Upload 2 files

recipeteller.py

@@ -0,0 +1,206 @@
+# -*- coding: utf-8 -*-
+"""recipeteller.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1cs8uKGpq9jzvso3vhAKjy9iSwbV_gnGM
+"""
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", model="google/flan-t5-base")
+
+pip install datasets
+
+pip install datasets
+
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Zappandy/recipe_nlg")
+
+# Sample 5% of the dataset
+train_sample = dataset["train"].shuffle(seed=42).select(range(int(0.05 * len(dataset["train"]))))
+val_sample = dataset["validation"].shuffle(seed=42).select(range(int(0.05 * len(dataset["validation"]))))
+print(f"Training samples: {len(train_sample)}, Validation samples: {len(val_sample)}")
+
+print(dataset.column_names)
+
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+def preprocess_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+def preprocess_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True, return_tensors="pt")
+
+def preprocess_function(examples):
+    try:
+        return tokenizer(examples['text'], padding="max_length", truncation=True)
+    except KeyError as e:
+        print(f"KeyError: {e} - Available columns: {examples.keys()}")
+        raise
+
+from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
+
+# Load dataset (replace with your own dataset or path)
+dataset = load_dataset('imdb')
+
+# Initialize the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+# Define the preprocessing function
+def preprocess_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+# Reduce the dataset size for faster training
+small_train_dataset = dataset['train'].select(range(2000))  # Use only 2000 samples for training
+small_test_dataset = dataset['test'].select(range(1000))    # Use only 1000 samples for testing
+
+# Tokenize the datasets
+tokenized_train = small_train_dataset.map(preprocess_function, batched=True)
+tokenized_val = small_test_dataset.map(preprocess_function, batched=True)
+
+# Define the model (replace with your own model or pre-trained model)
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+
+# Define the training arguments
+training_args = TrainingArguments(
+    output_dir='./results',          # output directory
+    evaluation_strategy="epoch",     # evaluate after each epoch
+    per_device_train_batch_size=16,  # batch size for training
+    per_device_eval_batch_size=64,   # batch size for evaluation
+    num_train_epochs=1,              # number of training epochs (reduced for speed)
+    weight_decay=0.01,               # strength of weight decay
+    save_total_limit=1,              # keep only the most recent checkpoint
+    logging_dir='./logs',            # directory for storing logs
+    logging_steps=10,                # log every 10 steps
+)
+
+# Create Trainer instance
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_val,
+    tokenizer=tokenizer
+)
+
+# Start training
+trainer.train()
+
+eval_results = trainer.evaluate()
+print(eval_results)
+
+# Save the model and tokenizer
+model.save_pretrained('./trained_model')
+tokenizer.save_pretrained('./trained_model')
+
+from transformers import pipeline
+
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('./trained_model')
+tokenizer = AutoTokenizer.from_pretrained('./trained_model')
+
+# Create a pipeline for text classification
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Make predictions
+predictions = classifier("This is a great movie!")
+print(predictions)
+
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",  # Evaluate every epoch
+    save_strategy="epoch",  # Save the model at the end of each epoch
+    save_total_limit=3,  # Keep only the last 3 checkpoints
+)
+
+pip install gradio
+
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+# Load the trained model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('./trained_model')
+tokenizer = AutoTokenizer.from_pretrained('./trained_model')
+
+# Create a pipeline for text classification
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Define a function that takes input text and returnimport gradio as gr
+
+# Define the recipe suggestion function
+def suggest_recipe(ingredients):
+    # Example recipe database
+    recipes = {
+
+
+    # Pakistani Cuisine
+    "biryani": "Ingredients: basmati rice, chicken, yogurt, spices, onions, tomatoes. Instructions: Marinate chicken, cook with spices and yogurt, and layer with rice. Steam together.",
+    "karahi": "Ingredients: chicken, tomatoes, ginger, garlic, green chilies, spices. Instructions: Cook chicken with spices and tomatoes in a wok until tender.",
