app.py

# This cell will generate a unified Gradio app.py content based on all 5 apps provided

import os
import gradio as gr
import pandas as pd
import numpy as np
import joblib
import spacy
from transformers import pipeline
from langchain_core.pydantic import BaseModel, Field
from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain_openai import ChatOpenAI

# ---------------- Text Translator ---------------- #

chat = ChatOpenAI()

class TextTranslator(BaseModel):
    output: str = Field(description="Translated output text")

output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
format_instructions = output_parser.get_format_instructions()

def text_translator(input_text: str, language: str) -> str:
    human_template = f"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)
    return output.output

# ---------------- Sentiment Analysis ---------------- #

sentiment_classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")

def sentiment_analysis(message, history):
    result = sentiment_classifier(message)
    return f"Sentiment: {result[0]['label']} (Probability: {result[0]['score']:.2f})"

# ---------------- Financial Analyst ---------------- #

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 = [res['label'] for res in results]
    return list(zip(split_in_sentences(text), results_list))

auth_token = os.environ.get("HF_Token")

asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
def speech_to_text(speech):
    return asr(speech)["text"]

summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
def summarize_text(text):
    return summarizer(text)[0]['summary_text']

fin_model = pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
def text_to_sentiment(text):
    return fin_model(text)[0]["label"]

def fin_ner(text):
    api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token)
    return api(text)

def fin_ext(text):
    results = fin_model(split_in_sentences(text))
    return make_spans(text, results)

def fls(text):
    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)

# ---------------- Personal Information Identifier ---------------- #

def detect_personal_info(text):
    pii_model = gr.Interface.load("models/iiiorg/piiranha-v1-detect-personal-information")
    return pii_model(text)

# ---------------- Customer Churn ---------------- #

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')

pipeline_churn = joblib.load(pipeline_path)
model_churn = joblib.load(model_path)

def calculate_total_charges(tenure, monthly_charges):
    return tenure * monthly_charges

def predict_churn(SeniorCitizen, Partner, Dependents, tenure,
    InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
    StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,
    MonthlyCharges):

    TotalCharges = calculate_total_charges(tenure, MonthlyCharges)
    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]
    })

    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_churn.transform(input_df)

    cat_encoder = pipeline_churn.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
    cat_feature_names = cat_encoder.get_feature_names_out(cat_cols)
    feature_names = num_cols + list(cat_feature_names)
    final_df = pd.DataFrame(X_processed, columns=feature_names)

    first_three_columns = final_df.iloc[:, :3]
    remaining_columns = final_df.iloc[:, 3:]
    final_df = pd.concat([remaining_columns, first_three_columns], axis=1)

    prediction_probs = model_churn.predict_proba(final_df)[0]
    return {
        "Prediction: CHURN 🔴": prediction_probs[1],
        "Prediction: STAY ✅": prediction_probs[0]
    }

# ---------------- Interface ---------------- #

with gr.Blocks() as app:
    with gr.Tab("Text Translator"):
        input_text = gr.Textbox(label="Enter text to translate")
        lang = gr.Textbox(label="Target language (e.g., Hindi, French)")
        output_text = gr.Textbox(label="Translated text")
        gr.Button("Translate").click(fn=text_translator, inputs=[input_text, lang], outputs=output_text)

    with gr.Tab("Sentiment Analysis"):
        gr.ChatInterface(sentiment_analysis)

    with gr.Tab("Financial Analyst"):
        audio_input = gr.Audio(source="microphone", type="filepath")
        text = gr.Textbox(label="Transcribed Text")
        gr.Button("Transcribe").click(fn=speech_to_text, inputs=audio_input, outputs=text)
        stext = gr.Textbox(label="Summary")
        gr.Button("Summarize").click(fn=summarize_text, inputs=text, outputs=stext)
        gr.Button("Financial Tone").click(fn=text_to_sentiment, inputs=stext, outputs=gr.Label())
        gr.Button("NER").click(fn=fin_ner, inputs=text, outputs=gr.HighlightedText())
        gr.Button("Tone per sentence").click(fn=fin_ext, inputs=text, outputs=gr.HighlightedText())
        gr.Button("Forward-looking").click(fn=fls, inputs=text, outputs=gr.HighlightedText())

    with gr.Tab("Personal Information Identifier"):
        pii_input = gr.Textbox(label="Enter text to analyze")
        pii_output = gr.Textbox(label="Detected Personal Info")
        gr.Button("Detect").click(fn=detect_personal_info, inputs=pii_input, outputs=pii_output)

    with gr.Tab("Customer Churn"):
        churn_inputs = [
            gr.Radio(['Yes', 'No'], label="SeniorCitizen"),
            gr.Radio(['Yes', 'No'], label="Partner"),
            gr.Radio(['No', 'Yes'], label="Dependents"),
            gr.Slider(1, 73, step=1, label="Tenure (Months)"),
            gr.Radio(['DSL', 'Fiber optic', 'No Internet'], label="InternetService"),
            gr.Radio(['No', 'Yes'], label="OnlineSecurity"),
            gr.Radio(['No', 'Yes'], label="OnlineBackup"),
            gr.Radio(['No', 'Yes'], label="DeviceProtection"),
            gr.Radio(['No', 'Yes'], label="TechSupport"),
            gr.Radio(['No', 'Yes'], label="StreamingTV"),
            gr.Radio(['No', 'Yes'], label="StreamingMovies"),
            gr.Radio(['Month-to-month', 'One year', 'Two year'], label="Contract"),
            gr.Radio(['Yes', 'No'], label="PaperlessBilling"),
            gr.Radio(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label="PaymentMethod"),
            gr.Slider(18.40, 118.65, label="MonthlyCharges")
        ]
        churn_output = gr.Label(label="Churn Prediction")
        gr.Button("Predict").click(fn=predict_churn, inputs=churn_inputs, outputs=churn_output)

app.launch()