aggiunto modulo forecasting e update sentiment con modello CNN

.gitattributes

@@ -2,3 +2,5 @@
 *.h5 filter=lfs diff=lfs merge=lfs -text
 multi-classification-tokenizer.json filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
+data/model/sentiment_cnn/variables/* filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,55 +8,72 @@ colorTo: purple
 pinned: false
 ---
 
-# 🚀 NGT AI Platform
+# 🚀 NGT AI Platform (v2.0)
 
-**NextGenTech AI Platform** è una suite modulare di Intelligenza Artificiale progettata per dimostrare capacità avanzate in ambito **NLP** (Natural Language Processing) e **Computer Vision**.
+**NextGenTech AI Platform** è un orchestratore modulare di Intelligenza Artificiale progettato per ambienti Enterprise. Dimostra capacità avanzate in **NLP** (Natural Language Processing), **Predictive Analytics** e **Computer Vision**, integrate in una dashboard professionale.
 
-La piattaforma è costruita con un'architettura ibrida che integra modelli **TensorFlow/Keras** (Legacy) e **PyTorch/Transformers** (NextGenTech), il tutto esposto tramite un'interfaccia web interattiva basata su **Gradio**.
+La piattaforma adotta un'architettura ibrida (**TensorFlow/Keras** + **PyTorch** + **Scikit-Learn**) ed è stata aggiornata con una **UI/UX avanzata** basata su **Gradio 5**, caratterizzata da visualizzazioni interattive, gestione dello stato e navigazione contestuale.
 
-![Platform UI](https://img.shields.io/badge/UI-Gradio-orange) ![Python](https://img.shields.io/badge/Python-3.10-blue) ![Framework](https://img.shields.io/badge/Hybrid-PyTorch%20%2B%20TensorFlow-purple)
+![Platform UI](https://img.shields.io/badge/UI-Gradio%205.x-orange) ![Python](https://img.shields.io/badge/Python-3.10-blue) ![Architecture](https://img.shields.io/badge/Hybrid-Multi%20Model-purple) ![License](https://img.shields.io/badge/License-Apache%202.0-green)
 
 ---
 
-## 🧩 Moduli Disponibili
+## ✨ Key Features
 
-### 1. 🧩 BPO Intelligent Dispatcher (NextGen) 
-Un sistema avanzato per l'analisi dei ticket di assistenza clienti (Business Process Outsourcing).
-* **Tecnologia:** DistilBERT (Fine-tuned) + spaCy (NER) + Sentiment BPO Engine.
+Oltre ai modelli AI, la piattaforma implementa pattern di sviluppo Full Stack moderni:
+
+* **📊 Interactive Dashboards:** Grafici dinamici (Plotly) con zoom, tooltip e selezione temporale per l'analisi dei dati.
+* **🗂️ Context-Aware Sidebar:** La barra laterale cambia dinamicamente contenuto (cartelle e file) in base al Tab selezionato.
+* **🔄 Dynamic Backtesting:** Il modulo di forecasting si adatta automaticamente alla quantità di dati storici forniti, separando training e test set al volo.
+* **🖱️ Click-to-Load System:** Caricamento istantaneo dei file di test cliccando direttamente nella sidebar.
+
+---
+
+## 🧩 Moduli AI Disponibili
+
+### 1. 🎧 BPO Intelligent Dispatcher (NextGen)
+Il cuore della piattaforma per l'automazione dell'Assistenza Clienti.
+* **Tecnologia:** DistilBERT (Fine-tuned) + spaCy (NER) + Custom Logic.
 * **Workflow:**
     1.  **Intent Classification:** Riconosce l'intento (es. *Amministrazione*, *Supporto Tecnico*, *Churn*).
     2.  **Smart Urgency:** Calcola la priorità incrociando gravità del problema, intento e Sentiment Score.
     3.  **Action Recommendation:** Suggerisce l'azione di business concreta (es. *"Apertura Ticket JIRA L1"*, *"Alert Retention"*).
     4.  **Hybrid NER:** Estrae ed evidenzia graficamente dati sensibili (Codici Cliente, Fatture, Email).
 
-### 2. 🩻 Healthcare Diagnostics (Computer Vision)
-Suite di supporto diagnostico basata su Convolutional Neural Networks (CNN).
-* **Chest X-Ray Analyzer:**
-    * **Obiettivo:** Screening rapido di patologie polmonari infettive
-    * **Classi:** Il modello discrimina tra 4 stati: Rischio Polmonite, Rischio Tubercolosi, Negativo Polmonite, Negativo Tubercolosi.
-    * **Architettura:** CNN custom addestrata su dataset radiografici pubblici (resize 224x224).
-    * **Use Case:** Triage automatizzato per dare priorità ai casi sospetti in lista d'attesa.
-* **Diabetic Retinopathy Screening:**
-    * **Obiettivo:** Analisi automatizzata del fondo oculare (Fundus Image) per individuare microaneurismi o emorragie tipiche della retinopatia.
-    * **Output:** Fornisce una "Second Opinion" strutturata in due parti: Diagnosi Clinica (Presenza/Assenza) e Livello di Confidenza (%).
-    * **Architettura:** Binary Classification CNN con preprocessing dell'immagine (Normalization & Resizing).
-    * **Use Case:** Screening di massa preventivo per pazienti diabetici asintomatici.
-
-### 3. 📢 NLP & Text Analysis
-Moduli di analisi semantica per l'automazione dei processi documentali.
-* **Smart Content Tagger (News Classification):** 
-    * **Obiettivo:** Categorizzazione automatica di flussi di notizie per redazioni e archivi digitali.
-    * **Classi:** Il modello distingue tra Economia, Politica, Scienza & Tecnica, Sport, Storia.
-    * **Architettura:** Rete Neurale ricorrente (Input Sequence Padding a 200 token) per catturare il contesto semantico oltre la semplice keyword density.
-* **Sentiment Analysis (BPO Edition):** 
-    * **Modello:** Rete neurale ottimizzata su dataset "Small Data" (1.2k esempi reali Helpdesk).
