangChain with OpenAI, requires API key)
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) 


# 4. Personal Info Detection
def detect_pii(text):
    pii_patterns = {
        "email": r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+",
        "phone": r"\+?\d[\d\-\s]{8,}\d",
        "credit_card": r"\b(?:\d[ -]*?){13,16}\b"
    }
    found = {}
    for label, pattern in pii_patterns.items():
        matches = re.findall(pattern, text)
        if matches:
            found[label] = matches
    return found or "No personal information found."

# 5. Telco Customer Churn Prediction
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

input_interface = []

# Gradio UI setup
with gr.Blocks() as demo:
    with gr.Tab("Translator"):
        gr.HTML("<h1 align = 'center'> Text Translator </h1>")
    gr.HTML("<h4 align = 'center'> Translate to any language </h4>")
    
    inputs = [gr.Textbox(label = "Enter the text that you want to translate"), gr.Textbox(label = "Enter the language that you want it to translate to", placeholder = "Example : Hindi,French,Bengali,etc")]
    generate_btn = gr.Button(value = 'Generate')
    outputs  = [gr.Textbox(label = "Translated text")]
    generate_btn.click(fn = text_translator, inputs= inputs, outputs = outputs)

    with gr.Tab("Sentiment Analysis"):
        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/).
    """)

    with gr.Tab("Financial Analyst"):
        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.inputs.Audio(source="microphone", 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)

    with gr.Tab("PII Detector"):
        pii_input = gr.Textbox(label="Text")
        pii_output = gr.JSON(label="Detected PII")
        pii_button = gr.Button("Detect")
        pii_button.click(fn=detect_pii, inputs=pii_input, outputs=pii_output)

    with gr.Tab("Telco Churn Predictor"):
        Title = gr.Label('Customer Churn Prediction App')

    with gr.Row():
        Title

    with gr.Row():
        gr.Markdown("This app predicts likelihood of a customer to leave or stay with the company")

    with gr.Row():
        with gr.Column():
            input_interface_column_1 = [
                gr.components.Radio(['Yes', 'No'], label="Are you a Seniorcitizen?"),
                gr.components.Radio(['Yes', 'No'], label='Do you have Partner?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have any Dependents?'),
                gr.components.Slider(label='Enter lenghth of Tenure in Months', minimum=1, maximum=73, step=1),
                gr.components.Radio(['DSL', 'Fiber optic', 'No Internet'], label='What is your Internet Service?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have Online Security?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have Online Backup?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have Device Protection?')
            ]

        with gr.Column():
            input_interface_column_2 = [
                gr.components.Radio(['No', 'Yes'], label='Do you have Tech Support?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have Streaming TV?'),
                gr.components.Radio(['No', 'Yes'], label='Do you have Streaming Movies?'),
                gr.components.Radio(['Month-to-month', 'One year', 'Two year'], label='What is your Contract Type?'),
                gr.components.Radio(['Yes', 'No'], label='Do you prefer Paperless Billing?'),
                gr.components.Radio(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label='Which PaymentMethod do you prefer?'),
                gr.components.Slider(label="Enter monthly charges", minimum=18.40, maximum=118.65)
            ]

    with gr.Row():
        input_interface.extend(input_interface_column_1)
        input_interface.extend(input_interface_column_2)

    with gr.Row():
        predict_btn = gr.Button('Predict')
        output_interface = gr.Label(label="churn")
    
    with gr.Accordion("Open for information on inputs", open=False):
        gr.Markdown("""This app receives the following as inputs and processes them to return the prediction on whether a customer, will churn or not.
                    
                    - SeniorCitizen: Whether a customer is a senior citizen or not
                    - Partner: Whether the customer has a partner or not (Yes, No)
                    - Dependents: Whether the customer has dependents or not (Yes, No)
                    - Tenure: Number of months the customer has stayed with the company
                    - InternetService: Customer's internet service provider (DSL, Fiber Optic, No)
                    - OnlineSecurity: Whether the customer has online security or not (Yes, No, No Internet)
                    - OnlineBackup: Whether the customer has online backup or not (Yes, No, No Internet)
                    - DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)
                    - TechSupport: Whether the customer has tech support or not (Yes, No, No internet)
                    - StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)
                    - StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No Internet service)
                    - Contract: The contract term of the customer (Month-to-Month, One year, Two year)
                    - PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)
                    - Payment Method: The customer's payment method (Electronic check, mailed check, Bank transfer(automatic), Credit card(automatic))
                    - MonthlyCharges: The amount charged to the customer monthly
                    """)
        
    predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)

demo.launch()