Update app.py

app.py

@@ -1,170 +1,92 @@
-# app.py
-
-import os
 import gradio as gr
-import pandas as pd
-import numpy as np
 import joblib
-import spacy
-
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.output_parsers import PydanticOutputParser
+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 transformers import pipeline
-
-### 1. Translator ###
-chat = ChatOpenAI()
-
-class TextTranslator(BaseModel):
-    output: str = Field(description="Translated output")
-
-output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
-format_instructions = output_parser.get_format_instructions()
-
-def text_translator(input_text: str, language: str) -> str:
-    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_prompt = HumanMessagePromptTemplate.from_template(template)
-    prompt = ChatPromptTemplate.from_messages([human_prompt]).format_prompt(
-        input_text=input_text, language=language, format_instructions=format_instructions)
-    response = chat(messages=prompt.to_messages())
-    return output_parser.parse(response.content).output
-
-### 2. Sentiment ###
-sentiment_model = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")
-def sentiment_analysis(message, history):
-    result = sentiment_model(message)
-    return f"Sentimiento : {result[0]['label']} (Probabilidad: {result[0]['score']:.2f})"
-
-### 3. Financial Analyst ###
-nlp = spacy.load('en_core_web_sm')
-nlp.add_pipe('sentencizer')
-def split_in_sentences(text):
-    return [str(sent).strip() for sent in nlp(text).sents]
-
-def make_spans(text, results):
-    labels = [r['label'] for r in results]
-    return list(zip(split_in_sentences(text), labels))
-
-auth_token = os.environ.get("HF_Token")
-
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-def speech_to_text(audio):
-    return asr(audio)["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')
-def text_to_sentiment(text):
-    return fin_model(text)[0]["label"]
-
-def fin_ner(text):
-    return gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token)(text)
-
-def fin_ext(text):
-    return make_spans(text, fin_model(split_in_sentences(text)))
-
-def fls(text):
-    model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token)
-    return make_spans(text, model(split_in_sentences(text)))
-
-### 4. Personal Info Detection ###
-def detect_personal_info(text):
-    model = gr.Interface.load("iiiorg/piiranha-v1-detect-personal-information")
-    return model(text)
-
-### 5. 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_model = 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_model.transform(input_df)
-    cat_encoder = pipeline_model.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
-    feature_names = [*input_df.select_dtypes(exclude='object').columns, *cat_encoder.get_feature_names_out()]
-    final_df = pd.DataFrame(X_processed, columns=feature_names)
-    pred_probs = model.predict_proba(final_df)[0]
-    return {
-        "Prediction: CHURN 🔴": pred_probs[1],
-        "Prediction: STAY ✅": pred_probs[0]
+from langchain_core.output_parsers import StrOutputParser
+
+# 1. Translator
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+
+def translate_text(text):
+    return translator(text)[0]['translation_text']
+
+# 2. Sentiment Analysis
+sentiment = pipeline("sentiment-analysis")
+
+def analyze_sentiment(text):
+    return sentiment(text)[0]
+
+# 3. Financial Analyst (LangChain with OpenAI, requires API key)
+def financial_analysis(text, api_key):
+    chat = ChatOpenAI(api_key=api_key)
+    template = "Analyze the financial context of this text:\n\n{text}"
+    prompt = PromptTemplate.from_template(template)
+    chain = LLMChain(llm=chat, prompt=prompt, output_parser=StrOutputParser())
+    return chain.run({"text": text})
+
+# 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"
     }
-
-### COMBINED UI ###
+    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
+model = joblib.load("model.joblib")
+def churn_prediction(gender, SeniorCitizen, Partner, tenure, MonthlyCharges):
+    input_df = pd.DataFrame([[gender, SeniorCitizen, Partner, tenure, MonthlyCharges]],
+                            columns=["gender", "SeniorCitizen", "Partner", "tenure", "MonthlyCharges"])
+    prediction = model.predict(input_df)[0]
+    return "Churn" if prediction == 1 else "Not Churn"
+
+# Gradio UI setup
 with gr.Blocks() as demo:
     with gr.Tab("Translator"):
-        gr.Markdown("## Translator")
-        input_text = gr.Textbox(label="Text to Translate")
-        language = gr.Textbox(label="Target Language")
-        output = gr.Textbox(label="Translated Text")
-        gr.Button("Translate").click(text_translator, inputs=[input_text, language], outputs=output)
+        input_text = gr.Textbox(label="Input Text")
+        output_text = gr.Textbox(label="Translated Text")
+        translate_button = gr.Button("Translate")
+        translate_button.click(fn=translate_text, inputs=input_text, outputs=output_text)
 
-    with gr.Tab("Sentiment"):
-        gr.Markdown("## Sentiment Analysis")
-        gr.ChatInterface(sentiment_analysis, type="messages")
+    with gr.Tab("Sentiment Analysis"):
+        sentiment_input = gr.Textbox(label="Text")
+        sentiment_output = gr.Textbox(label="Sentiment")
+        sentiment_button = gr.Button("Analyze")
+        sentiment_button.click(fn=analyze_sentiment, inputs=sentiment_input, outputs=sentiment_output)
 
     with gr.Tab("Financial Analyst"):
-        gr.Markdown("## Financial Analyst")
-        audio = gr.Audio(source="microphone", type="filepath")
-        text_input = gr.Textbox()
-        summary = gr.Textbox()
-        tone_label = gr.Label()
-        gr.Button("Speech to Text").click(speech_to_text, inputs=audio, outputs=text_input)
-        gr.Button("Summarize").click(summarize_text, inputs=text_input, outputs=summary)
-        gr.Button("Classify Tone").click(text_to_sentiment, inputs=summary, outputs=tone_label)
-        gr.HighlightedText(label="Tone").render()
-        gr.HighlightedText(label="Forward-Looking").render()
-        gr.Button("Analyze All").click(fn=fin_ext, inputs=text_input, outputs=None).click(fls, inputs=text_input, outputs=None)
-        gr.Button("Entities").click(fin_ner, inputs=text_input, outputs=None)
-
-    with gr.Tab("Personal Info Detector"):
-        gr.Markdown("## Detect Personal Info")
-        pi_input = gr.Textbox()
-        pi_output = gr.HighlightedText()
-        gr.Button("Detect").click(detect_personal_info, inputs=pi_input, outputs=pi_output)
-
-    with gr.Tab("Customer Churn"):
-        gr.Markdown("## Customer Churn Prediction")
-        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"),
-            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()
-        gr.Button("Predict").click(predict, inputs=inputs, outputs=churn_output)
-
-if __name__ == "__main__":
-    demo.launch()
+        finance_input = gr.Textbox(label="Financial Text")
+        api_key_input = gr.Textbox(label="OpenAI API Key", type="password")
+        finance_output = gr.Textbox(label="Analysis")
+        finance_button = gr.Button("Analyze")
+        finance_button.click(fn=financial_analysis, inputs=[finance_input, api_key_input], outputs=finance_output)
+
+    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"):
+        gender = gr.Dropdown(choices=["Male", "Female"], label="Gender")
+        senior = gr.Dropdown(choices=[0, 1], label="Senior Citizen")
+        partner = gr.Dropdown(choices=["Yes", "No"], label="Partner")
+        tenure = gr.Number(label="Tenure (months)")
+        charges = gr.Number(label="Monthly Charges")
+        churn_output = gr.Textbox(label="Prediction")
+        churn_button = gr.Button("Predict")
+        churn_button.click(fn=churn_prediction,
+                           inputs=[gender, senior, partner, tenure, charges],
+                           outputs=churn_output)
+
+demo.launch()