-    * **Caratteristiche:** Calibrato per distinguere lamentele reali da falsi positivi (gestione avanzata di "router", "fattura" e negazioni).
-    * **Integrazione:** Fornisce il punteggio emotivo al modulo BPO Dispatcher.
+### 2. 🔮 AI Workforce Forecaster 
+Modulo di pianificazione predittiva.
+* **Obiettivo:** Superare i limiti dei modelli statistici classici (Erlang-C) utilizzando il Machine Learning per prevedere i volumi di traffico.
+* **Tecnologia:** Random Forest Regressor + Plotly (Visualizzazione).
+* **Funzionalità Chiave:**
+    * **Dynamic Backtesting:** Il sistema apprende dalle prime *n-1* settimane caricate e verifica la precisione sull'ultima settimana (Ground Truth).
+    * **Seasonality Awareness:** Il modello cattura automaticamente pattern orari (picchi 10:00/16:00), giornalieri (Lunedì vs Weekend) e trend settimanali.
+    * **Interactive Dashboard:** Grafico interattivo che permette di zoomare su specifici intervalli orari e confrontare visivamente la curva *Reale* con quella *Pianificata* (AI).
+
+### 3. 📢 Sentiment Analysis (BPO Edition)
+* **Specializzazione:** Modello di rete neurale addestrato su dataset "Small Data" (1.2k esempi reali Helpdesk).
+* **Caratteristiche:** Calibrato specificamente per evitare falsi positivi su termini tecnici (es. "guasto", "disdetta") che i modelli generici fraintendono.
+* **Integrazione:** Fornisce il punteggio emotivo utilizzato dal *BPO Dispatcher* per il calcolo dell'urgenza.
+
+### 4. 📰 Smart Content Tagger (News)
+* **Obiettivo:** Assistente editoriale per la categorizzazione automatica di flussi di notizie.
+* **Classi:** *Economia, Politica, Scienza, Sport, Storia*.
+* **Architettura:** Deep Learning su sequenze di testo (Embedding layer + Global Average Pooling).
+
+### 5. 🩻 Chest X-Ray Analyzer (Computer Vision)
+Supporto decisionale per il triage radiologico.
+* **Obiettivo:** Screening rapido di patologie polmonari.
+* **Classi:** *Rischio Polmonite*, *Rischio Tubercolosi*, *Negativo Polmonite*, *Negativo Tubercolosi*.
+* **Architettura:** CNN custom addestrata su dataset radiografici pubblici (resize 224x224).
+
+### 6. 👁️ Diabetic Retinopathy Screening
+* **Obiettivo:** Analisi automatizzata del fondo oculare (Fundus Image) per screening preventivo.
+* **Output Avanzato:** Restituisce una **Diagnosi Clinica** (Presenza/Assenza) separata dal **Livello di Confidenza** (%).
+
 ---
 
 ## 🛠️ Installazione
 
-Il progetto richiede **Python 3.10**. Si consiglia l'uso di un virtual environment.
+Il progetto richiede **Python 3.10**.
 
 ### 1. Clona il repository
 ```bash
@@ -84,14 +101,22 @@ Nota: Il sistema scaricherà automaticamente anche il modello linguistico italia
 
 Affinché la piattaforma funzioni, è necessario posizionare i modelli addestrati nella cartella corretta. Assicurati che la struttura sia la seguente:
 
+```bash
 ngt-ai-platform/
-├── app.py                  # Entry point dell'applicazione
-├── requirements.txt        # Dipendenze
-├── modules/                # Logica di business
+├── app.py                  # Entry point (Gradio UI & Event Logic)
+├── style.css               # Foglio di stile CSS (Layout & Design)
+├── requirements.txt        # Dipendenze Python
+├── modules/
+│   ├── utilities/          # Utils (Loader, Sidebar Logic, HTML Render)
+│   ├── forecasting.py      # Logica ML per il forecasting
+│   ├── bpo_dispatcher.py   # Business Logic BPO
+│   └── ...                 # Altri moduli di classificazione
 └── data/
-    ├── model/              # CARTELLA MODELLI (Non versionata)
-    ├── gallery/            # Immagini di esempio per la Demo
-    └── tokenizer/          # tokenizer per la BinaryClassification e MultiClassification
+    ├── model/              # Modelli .h5 e .bin (Non versionati)
+    ├── export/             # CSV generati per il modulo Forecaster
+    ├── gallery/            # Dataset Demo (xray, retinopaty, ecc.)
+    └── tokenizer/          # Tokenizer JSON per i modelli Keras
+```
 
 ### 🚀 Avvio Piattaforma
 

app.py

@@ -1,11 +1,13 @@
 import gradio as gr
 import cv2
+import os
 import modules.utilities.utils as utils
 from modules.binary_classification import binary_classification as binary
 from modules.image_classification import image_classification as image
 from modules.multilabel_classification import multi_classification as multi
 from modules.retina import predict_diabetic_retinopathy as retina_detector
 from modules.bpo_dispatcher import predict_bpo_ticket
+import modules.forecasting as forecast
 
 # --- CONFIGURAZIONE TEMA ---
 theme = gr.themes.Soft(
@@ -21,6 +23,27 @@ theme = gr.themes.Soft(
 PATH_ROOT = "./data/gallery"
 PATH_RETINA = "./data/gallery/retinopaty" 
 PATH_XRAY = "./data/gallery/xray"   
+PATH_FORECAST = "./data/export"
+
+# Crea cartella e file demo se non esistono
+if not os.path.exists(PATH_FORECAST):
+    os.makedirs(PATH_FORECAST)
+    
+demo_csv_path = os.path.join(PATH_FORECAST, "plan_sample.csv")
+
+# Se il file non esiste, ORA generiamo 4 settimane di dati procedurali
+if not os.path.exists(demo_csv_path):
+    csv_content = forecast.generate_mock_export()
+    with open(demo_csv_path, "w") as f:
+        f.write(csv_content)
+
+def forecast_logic(file):
+    if file is None: raise gr.Error("Carica un file CSV")
+    if isinstance(file, list): file = file[0]
+    
+    img, text = forecast.predict_workload(file)
+    if img is None: raise gr.Error(text)
+    return img, text
 
 def binary_classification(text):
     if text.strip(): return binary(text)
@@ -105,10 +128,12 @@ with gr.Blocks(title="NGT AI Platform", theme=theme, css_paths="style.css") as d
             with gr.Column(scale=1, elem_classes="header-text-col"):
                 gr.Markdown("""<h1>AI Platform</h1><div class='subheader'>Advanced Machine Learning Solutions</div>""")
         
-        # --- TAB 1: BPO INTELLIGENT DISPATCHER ---
+        # --- BPO INTELLIGENT DISPATCHER ---
         with gr.Tab("🧩 BPO Dispatcher") as tab_bpo:
-            gr.Markdown("### Intelligent Ticket Routing & NER")
-            gr.Markdown("Sistema proprietario per l'analisi automatica dei ticket di assistenza. Il modello identifica l'intento, l'urgenza e i dati sensibili del cliente.")
