Update app.py

app.py

@@ -1,187 +1,32 @@
 import gradio as gr
-from transformers import pipeline
-import os
-import pandas as pd
-import numpy as np
-import joblib
-import spacy
-try:
-    spacy.load("en_core_web_sm")
-except:
-    from spacy.cli import download
-    download("en_core_web_sm")
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.output_parsers import PydanticOutputParser
-from langchain_openai import ChatOpenAI
+from modules.sentiment import sentiment_function  # โมดูลวิเคราะห์ความรู้สึก
+from modules.financial_analyst import financial_analysis_function  # โมดูลวิเคราะห์การเงิน
+from modules.translator import text_translator  # โมดูลแปลภาษา
+from modules.personal_info_identifier import identify_personal_info  # โมดูลตรวจสอบข้อมูลส่วนบุคคล
+from modules.churn_analysis import churn_prediction  # โมดูลทำนายการเลิกบริการ
+
+def run_all_functions(input_text):
+    sentiment_result = sentiment_function(input_text)
+    financial_result = financial_analysis_function(input_text)
+    translation_result = text_translator(input_text, "English")
+    personal_info_result = identify_personal_info(input_text)
+    churn_result = churn_prediction(input_text)
+    return sentiment_result, financial_result, translation_result, personal_info_result, churn_result
 
-# Set up models for each app
-chat = ChatOpenAI()
-classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
-fin_model = pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
-fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls")
-
-# --- Translator App ---
-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
-
-# --- Sentiment Analysis App ---
-def sentiment_analysis(message, history):
-    result = classifier(message)
-    return f"Sentimiento : {result[0]['label']} (Probabilidad: {result[0]['score']:.2f})"
-
-# --- Financial Analyst App ---
-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 = [results[i]['label'] for i in range(len(results))]
-    return list(zip(split_in_sentences(text), results_list))
-
-def summarize_text(text):
-    resp = summarizer(text)
-    return resp[0]['summary_text']
-
-def text_to_sentiment(text):
-    sentiment = fin_model(text)[0]["label"]
-    return sentiment
-
-def fin_ext(text):
-    results = fin_model(split_in_sentences(text))
-    return make_spans(text, results)
-
-def fls(text):
-    results = fls_model(split_in_sentences(text))
-    return make_spans(text, results)
-
-# --- Customer Churn App ---
-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 = joblib.load(pipeline_path)
-model = joblib.load(model_path)
-
-def calculate_total_charges(tenure, monthly_charges):
-    return tenure * monthly_charges
-
-def predict(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]
-    })
-
-    X_processed = pipeline.transform(input_df)
-    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']
-
-    cat_encoder = pipeline.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)
-    final_df = pd.concat([final_df.iloc[:, 3:], final_df.iloc[:, :3]], axis=1)
-
-    prediction_probs = model.predict_proba(final_df)[0]
-    prediction_label = {
-        "Prediction: CHURN 🔴": prediction_probs[1],
-        "Prediction: STAY ✅": prediction_probs[0]
-    }
-    return prediction_label
-
-# --- Personal Information Detection App ---
-import gradio as gr
-gr.load("models/iiiorg/piiranha-v1-detect-personal-information").launch()
-
-# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# All-in-One AI Apps")
-    with gr.Tab("Text Translator"):
-        gr.HTML("<h1 align='center'>Text Translator</h1>")
-        text_input = gr.Textbox(label="Enter Text")
-        language_input = gr.Textbox(label="Enter Language")
-        translate_btn = gr.Button("Translate")
-        translated_text = gr.Textbox(label="Translated Text")
-        translate_btn.click(fn=text_translator, inputs=[text_input, language_input], outputs=translated_text)
+    gr.Markdown("# Multi-Function App")
+    gr.Markdown("### Combine various AI tasks into one platform")
 
-    with gr.Tab("Sentiment Analysis"):
-        gr.Markdown("# Sentiment Analysis")
-        sentiment_input = gr.Textbox(label="Enter Message")
-        sentiment_output = gr.Textbox(label="Sentiment")
-        sentiment_btn = gr.Button("Analyze Sentiment")
-        sentiment_btn.click(fn=sentiment_analysis, inputs=sentiment_input, outputs=sentiment_output)
+    text_input = gr.Textbox(label="Enter text to analyze")
 
-    with gr.Tab("Financial Analyst"):
-        gr.Markdown("# Financial Analyst AI")
-        financial_input = gr.Textbox(label="Enter Text for Financial Analysis")
-        summarize_btn = gr.Button("Summarize")
-        sentiment_btn = gr.Button("Classify Financial Tone")
-        financial_output = gr.Textbox(label="Analysis Results")
-        summarize_btn.click(fn=summarize_text, inputs=financial_input, outputs=financial_output)
-        sentiment_btn.click(fn=text_to_sentiment, inputs=financial_input, outputs=financial_output)
+    sentiment_output = gr.Textbox(label="Sentiment Analysis")
+    financial_output = gr.Textbox(label="Financial Analysis")
+    translation_output = gr.Textbox(label="Translation Result")
+    personal_info_output = gr.Textbox(label="Personal Info Detection")
+    churn_output = gr.Textbox(label="Customer Churn Prediction")
 
-    with gr.Tab("Customer Churn Prediction"):
-        gr.Markdown("# Customer Churn Prediction")
-        churn_inputs = [
-            gr.Radio(['Yes', 'No'], label="Are you a Seniorcitizen?"),
-            gr.Radio(['Yes', 'No'], label="Do you have a Partner?"),
-            gr.Radio(['No', 'Yes'], label="Do you have Dependents?"),
-            gr.Slider(label="Tenure (Months)", minimum=1, maximum=73),
-            gr.Radio(['DSL', 'Fiber optic', 'No Internet'], label="Internet Service"),
-            gr.Radio(['No', 'Yes'], label="Online Security"),
-            gr.Radio(['No', 'Yes'], label="Online Backup"),
-            gr.Radio(['No', 'Yes'], label="Device Protection"),
-            gr.Radio(['No', 'Yes'], label="Tech Support"),
-            gr.Radio(['No', 'Yes'], label="Streaming TV"),
-            gr.Radio(['No', 'Yes'], label="Streaming Movies"),
-            gr.Radio(['Month-to-month', 'One year', 'Two year'], label="Contract Type"),
-            gr.Radio(['Yes', 'No'], label="Paperless Billing"),
-            gr.Radio(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label="Payment Method"),
-            gr.Slider(label="Monthly Charges", minimum=18.4, maximum=118.65)
-        ]
-        churn_output = gr.Label(label="Churn Prediction")
-        churn_btn = gr.Button("Predict Churn")
-        churn_btn.click(fn=predict, inputs=churn_inputs, outputs=churn_output)
+    run_button = gr.Button("Run All Functions")
 
-    with gr.Tab("Personal Information Detection"):
-        gr.HTML("<h1 align='center'>Personal Information Detection</h1>")
-        gr.Interface.load("models/iiiorg/piiranha-v1-detect-personal-information").launch()
+    run_button.click(fn=run_all_functions, inputs=text_input, outputs=[sentiment_output, financial_output, translation_output, personal_info_output, churn_output])
 
-demo.launch(share=True)
+demo.launch()