app.py

import numpy as np
import pandas as pd
import gradio as gr
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
from fuzzywuzzy import fuzz
from transformers import pipeline
import requests
from PIL import Image
from io import BytesIO

# Load models using pipeline for recipe generation
models = {
    "Flan-T5 Small": pipeline("text2text-generation", model="BhavaishKumar112/flan-t5-small"),
    "GPT-Neo 125M": pipeline("text-generation", model="BhavaishKumar112/gpt-neo-125M"),
    "Final GPT-2 Trained": pipeline("text-generation", model="BhavaishKumar112/finalgpt2trained")
}

# Supported cuisines for recipe generation
cuisines = ["Thai", "Indian", "Chinese", "Italian"]

# Load the dataset for image classification and recipe search
dataset_path = "Food_Recipe.csv"  # Update with your dataset path
data_df = pd.read_csv(dataset_path)

# Load MobileNetV2 pre-trained model for image classification
mobilenet_model = MobileNetV2(weights="imagenet")

# Function to preprocess images
def preprocess_image(image_path, target_size=(224, 224)):
    image = load_img(image_path, target_size=target_size)
    image_array = img_to_array(image)
    image_array = np.expand_dims(image_array, axis=0)
    return preprocess_input(image_array)

# Function to classify an image
def classify_image(image):
    try:
        image_array = preprocess_image(image)
        predictions = mobilenet_model.predict(image_array)
        decoded_predictions = decode_predictions(predictions, top=3)[0]
        return decoded_predictions
    except Exception as e:
        print(f"Error during classification: {e}")
        return []

# Map classification to recipe using fuzzy matching
def map_to_recipe(classification_results):
    for result in classification_results:
        best_match = None
        best_score = 0
        for index, row in data_df.iterrows():
            score = fuzz.partial_ratio(result[1].lower(), row["name"].lower())
            if score > best_score:
                best_score = score
                best_match = row
        if best_score >= 70:
            return best_match
    return None

# Generate recipe summary
def generate_summary(recipe):
    ingredients = recipe.get("ingredients_name", "No ingredients provided")
    time_to_cook = recipe.get("time_to_cook", "Time to cook not provided")
    instructions = recipe.get("instructions", "No instructions provided")
    return f"Ingredients: {ingredients}\n\nTime to Cook: {time_to_cook}\n\nInstructions: {instructions}"

# Function to handle image input and return recipe details
def get_recipe_details(image):
    classification_results = classify_image(image)
    if not classification_results:
        return "Error: No classification results found for the image."
    recipe = map_to_recipe(classification_results)
    if recipe is not None:
        return generate_summary(recipe)
    else:
        return "No matching recipe found for this image."

# Function for recipe generation (as before)
def generate_recipe(input_text, selected_model, selected_cuisine):
    prompt = (
        f"Generate a detailed and structured {selected_cuisine} recipe for {input_text}. "
        f"Include all the necessary details such as ingredients under an 'Ingredients' heading "
        f"and steps under a 'Recipe' heading. Ensure the response is concise and well-organized."
    )
    model = models[selected_model]
    output = model(prompt, max_length=500, num_return_sequences=1)[0]['generated_text']
    return output

# Function to fetch and display the image for a recipe name
def fetch_recipe_image(recipe_name):
    matching_row = data_df[data_df['name'].str.contains(recipe_name, case=False, na=False)]
    if not matching_row.empty:
        image_url = matching_row.iloc[0]['image_url']
        try:
            response = requests.get(image_url)
            img = Image.open(BytesIO(response.content))
            return img
        except Exception as e:
            return f"Error fetching image: {e}"
    else:
        return "No matching recipe found. Please check the recipe name."

