Update app.py

app.py

@@ -1,144 +1,170 @@
+# app.py
+
+import os
 import gradio as gr
 import pandas as pd
 import numpy as np
-import joblib, os
-
-gr.load("models/iiiorg/piiranha-v1-detect-personal-information")
+import joblib
+import spacy
+
+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 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')
-
-# Load transformation pipeline and model
-pipeline = joblib.load(pipeline_path)
+pipeline_model = 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
+    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]
+        '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
+    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)
-
-    # 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]
+    pred_probs = model.predict_proba(final_df)[0]
+    return {
+        "Prediction: CHURN 🔴": pred_probs[1],
+        "Prediction: STAY ✅": pred_probs[0]
     }
 
-    return prediction_label
-
-input_interface = []
-
-with gr.Blocks(theme=gr.themes.Soft()) as app:
-
-    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)
-        
-app.launch(share=True)
+### COMBINED UI ###
+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)
+
+    with gr.Tab("Sentiment"):
+        gr.Markdown("## Sentiment Analysis")
+        gr.ChatInterface(sentiment_analysis, type="messages")
+
+    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()