+    "nihari": "Ingredients: beef shank, flour, ghee, ginger, garlic, spices. Instructions: Slow cook beef with spices and serve with naan.",
+    "haleem": "Ingredients: wheat, lentils, beef, ginger, garlic, spices. Instructions: Cook all ingredients together until a thick consistency is achieved.",
+    "seekh kebab": "Ingredients: ground beef, onions, spices, ginger, garlic. Instructions: Mix ingredients, shape onto skewers, and grill until cooked.",
+    "chapli kebab": "Ingredients: minced meat, pomegranate seeds, spices, onions, tomatoes. Instructions: Shape into patties and shallow fry until golden.",
+
+    # Indian Cuisine
+    "butter chicken": "Ingredients: chicken, butter, cream, tomatoes, spices. Instructions: Cook chicken, make a rich tomato-based sauce with cream, and simmer together.",
+    "palak paneer": "Ingredients: spinach, paneer, cream, garlic, spices. Instructions: Cook spinach with spices, blend, and add paneer cubes.",
+    "masala dosa": "Ingredients: rice, lentils, potato, curry leaves, spices. Instructions: Make rice and lentil batter, cook thin pancakes, and fill with spiced potato mixture.",
+    "chole bhature": "Ingredients: chickpeas, tomatoes, onions, flour, spices. Instructions: Cook chickpeas with spices, and serve with deep-fried bread.",
+    "samosa": "Ingredients: flour, potatoes, peas, spices. Instructions: Make a dough, fill with spiced potato mixture, and deep fry.",
+    "tandoori chicken": "Ingredients: chicken, yogurt, tandoori spices, lemon juice. Instructions: Marinate chicken, and cook in a tandoor or oven until charred.",
+
+    # Chinese Cuisine
+    "dumplings": "Ingredients: flour, ground meat, cabbage, soy sauce, ginger. Instructions: Make dough, prepare filling, and steam or fry dumplings.",
+    "sweet and sour chicken": "Ingredients: chicken, bell peppers, pineapple, soy sauce, vinegar. Instructions: Stir-fry chicken with vegetables and a tangy sweet sauce.",
+    "fried rice": "Ingredients: rice, soy sauce, eggs, vegetables, garlic. Instructions: Stir-fry rice with soy sauce, eggs, and vegetables.",
+    "hot and sour soup": "Ingredients: tofu, mushrooms, soy sauce, vinegar, spices. Instructions: Cook all ingredients together in a spicy, tangy broth.",
+    "kung pao chicken": "Ingredients: chicken, peanuts, dried chilies, soy sauce, garlic. Instructions: Stir-fry chicken with peanuts and a savory sauce.",
+    "spring rolls": "Ingredients: spring roll wrappers, vegetables, soy sauce. Instructions: Fill wrappers with vegetables, roll, and deep fry.",
+
+    # Western Cuisine
+    "fish and chips": "Ingredients: fish fillets, potatoes, flour, eggs, breadcrumbs. Instructions: Coat fish in batter, fry with chips, and serve with tartar sauce.",
+    "caesar salad": "Ingredients: lettuce, croutons, Parmesan cheese, Caesar dressing. Instructions: Toss lettuce with croutons and dressing, garnish with Parmesan.",
+    "spaghetti carbonara": "Ingredients: spaghetti, eggs, Parmesan cheese, pancetta, black pepper. Instructions: Cook spaghetti, mix with sauce made of eggs, cheese, and pancetta.",
+    "pancakes": "Ingredients: flour, eggs, milk, sugar, butter. Instructions: Mix ingredients and cook on a hot griddle until golden brown.",
+    "hamburger": "Ingredients: ground beef, burger buns, lettuce, tomato, cheese. Instructions: Shape beef into patties, grill, and assemble burger with toppings.",
+    "apple pie": "Ingredients: apples, sugar, flour, butter, cinnamon. Instructions: Make pie crust, fill with spiced apples, and bake until golden."
+}
+
+
+    # Convert the ingredients to lowercase for case-insensitive matching
+    ingredients = ingredients.lower()
+
+    # Check if any of the recipes match the ingredients
+    for dish, recipe in recipes.items():
+        if dish in ingredients:
+            return recipe
+
+    return "Sorry, we couldn't find a recipe for these ingredients."
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=suggest_recipe,  # Function to call for generating recipe
+    inputs=gr.Textbox(label="Enter Ingredients (comma-separated)"),  # Input for ingredients
+    outputs=gr.Textbox(label="Recipe Suggestion"),  # Output for recipe
+    title="Recipe Suggestion App",  # Title of the app
+    description="Enter the ingredients you have, and I'll suggest a recipe for you."  # Description
+)
+
+# Launch the Gradio interface with a public link
+interface.launch(share=True)  # share=True to generate a public link
+
+
+

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+transformers
+datasets
+torch  # or tensorflow
+numpy
+pandas