+            gr.Markdown("""
+            # 🧩 Intelligent Ticket Routing & NER
+            Sistema proprietario per l'analisi automatica dei ticket di assistenza. Il modello identifica l'intento, l'urgenza e i dati sensibili del cliente.
+            """)
             
             with gr.Row(elem_classes="responsive-row"):
                 # INPUT
@@ -149,72 +174,100 @@ with gr.Blocks(title="NGT AI Platform", theme=theme, css_paths="style.css") as d
                 inputs=bpo_input, 
                 outputs=[bpo_intent_output, bpo_urgency_output, bpo_action_output, bpo_ner_output]
             )
-            
-        # --- TAB 2: Chest X-Ray Diagnostics ---
-        with gr.Tab("🩻 Chest X-Ray Diagnostics") as tab_xray:
-            
-            # 1. DISCLAIMER MEDICO (Fondamentale)
+
+        # --- AI FORECASTER ---
+        with gr.Tab("🔮 AI Forecaster") as tab_forecast:
             gr.Markdown("""
-            # 🩻 Analisi Radiografica Toracica (Supporto Decisionale)
-            
-            **DISCLAIMER:** Questo modulo è un prototipo di ricerca AI. **NON sostituisce il parere di un medico.**
-            Il sistema è addestrato per identificare pattern visivi associati a:
-            * **Polmonite** (Pneumonia)
-            * **Tubercolosi** (Tuberculosis)
-            
-            *Caricare solo immagini radiografiche frontali (Chest X-Ray) o utilizzare gli esempi recuperabili nella sidebar laterale.*
+            # 🔮 AI Brain: Predictive Planning
+            Modulo interattivo per la pianificazione dei turni. Confronta in tempo reale il forecast AI con i dati storici effettivi.
             """)
             
             with gr.Row():
-                # COLONNA INPUT
-                with gr.Column(scale=1):
-                    image_input = gr.Image(type="numpy", label="Carica Radiografia", height=400)
-                    analyze_btn_img = gr.Button("🏥 Avvia Diagnosi AI", variant="primary")
-
-                # COLONNA OUTPUT
                 with gr.Column(scale=1):
                     with gr.Group():
-                        gr.Markdown("#### 📋 Referto AI", elem_classes="h4-margin")
-                        output_label = gr.Label(num_top_classes=4, label="Probabilità Patologia")
+                        gr.Markdown("### 1. Configurazione")
+                        with gr.Row():
+                            gr.Dropdown(["Customer Care"], label="Business Unit", value="Customer Care")
+                            gr.Dropdown(["Ass. Tecnica"], label="Reparto", value="Ass. Tecnica")
+                        gr.Dropdown(["Inbound Calls"], label="Attività", value="Inbound Calls")
                     
-            analyze_btn_img.click(image, inputs=image_input, outputs=output_label)
-
-        # --- TAB 3: Diabetic Retinopathy ---
-        with gr.Tab("👁️ Diabetic Retinopathy") as tab_retina:
+                    gr.Markdown("### 2. Dati Storici")
+                    forecast_file = gr.File(label="Seleziona Export (.csv)", file_types=[".csv"], height=100, interactive=False)
+                    forecast_btn = gr.Button("🔮 Genera Grafico Interattivo", variant="primary")
+                
+                with gr.Column(scale=3): 
+                    gr.Markdown("### 📊 Dashboard Interattiva")
+                    forecast_plot = gr.Plot(label="Analisi Volumi")
+                    forecast_stats = gr.Textbox(label="KPI Backtesting", lines=4)
+            
+            forecast_btn.click(
+                forecast_logic, 
+                inputs=forecast_file, 
+                outputs=[forecast_plot, forecast_stats]
+            )
+        
+        # --- Sentiment Analysis (BPO) ---
+        with gr.Tab("📢 Sentiment Analysis (BPO)") as tab_sentiment:
+            
+            # 1. HEADER E GUIDA UTENTE
             gr.Markdown("""
-            # 👁️ Screening Retinopatia Diabetica
-            **DISCLAIMER:** Questo modulo è un prototipo di ricerca AI. **NON sostituisce il parere di un medico.** Sistema di supporto decisionale. Analizza scansioni del fondo oculare.
+            # 😠/😍 Analizzatore di Sentiment (Dominio Helpdesk)
+            
+            **ATTENZIONE:** Questo modello è **altamente specializzato** nel dominio dell'Assistenza Clienti (Telco/Energy).
             
-            *Caricare solo immagini di retinografie digitali o utilizzare gli esempi recuperabili nella sidebar laterale.*            
+            * ✅ **Usa questo modulo per:** Ticket di guasti, lamentele amministrative, feedback su operatori, richieste di disdetta.
+            * ❌ **NON usare questo modulo per:** Frasi generiche ("Il cielo è blu"), recensioni di film, o linguaggio comune non tecnico.