# Gradio interface with updated vibrant colors and higher contrast for better readability
def main():
    with gr.Blocks(css="""
        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            background-color: #1c1c1c;  /* Dark background for high contrast */
            margin: 0;
            padding: 0;
            color: #e0e0e0;  /* Light text for contrast */
        }
        .chat-container {
            max-width: 800px;
            margin: 30px auto;
            padding: 20px;
            background: #333333;  /* Dark gray background */
            border-radius: 16px;
            box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
        }
        .chat-header {
            text-align: center;
            font-size: 32px;
            font-weight: bold;
            color: #ff9800;  /* Orange for visibility */
            margin-bottom: 20px;
        }
        .chat-input {
            width: 100%;
            padding: 14px;
            font-size: 16px;
            border-radius: 12px;
            border: 1px solid #ff9800;  /* Orange border */
            margin-bottom: 15px;
            background-color: #424242;  /* Dark input field */
            color: #e0e0e0;  /* Light text */
        }
        .chat-button {
            background-color: #ff9800;
            color: white;
            border: none;
            padding: 12px 24px;
            font-size: 16px;
            border-radius: 12px;
            cursor: pointer;
        }
        .chat-button:hover {
            background-color: #e65100;  /* Darker orange for hover */
        }
        .chat-output {
            padding: 15px;
            background: #424242;  /* Dark gray background for output */
            border-radius: 10px;
            border: 1px solid #616161;  /* Light gray border */
            color: #e0e0e0;  /* Light text */
            white-space: pre-wrap;
            min-height: 120px;
        }
        .tab-title {
            font-weight: bold;
            font-size: 22px;
            color: #ff9800;  /* Orange text for tab title */
        }
        .tab-button {
            background-color: #616161;
            color: #ff9800;
            border: 1px solid #ff9800;
            padding: 12px;
            border-radius: 12px;
        }
        .tab-button:hover {
            background-color: #ff5722;  /* Bright orange for tab button hover */
        }
        .icon {
            font-size: 20px;
            margin-right: 10px;
        }
        .gradio-container {
            margin-top: 20px;
        }
    """) as app:

        with gr.Tab("Recipe Generator"):
            gr.HTML("<div class='chat-container'><div class='chat-header'><i class='icon'>🍽</i>Recipe Generator</div><p class='tab-title'>Enter a recipe name or ingredients, select a cuisine and model, and get structured recipe instructions!</p></div>")
            recipe_input = gr.Textbox(label="Enter Recipe Name or Ingredients", placeholder="e.g., Chicken curry or chicken, garlic, onions", elem_classes=["chat-input"])
            selected_cuisine = gr.Radio(choices=cuisines, label="Cuisine", value="Indian")
            selected_model = gr.Radio(choices=list(models.keys()), label="Model", value="Flan-T5 Small")
            recipe_output = gr.Textbox(label="Recipe", lines=15, elem_classes=["chat-output"])
            generate_button = gr.Button("Generate Recipe", elem_classes=["chat-button"])
            generate_button.click(generate_recipe, inputs=[recipe_input, selected_model, selected_cuisine], outputs=recipe_output)

        with gr.Tab("Recipe Finder from Image"):
            gr.HTML("<div class='chat-container'><div class='chat-header'><i class='icon'>📸</i>Recipe Finder from Image</div><p class='tab-title'>Upload an image of a dish to find a matching recipe.</p></div>")
            image_input = gr.Image(type="filepath", label="Upload an Image")
            image_output = gr.Textbox(label="Recipe Details", lines=10, elem_classes=["chat-output"])
            image_input.change(get_recipe_details, inputs=image_input, outputs=image_output)

        with gr.Tab("Recipe Image Search"):
            gr.HTML("<div class='chat-container'><div class='chat-header'><i class='icon'>📷</i>Recipe Image Search</div><p class='tab-title'>Enter the name of a recipe to view its image.</p></div>")
            recipe_name_input = gr.Textbox(label="Recipe Name", placeholder="e.g., Mixed Sprouts in Chettinad Masala Recipe", elem_classes=["chat-input"])
            recipe_image_output = gr.Image(label="Recipe Image")
            fetch_image_button = gr.Button("Generate Image", elem_classes=["chat-button"])
            fetch_image_button.click(fetch_recipe_image, inputs=recipe_name_input, outputs=recipe_image_output)

    app.launch()

if __name__ == "__main__":
    main()