Update README.md

README.md

@@ -9,4 +9,130 @@ tags:
 - Les recettes loufoques
 - assemblage
 - fait moi une glace a la viande
----
+---
+
+# 🍓 Ice-Clem 🍓
+
+Bienvenue sur la documentation de l'IA : Ice-Clem.
+ce petit modèle tres simple,
+a été crée et entraîné en 5 minutes, en réponse a une idée éclair,
+apparu lorsque j'étais assez fatiguée j'avoue 🤣.
+
+Le but de ce modèle est de générer des combinaisons loufoques d'aliments qui ont rien a voir entre eux,
+pour vous faire imaginer des plats dégueulasse et vous faire sourir (ou rire j'espère).
+
+le modèle a ete entraîné sur certains mots-clés (les ingrédients),
+et a partir d'un ingrédients de départ,
+génère totalement aléatoirement la suite des ingrédients pour faire votre plat loufoque (a ne pas concrétiser).
+
+les noms des ingrédients sont rédigés en anglais.
+
+## 🌸 Liste des ingrédients 🌸
+
+Voici la listes des ingrédients de départ que vous pouvez utiliser :
+
+pizza
+sushi
+pasta
+soup
+curry
+steak
+salad
+burger
+tacos
+noodles
+rice
+bread
+cake
+cookies
+pie
+chocolate
+vanilla
+strawberry
+spicy
+sour
+
+le modèle générera la suite, de façon aléatoire mais intelligente.
+;) 🔥
+
+# 🩵 Exemple d'utilisation 🩵
+
+```
+from huggingface_hub import hf_hub_download
+import torch
+import json
+import os
+
+# Define your Hugging Face repository name and the filenames
+repo_name = "Clemylia/Ice-Clem" # Make sure this matches the repository name you used
+model_filename = "pytorch_model.bin"
+word_to_index_filename = "word_to_index.json"
+index_to_word_filename = "index_to_word.json"
+
+# Download the files from the Hugging Face Hub
+try:
+    model_path = hf_hub_download(repo_id=repo_name, filename=model_filename)
+    word_to_index_path = hf_hub_download(repo_id=repo_name, filename=word_to_index_filename)
+    index_to_word_path = hf_hub_download(repo_id=repo_name, filename=index_to_word_filename)
+
+    print(f"Downloaded model to: {model_path}")
+    print(f"Downloaded word_to_index to: {word_to_index_path}")
+    print(f"Downloaded index_to_word to: {index_to_word_path}")
+
+    # Load the word_to_index and index_to_word mappings
+    with open(word_to_index_path, 'r') as f:
+        word_to_index = json.load(f)
+
+    with open(index_to_word_path, 'r') as f:
+        index_to_word = json.load(f)
+        # Convert keys back to integers if they were saved as strings
+        index_to_word = {int(k): v for k, v in index_to_word.items()}
+
+
+    # Define the model architecture (must match the architecture used for training)
+    # You'll need the same hyperparameters as before
+    vocab_size = len(word_to_index)
+    embedding_dim = 100 # Make sure this matches your training parameter
+    hidden_dim = 256 # Make sure this matches your training parameter
+    output_dim = vocab_size
+
+    class FoodCombinerModel(torch.nn.Module):
+        def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim):
+            super(FoodCombinerModel, self).__init__()
+            self.embedding = torch.nn.Embedding(vocab_size, embedding_dim)
+            self.lstm = torch.nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
+            self.fc = torch.nn.Linear(hidden_dim, output_dim)
+
+        def forward(self, x):
+            embedded = self.embedding(x)
+            lstm_out, _ = self.lstm(embedded)
+            output = self.fc(lstm_out[:, -1, :])
+            return output
+
+    # Instantiate the model
+    loaded_model = FoodCombinerModel(vocab_size, embedding_dim, hidden_dim, output_dim)
+
+    # Load the saved state dictionary
+    loaded_model.load_state_dict(torch.load(model_path))
+
+    # Set the model to evaluation mode
+    loaded_model.eval()
+
+    print("Model loaded successfully!")
+
+    # Now you can use the loaded_model for generation
+    # Make sure the generate_combination function is defined in a previous cell and accessible
+
+    # Generate a combination using the loaded model
+    starting_phrase_loaded = "sushi" # You can use any word from your vocabulary
+    generated_combination_loaded = generate_combination(loaded_model, starting_phrase_loaded, word_to_index, index_to_word)
+    print(f"\nGenerated combination using loaded model starting with '{starting_phrase_loaded}': {generated_combination_loaded}")
+
+    starting_phrase_loaded_2 = "pizza"
+    generated_combination_loaded_2 = generate_combination(loaded_model, starting_phrase_loaded_2, word_to_index, index_to_word)
+    print(f"Generated combination using loaded model starting with '{starting_phrase_loaded_2}': {generated_combination_loaded_2}")
+
+except Exception as e:
+    print(f"An error occurred: {e}")
+    print("Please ensure the repository name is correct and the files exist on Hugging Face Hub.")
+```