+            
+            _Il modello potrebbe interpretare frasi generiche positive come negative se non contengono parole chiave del suo vocabolario specifico._
             """)
             
+            # 2. DETTAGLI TECNICI (Nascosti in un Accordion per pulizia)
+            with gr.Accordion("ℹ️ Come funziona questo modello?", open=False):
+                gr.Markdown("""
+                Questo sistema utilizza una rete neurale leggera addestrata su un dataset proprietario di **1.200 interazioni reali** cliente-operatore.
+                
+                * **Preprocessing:** Lemmatizzazione Spacy + Rimozione Stopwords (con Whitelist per le negazioni).
+                * **Architettura:** Dense Neural Network con Dropout e L2 Regularization.
+                * **Focus:** È calibrato per rilevare l'urgenza nascosta anche in frasi apparentemente calme.
+                """)
+            
+            # 3. INTERFACCIA
             with gr.Row():
-                # COLONNA INPUT
-                with gr.Column(scale=1):
-                    image_input_dr = gr.Image(
-                        type="numpy", 
-                        label="Carica Scansione Retinica", 
-                        height=400,
-                        sources=["upload", "clipboard"]
+                with gr.Column():
+                    # Input Text
+                    sentiment_input = gr.Textbox(
+                        label="Inserisci il testo del ticket o della mail", 
+                        placeholder="Es: Non funziona internet e nessuno mi risponde...",
+                        lines=3
                     )
-                    analyze_btn_dr = gr.Button("🏥 Analisi Fondo Oculare", variant="primary")
-
-                # COLONNA OUTPUT
-                with gr.Column(scale=1):
-                    with gr.Group():
-                        gr.Markdown("### 📋 Esito Screening", elem_classes="h4-margin")
-                        # Output 1: La Diagnosi (Testo)
-                        output_dr_diagnosis = gr.Label(label="Diagnosi AI")
-                        # Output 2: La Percentuale (Testo/Numero)
-                        output_dr_prob = gr.Label(label="Livello di Confidenza (Rischio)")
                     
-            analyze_btn_dr.click(
-                retina_detector, 
-                inputs=image_input_dr, 
-                outputs=[output_dr_diagnosis, output_dr_prob]
+                    # ESEMPI CLICCABILI (Fondamentali per guidare l'utente!)
+                    gr.Examples(
+                        examples=[
+                            ["L'assistenza ricevuta è stata pessima, sono deluso."],
+                            ["Il router funziona benissimo, grazie per la velocità."],
+                            ["Non ho ancora ricevuto la fattura di gennaio."],
+                            ["Sono due giorni che ho la linea ferma, è inaccettabile!"],
+                            ["L'operatore Marco è stato gentilissimo e ha risolto tutto."]
+                        ],
+                        inputs=sentiment_input,
+                        label="Prova questi esempi BPO:"
+                    )
+                    
+                    sentiment_btn = gr.Button("Analizza Sentiment", variant="primary")
+                    
+                with gr.Column():
+                    # Output Label (Percentuali)
+                    sentiment_output = gr.Label(num_top_classes=2, label="Risultato Analisi")
+            
+            # 4. COLLEGAMENTO FUNZIONE
+            sentiment_btn.click(
+                fn=binary, 
+                inputs=sentiment_input, 
+                outputs=sentiment_output
             )
-
-        # --- TAB 4: News Classification (AI Editor) ---
+        
+        # --- News Classification (AI Editor) ---
         with gr.Tab("📰 Smart Content Tagger") as tab_news:
             
             # 1. HEADER & CONTESTO
@@ -259,67 +312,72 @@ with gr.Blocks(title="NGT AI Platform", theme=theme, css_paths="style.css") as d
                         multi_output = gr.Label(num_top_classes=5, label="Confidenza del Modello")
                     
             analyze_btn_multi.click(multi, inputs=multi_input, outputs=multi_output)
-
-    # --- TAB 5: Sentiment Analysis (BPO) ---
-        with gr.Tab("📢 Sentiment Analysis (BPO)") as tab_sentiment:
+        
+        # --- Chest X-Ray Diagnostics ---
+        with gr.Tab("🩻 Chest X-Ray Diagnostics") as tab_xray:
             
-            # 1. HEADER E GUIDA UTENTE
+            # 1. DISCLAIMER MEDICO (Fondamentale)
             gr.Markdown("""
-            # 😠/😍 Analizzatore di Sentiment (Dominio Helpdesk)
-            
-            **ATTENZIONE:** Questo modello è **altamente specializzato** nel dominio dell'Assistenza Clienti (Telco/Energy).
+            # 🩻 Analisi Radiografica Toracica (Supporto Decisionale)
             
-            * ✅ **Usa questo modulo per:** Ticket di guasti, lamentele amministrative, feedback su operatori, richieste di disdetta.
-            * ❌ **NON usare questo modulo per:** Frasi generiche ("Il cielo è blu"), recensioni di film, o linguaggio comune non tecnico.
+            **DISCLAIMER:** Questo modulo è un prototipo di ricerca AI. **NON sostituisce il parere di un medico.**
+            Il sistema è addestrato per identificare pattern visivi associati a:
+            * **Polmonite** (Pneumonia)
+            * **Tubercolosi** (Tuberculosis)
             
-            _Il modello potrebbe interpretare frasi generiche positive come negative se non contengono parole chiave del suo vocabolario specifico._
+            *Utilizzare gli esempi di radiografiche frontali (Chest X-Ray) recuperabili nella sidebar laterale.*
             """)
             
-            # 2. DETTAGLI TECNICI (Nascosti in un Accordion per pulizia)
-            with gr.Accordion("ℹ️ Come funziona questo modello?", open=False):
-                gr.Markdown("""
-                Questo sistema utilizza una rete neurale leggera addestrata su un dataset proprietario di **1.200 interazioni reali** cliente-operatore.
-                
-                * **Preprocessing:** Lemmatizzazione Spacy + Rimozione Stopwords (con Whitelist per le negazioni).
