Update app.py

app.py

@@ -1,46 +1,6 @@
+from transformers import pipeline
 import gradio as gr
-import joblib
-import re
-import pandas as pd
-import os
-import spacy
-from pydantic import BaseModel, Field
-from langchain.output_parsers import PydanticOutputParser
-from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
-from langchain.chat_models import ChatOpenAI
-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):
@@ -51,7 +11,35 @@ def sentiment_analysis(message, history):
     result = classifier(message)
     return f"Sentimiento : {result[0]['label']} (Probabilidad: {result[0]['score']:.2f})"
 
-# 3. Financial Analyst (LangChain with OpenAI, requires API key)
+with gr.Blocks() as demo:
+    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/).
+    """)
+
+
+import os
+os.system("pip install gradio==3.0.18")
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+import gradio as gr
+import spacy
 nlp = spacy.load('en_core_web_sm')
 nlp.add_pipe('sentencizer')
 
@@ -109,22 +97,95 @@ def fls(text):
     results = fls_model(split_in_sentences(text))
     return make_spans(text,results) 
 
+demo = gr.Blocks()
+
+with demo:
+    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)
+
+import os
+import gradio as gr
+
+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
+
+chat = ChatOpenAI()
+
+# Define the Pydantic Model
+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
+
+# Interface
+with gr.Blocks() as demo:
+    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)
+
+import gradio as gr
+
+gr.load("models/iiiorg/piiranha-v1-detect-personal-information").launch()
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib, os
 
-# 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')
@@ -198,80 +259,9 @@ def predict(SeniorCitizen, Partner, Dependents, tenure,
 
 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.Blocks(theme=gr.themes.Soft()) as app:
 
-    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')
+    Title = gr.Label('Customer Churn Prediction App')
 
     with gr.Row():
         Title
@@ -333,4 +323,7 @@ with gr.Blocks() as demo:
         
     predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()
+
+    app.launch(share=True)