ss

requirements.txt

@@ -1,3 +1,10 @@
-altair
+streamlit
+tensorflow
+tensorflow-datasets
+matplotlib
+numpy
+scikit-learn
+seaborn
+huggingface_hub
 pandas
-streamlit
+python-dotenv

src/streamlit_app.py

@@ -1,40 +1,114 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow_datasets as tfds
+import tensorflow as tf
+import numpy as np
+import time
+import tensorflow.keras as keras
+from tensorflow.keras.applications import VGG16
+from tensorflow.keras.layers import Dense, Flatten
+from tensorflow.keras.models import Model, load_model
+import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+from huggingface_hub import HfApi
+import os
+
+# 📌 Percorso della cartella dove è salvato il dataset
+DATA_DIR = "/app/src"
+
+# 📌 Autenticazione Hugging Face dal Secret nello Space
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if HF_TOKEN:
+    api = HfApi()
+    user_info = api.whoami(HF_TOKEN)
+
+    if "name" in user_info:
+        st.write(f"✅ Autenticato come {user_info['name']}")
+    else:
+        st.warning("⚠️ Token API non valido! Controlla il Secret nello Space.")
+else:
+    st.warning("⚠️ Nessun token API trovato! Verifica il Secret nello Space.")
+
+# 📌 Carica solo 300 immagini da ImageNet
+st.write("🔄 Caricamento di 300 immagini da ImageNet...")
+imagenet = tfds.load("imagenet2012", split="train", as_supervised=True, download=False, data_dir=DATA_DIR)
+
+image_list = []
+label_list = []
+
+for i, (image, label) in enumerate(imagenet.take(300)):  # Prende solo 300 immagini
+    image = tf.image.resize(image, (224, 224)) / 255.0  # Normalizzazione
+    image_list.append(image.numpy())
+    label_list.append(label.numpy())
+
+X_train = np.array(image_list)
+y_train = np.array(label_list)
+
+st.write(f"✅ Scaricate e preprocessate {len(X_train)} immagini da ImageNet!")
+
+# 📌 Caricamento del modello
+if os.path.exists("Silva.h5"):
+    model = load_model("Silva.h5")
+    st.write("✅ Modello `Silva.h5` caricato, nessun nuovo training necessario!")
+else:
+    st.write("🚀 Training in corso perché `Silva.h5` non esiste...")
+
+    # Caricare il modello VGG16 pre-addestrato
+    base_model = VGG16(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
+
+    # Congelare i livelli convoluzionali
+    for layer in base_model.layers:
+        layer.trainable = False
+
+    # Aggiungere nuovi livelli Dense
+    x = Flatten()(base_model.output)
+    x = Dense(256, activation="relu")(x)
+    x = Dense(128, activation="relu")(x)
+    output = Dense(len(set(y_train)), activation="softmax")(x)
+
+    # Creare il modello finale
+    model = Model(inputs=base_model.input, outputs=output)
+    model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+
+    # 📌 Barra di progresso del training
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+    start_time = time.time()
+
+    # 📌 Addestramento con progress bar
+    for epoch in range(10):
+        history = model.fit(X_train, y_train, epochs=1)
+        progress_bar.progress((epoch + 1) / 10)
+        elapsed_time = time.time() - start_time
+        status_text.text(f"⏳ Tempo rimanente stimato: {int(elapsed_time / (epoch + 1) * (10 - (epoch + 1)))} secondi")
+
+    st.write("✅ Addestramento completato!")
+
+    # 📌 Salvare il modello
+    model.save("Silva.h5")
+    st.write("✅ Modello salvato come `Silva.h5`!")
+
+# 📌 Bottone per scaricare il modello
+if os.path.exists("Silva.h5"):
+    with open("Silva.h5", "rb") as f:
+        st.download_button(
+            label="📥 Scarica il modello Silva.h5",
+            data=f,
+            file_name="Silva.h5",
+            mime="application/octet-stream"
+        )
+
+# Bottone per caricare il modello su Hugging Face
+def upload_model():
+    api.upload_file(
+        path_or_fileobj="Silva.h5",
+        path_in_repo="Silva.h5",
+        repo_id="scontess/Silva",
+        repo_type="model"
+    )
+    st.success("✅ Modello 'Silva' caricato su Hugging Face!")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.write("📥 Carica il modello Silva su Hugging Face")
+if st.button("🚀 Carica Silva su Model Store"):
+    upload_model()