-                * **Architettura:** Dense Neural Network con Dropout e L2 Regularization.
-                * **Focus:** È calibrato per rilevare l'urgenza nascosta anche in frasi apparentemente calme.
-                """)
-            
-            # 3. INTERFACCIA
             with gr.Row():
-                with gr.Column():
-                    # Input Text
-                    sentiment_input = gr.Textbox(
-                        label="Inserisci il testo del ticket o della mail", 
-                        placeholder="Es: Non funziona internet e nessuno mi risponde...",
-                        lines=3
-                    )
+                # COLONNA INPUT
+                with gr.Column(scale=1):
+                    image_input = gr.Image(type="numpy", label="Seleziona Radiografia", height=400, interactive=False)
+                    analyze_btn_img = gr.Button("🏥 Avvia Diagnosi AI", variant="primary")
+
+                # COLONNA OUTPUT
+                with gr.Column(scale=1):
+                    with gr.Group():
+                        gr.Markdown("#### 📋 Referto AI", elem_classes="h4-margin")
+                        output_label = gr.Label(num_top_classes=4, label="Probabilità Patologia")
                     
-                    # ESEMPI CLICCABILI (Fondamentali per guidare l'utente!)
-                    gr.Examples(
-                        examples=[
-                            ["L'assistenza ricevuta è stata pessima, sono deluso."],
-                            ["Il router funziona benissimo, grazie per la velocità."],
-                            ["Non ho ancora ricevuto la fattura di gennaio."],
-                            ["Sono due giorni che ho la linea ferma, è inaccettabile!"],
-                            ["L'operatore Marco è stato gentilissimo e ha risolto tutto."]
-                        ],
-                        inputs=sentiment_input,
-                        label="Prova questi esempi BPO:"
+            analyze_btn_img.click(image, inputs=image_input, outputs=output_label)
+
+        # --- Diabetic Retinopathy ---
+        with gr.Tab("👁️ Diabetic Retinopathy") as tab_retina:
+            gr.Markdown("""
+            # 👁️ Screening Retinopatia Diabetica
+            **DISCLAIMER:** Questo modulo è un prototipo di ricerca AI. **NON sostituisce il parere di un medico.** Sistema di supporto decisionale. Analizza scansioni del fondo oculare.
+            
+            *Utilizzare gli esempi di retinografie digitali recuperabili nella sidebar laterale.*            
+            """)
+            
+            with gr.Row():
+                # COLONNA INPUT
+                with gr.Column(scale=1):
+                    image_input_dr = gr.Image(
+                        type="numpy", 
+                        label="Seleziona Scansione Retinica", 
+                        height=400,
+                        sources=["upload", "clipboard"],
+                        interactive=False
                     )
+                    analyze_btn_dr = gr.Button("🏥 Analisi Fondo Oculare", variant="primary")
+
+                # COLONNA OUTPUT
+                with gr.Column(scale=1):
+                    with gr.Group():
+                        gr.Markdown("### 📋 Esito Screening", elem_classes="h4-margin")
+                        # Output 1: La Diagnosi (Testo)
+                        output_dr_diagnosis = gr.Label(label="Diagnosi AI")
+                        # Output 2: La Percentuale (Testo/Numero)
+                        output_dr_prob = gr.Label(label="Livello di Confidenza (Rischio)")
                     
-                    sentiment_btn = gr.Button("Analizza Sentiment", variant="primary")
-                    
-                with gr.Column():
-                    # Output Label (Percentuali)
-                    sentiment_output = gr.Label(num_top_classes=2, label="Risultato Analisi")
-            
-            # 4. COLLEGAMENTO FUNZIONE
-            sentiment_btn.click(
-                fn=binary, 
-                inputs=sentiment_input, 
-                outputs=sentiment_output
+            analyze_btn_dr.click(
+                retina_detector, 
+                inputs=image_input_dr, 
+                outputs=[output_dr_diagnosis, output_dr_prob]
             )
+
      
     ui_outputs = [main_sidebar, sidebar_explorer, image_input, image_input_dr]
 
@@ -327,39 +385,42 @@ with gr.Blocks(title="NGT AI Platform", theme=theme, css_paths="style.css") as d
     sidebar_explorer.change(
         fn=utils.global_file_loader, 
         inputs=sidebar_explorer,
-        outputs=[bpo_input, image_input, image_input_dr, multi_input, sentiment_input]
+        outputs=[bpo_input, forecast_file, image_input, image_input_dr, multi_input, sentiment_input]
     )
     
-    # 2. DEFINIZIONE DEL RESET TOTALE
+    # 2. ESET TOTALE
     # Questa funzione restituisce valori "vuoti" per tutti i campi sensibili
     def reset_all_fields():
         return (
-            None,   # bpo_input (Textbox)
-            None,   # bpo_intent (Label)
-            None,     # bpo_urgency (Textbox)
-            None,     # bpo_action (Textbox)
-            None,     # bpo_ner (HTML)
-            None,   # image_input (Image Xray)
-            None,   # output_label (Label Xray)
-            None,   # image_input_dr (Image Retina)
-            None,   # output_dr_diag (Label Retina)
-            None,   # output_dr_prob (Label Retina)
-            None,     # multi_input (Textbox News)
-            None,   # multi_output (Label News)
-            None,     # sentiment_input (Textbox)
-            None    # sentiment_output (Label)
+            None,   # 1. bpo_input
+            None,   # 2. bpo_intent
+            None,   # 3. bpo_urgency
+            None,   # 4. bpo_action
+            None,   # 5. bpo_ner
+            None,   # 6. forecast_file
+            None,   # 7. forecast_plot
+            None,   # 8. forecast_stats
+            None,   # 9. image_input
+            None,   # 10. output_label
+            None,   # 11. image_input_dr
+            None,   # 12. output_dr_diag
+            None,   # 13. output_dr_prob
+            None,   # 14. multi_input
+            None,   # 15. multi_output
+            None,   # 16. sentiment_input
+            None    # 17. sentiment_output
         )
 
-    # Lista ordinata di TUTTI gli output da pulire
-    # DEVE corrispondere esattamente all'ordine del return sopra!
     reset_outputs = [
         bpo_input, bpo_intent_output, bpo_urgency_output, bpo_action_output, bpo_ner_output,
+        forecast_file, forecast_plot, forecast_stats,
         image_input, output_label,
         image_input_dr, output_dr_diagnosis, output_dr_prob,
         multi_input, multi_output,
         sentiment_input, sentiment_output
     ]
 
+
     # 3. GESTIONE CAMBIO TAB (RESET + SIDEBAR)
     
     # Per i Tab NLP: Disabilita Sidebar + Resetta TUTTO
@@ -370,6 +431,14 @@ with gr.Blocks(title="NGT AI Platform", theme=theme, css_paths="style.css") as d
         fn=reset_all_fields, outputs=reset_outputs
     )
 
+    tab_forecast.select(
+        fn=lambda: utils.enable_sidebar(PATH_FORECAST), 
+        outputs=ui_outputs
+    ).then(
+        fn=reset_all_fields, 
+        outputs=reset_outputs
+    )
+
     tab_news.select(
         fn=utils.disable_sidebar, 
         outputs=ui_outputs

data/export/plan_sample.csv

@@ -0,0 +1,2689 @@
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data/model/sentiment_cnn/fingerprint.pb

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data/model/sentiment_cnn/saved_model.pb

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data/model/sentiment_cnn/variables/variables.index

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modules/binary_classification.py

@@ -1,105 +1,62 @@
-import spacy
-import re
-import keras.models as models
-from nltk.corpus import stopwords
-import nltk
-import modules.utilities.utils as utils
+from __future__ import annotations
+from typing import Optional
+import tensorflow as tf
+import os
+
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODEL_DIR_DEFAULT = os.path.join(BASE_DIR, 'data', 'model', 'sentiment_cnn')
+
+# Cache di processo (il modello viene caricato una volta sola)
+_INFER: Optional[tf.types.experimental.ConcreteFunction] = None
+_MODEL_DIR_LOADED: Optional[str] = None
+
+def _load_infer(model_dir: str) -> tf.types.experimental.ConcreteFunction:
+    """
+    Load SavedModel once and return serving function.
+    TF 2.12 compatible, expects signature 'serving_default'.
+    """
+    sm = tf.saved_model.load(model_dir)
+    return sm.signatures["serving_default"]
+
+def _get_infer(model_dir: str) -> tf.types.experimental.ConcreteFunction:
+    global _INFER, _MODEL_DIR_LOADED
+    model_dir = str(model_dir)
+    if _INFER is None or _MODEL_DIR_LOADED != model_dir:
+        _INFER = _load_infer(model_dir)
+        _MODEL_DIR_LOADED = model_dir
+    return _INFER
+
+
+def _predict_prob_positive(text: str, model_dir: str) -> float:
+    infer = _get_infer(model_dir)
+    x = tf.constant([text], dtype=tf.string)
+    out = infer(text=x)
+    # Output key stabilized by our exporter
+    y = out["prob"]
+    return float(y.numpy()[0][0])
+
+
+def binary_classification(text: str):
+    """
+    IDENTICAL behavior to legacy:
+    - if text is empty -> ({"error": "Sentence is required"}, 415)
+    - else -> {"positive": "0.xx", "negative": "0.yy"}  (strings, 2 decimals)
+    """
+    try:
+        if text == "":
+            raise Exception
+    except:
+        return {"error": "Sentence is required"}, 415
+
+    prob_pos = _predict_prob_positive(text, MODEL_DIR_DEFAULT)
+
+    positive = prob_pos
+    negative = 1.0 - positive
+
+    labels = {
+        "positive": f"{positive:.2f}",
+        "negative": f"{negative:.2f}",
+    }
+    return labels
 
-BASE_PATH = './data/'
-VOCAB = BASE_PATH + 'binary-classification-vocab.txt'
-MODEL = BASE_PATH + 'model/binary-classification.h5'
-WEIGHTS = BASE_PATH + 'weights/'
-TOKEN = BASE_PATH + 'tokenizer/binary-classification-tokenizer.json'
-
-def init():
-	nltk.download('stopwords')
-	global nlp
-	nlp = spacy.load("it_core_news_lg")
-
-def load_vocab():
-	vocab=utils.load_doc(VOCAB)
-	vocab=vocab.split()
-	vocab = set(vocab)
-	return vocab
-
-def lemma_text(text):
-	doc = nlp(text)
-	lemmatized_tokens = [token.lemma_ for token in doc]
-	lemmatized_text = ' '.join(lemmatized_tokens)
-	return lemmatized_text
-
-def splitWords(l):
-    for words in l:
-        yield from words.split()
-
-def clean_doc(doc):
-    # 1. Pulizia Standard
-    doc = lemma_text(doc)
-    tokens = doc.split()
-    # Rimuovi punteggiatura
-    tokens = [re.sub(r'[^\w\s]', ' ', w) for w in tokens]
-    # Flattening della lista se splitWords ha creato sottoliste
-    tokens = list(splitWords(tokens))
-    # Solo alfabetici
-    tokens = [word for word in tokens if word.isalpha()]
-    # 2. STOPWORDS INTELLIGENTI
-    stop_words = set(stopwords.words('italian'))
-    # Parole da SALVARE (Whitelist)
-    whitelist = {'non', 'né', 'ne', 'mai', 'nessuno', 'niente', 'nulla', 'contro', 'senza'}
-    final_stop_words = stop_words - whitelist
-    # Filtra
-    tokens = [w for w in tokens if not w in final_stop_words]
-    # Accetta parole anche di 2 lettere (es. "ne", "no")
-    tokens = [word for word in tokens if len(word) > 1]
-    return tokens
-
-def predict_sentiment(review, vocab, tokenizer, model):
-    # clean
-    tokens = clean_doc(review)
-    # filter by vocab
-    tokens = [w for w in tokens if w in vocab] 
-    # convert to line
-    line = ' '.join(tokens)
-    # encode
-    encoded = tokenizer.texts_to_matrix([line], mode='tfidf')
-    # predict sentiment
-    yhat = model.predict(encoded, verbose=0)
-    # retrieve predicted percentage and label
-    percent_pos = yhat[0,0]
-    if round(percent_pos) == 0:
-        return (1-percent_pos), 'NEGATIVE'
-    return percent_pos, 'POSITIVE'
-
-def predict(model_path, weights_path, tokenizer_path, text, debug) :
-	tokenizer = utils.load_tokenizer(tokenizer_path)
-	vocab = load_vocab()
-	doc = lemma_text(text)
-	model = models.load_model(model_path, compile=False)
-	if(weights_path != '') :
-		model.load_weights(weights_path, skip_mismatch=True, by_name=True)
-	percent, sentiment = predict_sentiment(doc, vocab, tokenizer, model)
-	if(debug):
-		print('Review: [%s]\nSentiment: %s (%.3f%%)' % (doc, sentiment, percent*100))
-	return doc, sentiment, percent
-
-def binary_classification(text):
-	init()
-	weights = ''
-	try:
-		if(text == "") : raise Exception
-	except :
-		return {"error": "Sentence is required"}, 415
-	doc, sentiment, percent = predict(MODEL, weights, TOKEN, text, False)
-	positive = 0
-	negative = 0
-	if(sentiment == 'POSITIVE'):
-		positive = percent.astype(float) * 100
-		negative = 100 - positive
-	else:
-		negative = percent.astype(float) * 100
-		positive = 100 - negative
-	labels = {
-        'positive' : "%.2f" % float(positive/100),
-        'negative' : "%.2f" % float(negative/100)
-    }    
-	return labels

modules/forecasting.py

@@ -0,0 +1,163 @@
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error
+import plotly.graph_objects as go
+import io
+
+def generate_mock_export():
+    """
+    Genera un CSV con 4 settimane di dati (28 giorni) per simulare una pianificazione.
+    Include pattern orari (picchi 10:00/16:00), settimanali (Lunedì alto) e festivi.
+    """
+    # Generiamo dati partendo da un Lunedì recente
+    start_date = pd.Timestamp("2025-05-05")
+    periods = 28 * 24 * 4
+    dates = pd.date_range(start=start_date, periods=periods, freq='15min')
+    df = pd.DataFrame({'Timestamp': dates})
+    df['Hour'] = df['Timestamp'].dt.hour
+    df['DayOfWeek'] = df['Timestamp'].dt.dayofweek
+    base_volume = 10
+    df['Volume'] = base_volume
+    mask_work = (df['Hour'] >= 8) & (df['Hour'] <= 21)
+    df.loc[mask_work, 'Volume'] += 50
+    mask_peak1 = (df['Hour'] >= 10) & (df['Hour'] <= 11)
+    mask_peak2 = (df['Hour'] >= 15) & (df['Hour'] <= 16)
+    df.loc[mask_peak1, 'Volume'] += 40
+    df.loc[mask_peak2, 'Volume'] += 30
+    df.loc[df['Hour'] == 13, 'Volume'] -= 15
+    df.loc[df['DayOfWeek'] <= 1, 'Volume'] *= 1.2
+    df.loc[df['DayOfWeek'] >= 5, 'Volume'] *= 0.2
+    df.loc[df['Hour'] < 7, 'Volume'] = np.random.randint(0, 5, size=sum(df['Hour'] < 7))
+    noise = np.random.randint(-5, 10, size=len(df))
+    df['Volume'] += noise
+    df['Volume'] = df['Volume'].clip(lower=0).astype(int)
+    
+    csv_buffer = io.StringIO()
+    df[['Timestamp', 'Volume']].to_csv(csv_buffer, index=False)
+    csv_buffer.seek(0)
+    return csv_buffer.getvalue()
+
+def predict_workload(file_obj):
+    """
+    Logica Backtesting con Output PLOTLY (Interattivo).
+    """
+    if file_obj is None: return None, "⚠️ Seleziona un file CSV dalla sidebar laterale."
+    
+    try:
+        # Caricamento file cvs
+        if hasattr(file_obj, 'name'):
+            df = pd.read_csv(file_obj.name)
+        else:
+            df = pd.read_csv(file_obj)
+            
+        df['Timestamp'] = pd.to_datetime(df['Timestamp'])
+        df = df.sort_values('Timestamp')
+        
+        # --- LOGICA SPLIT settimana n da settimane n-1 ---
+        last_timestamp = df['Timestamp'].max()
+        days_to_subtract = last_timestamp.dayofweek
+        split_date = (last_timestamp - pd.Timedelta(days=days_to_subtract)).normalize()
+        
+        train_df = df[df['Timestamp'] < split_date].copy()
+        test_df = df[df['Timestamp'] >= split_date].copy()
+        
+        if len(train_df) == 0 or len(test_df) == 0:
+            return None, "⚠️ Dati insufficienti per il backtesting."
+
+        # --- TRAINING ---
+        for d in [train_df, test_df]:
+            d['Hour'] = d['Timestamp'].dt.hour
+            d['Minute'] = d['Timestamp'].dt.minute
+            d['DayOfWeek'] = d['Timestamp'].dt.dayofweek
+        
+        X_train = train_df[['Hour', 'Minute', 'DayOfWeek']]
+        y_train = train_df['Volume']
+        
+        model = RandomForestRegressor(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+        
+        # --- PREDICTION ---
+        X_test = test_df[['Hour', 'Minute', 'DayOfWeek']]
+        test_df['Predicted_Volume'] = model.predict(X_test).astype(int)
+        
+        # --- METRICS ---
+        total_actual = test_df['Volume'].sum()
+        total_pred = test_df['Predicted_Volume'].sum()
+        mae = mean_absolute_error(test_df['Volume'], test_df['Predicted_Volume'])
+        diff_perc = ((total_pred - total_actual) / total_actual) * 100
+
+        # ==========================================
+        # CREAZIONE GRAFICO INTERATTIVO (PLOTLY)
+        # ==========================================
+        
+        # Creiamo un oggetto Figure
+        fig = go.Figure()
+
+        # 1. Serie: Dati Reali
+        fig.add_trace(go.Scatter(
+            x=test_df['Timestamp'],
+            y=test_df['Volume'],
+            mode='lines',
+            name='Reale (Consuntivo)',
+            line=dict(color='#3b82f6', width=2),
+            fill='tozeroy', # Riempie l'area sotto
+            fillcolor='rgba(59, 130, 246, 0.2)' # Blu trasparente
+        ))
+
+        # 2. Serie: Forecast AI (Previsione)
+        fig.add_trace(go.Scatter(
+            x=test_df['Timestamp'],
+            y=test_df['Predicted_Volume'],
+            mode='lines+markers', # Linea con pallini sui punti
+            name='Forecast AI (Pianificato)',
+            line=dict(color='#f97316', width=3, dash='solid'), 
+            marker=dict(size=4)
+        ))
+
+        # 3. Layout e Stile
+        fig.update_layout(
+            title=f"<b>Analisi Comparativa:</b> Reale vs AI (Settimana corrente)",
+            xaxis_title="Fascia Oraria (15min)",
+            yaxis_title="Volume Chiamate",
+            template="plotly_white", # Sfondo bianco pulito
+            hovermode="x unified",   # mostra entrambi i valori al passaggio del mouse
+            legend=dict(
+                orientation="h",     # Legenda orizzontale in alto
+                yanchor="bottom",
+                y=1.02,
+                xanchor="right",
+                x=1
+            ),
+            height=500, # Altezza fissa
+            margin=dict(l=20, r=20, t=60, b=20)
+        )
+
+        # 4. Aggiunta Range Slider (Barra sotto per scorrere i giorni)
+        fig.update_xaxes(
+            rangeslider_visible=True,
+            rangeselector=dict(
+                buttons=list([
+                    dict(count=1, label="1gg", step="day", stepmode="backward"),
+                    dict(count=3, label="3gg", step="day", stepmode="backward"),
+                    dict(step="all", label="Settimana")
+                ])
+            )
+        )
+
+        # Statistiche testuali
+        msg = (
+            f"✅ **Analisi Interattiva Completata**\n"
+            f"📊 **Risultati Backtesting:**\n"
+            f"• Volume Reale: {total_actual}\n"
+            f"• Volume Previsto AI: {total_pred} ({diff_perc:+.1f}%)\n"
+            f"• Scostamento Medio (MAE): {mae:.1f} chiamate/slot\n\n"
+            f"💡 *Usa lo slider in basso per zoomare su un giorno specifico.*"
+        )
+        
+        # Gradio gr.Plot accetta l'oggetto fig direttamente
+        return fig, msg
+
+    except Exception as e:
+        # In caso di errore restituiamo un grafico vuoto e il messaggio
+        return go.Figure(), f"Errore Tecnico: {str(e)}"

modules/utilities/utils.py

@@ -30,18 +30,19 @@ def global_file_loader(file_data):
     # 1. NORMALIZZAZIONE INPUT (Il Fix Cruciale)
     if isinstance(file_data, list):
         if not file_data:
-            return [gr.update() for _ in range(5)]
+            return [gr.update() for _ in range(6)]
         file_path = file_data[0]
     else:
         # Se è None o stringa vuota
         if not file_data:
-            return [gr.update() for _ in range(5)]
+            return [gr.update() for _ in range(6)]
         file_path = file_data # È già la stringa del percorso
 
     ext = os.path.splitext(file_path)[1].lower()
     
     # Valori di default (non cambiare nulla)
     bpo_update = gr.update()
+    forecast_update = gr.update()
     img_xray_update = gr.update()
     img_retina_update = gr.update()
     news_update = gr.update()
@@ -55,18 +56,32 @@ def global_file_loader(file_data):
             img_retina_update = gr.Image(value=file_path)
             
         # LOGICA PER TESTI
-        elif ext in ['.txt', '.csv', '.log', '.md', '.json']:
+        elif ext in ['.txt', '.log', '.md', '.json']:
             with open(file_path, "r", encoding="utf-8") as f:
                 content = f.read()
+
+        elif ext in ['.csv']:
+            # 1. Aggiorniamo il modulo Forecast (passando il PATH)
+            forecast_update = gr.update(value=file_path)
             
-            bpo_update = gr.Textbox(value=content)
-            news_update = gr.Textbox(value=content)
-            sentiment_update = gr.Textbox(value=content)
+            # 2. FIX ERRORE: Leggiamo il contenuto testuale per gli altri tab (opzionale)
+            # Se non leggi il file qui, 'content' non esiste e dava errore.
+            try:
+                with open(file_path, "r", encoding="utf-8") as f:
+                    content = f.read()
+                
+                # Ora 'content' esiste e possiamo usarlo
+                bpo_update = gr.Textbox(value=content)
+                news_update = gr.Textbox(value=content)
+                sentiment_update = gr.Textbox(value=content)
+            except:
+                # Se il CSV è illeggibile come testo, pazienza, non crashiamo
+                pass
             
     except Exception as e:
         print(f"Errore lettura file: {e}")
 
-    return bpo_update, img_xray_update, img_retina_update, news_update, sentiment_update
+    return bpo_update, forecast_update, img_xray_update, img_retina_update, news_update, sentiment_update
 
 def enable_sidebar(new_root):
     """Mostra la sidebar e aggiorna il path (Per Computer Vision)"""

requirements.txt

@@ -25,6 +25,7 @@ pandas>=2.0.0
 spacy==3.8.2
 nltk>=3.8.1
 scikit-learn>=1.3.0
+plotly>=5.0.0
 
 # --- IMAGE PROCESSING ---
 opencv-python-headless