Delete app.py

app.py

@@ -1,231 +0,0 @@
-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 professional design
-def main():
-    with gr.Blocks(css="""
-        /* General Body Styling */
-        body {
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-            background-color: #f5f5f5;  /* Light background */
-            margin: 0;
-            padding: 0;
-            color: #333;  /* Dark text */
-        }
-
-        /* Header Styling */
-        .header {
-            background-color: #006064; /* Professional Blue */
-            color: white;
-            padding: 20px;
-            font-size: 24px;
-            font-weight: bold;
-            text-align: center;
-            border-radius: 10px;
-        }
-
-        /* Card Style for Tabs */
-        .tab-container {
-            background-color: white;
-            padding: 20px;
-            border-radius: 8px;
-            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-            margin-top: 20px;
-        }
-
-        /* Card for each input section */
-        .card {
-            background-color: #ffffff;
-            padding: 15px;
-            border-radius: 8px;
-            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
-            margin-bottom: 20px;
-        }
-
-        .card-title {
-            font-size: 18px;
-            font-weight: bold;
-            color: #006064;
-            margin-bottom: 10px;
-        }
-
-        /* Inputs and Buttons */
-        .input-text, .radio, .button {
-            width: 100%;
-            padding: 12px;
-            border-radius: 8px;
-            border: 1px solid #ddd;
-            background-color: #f5f5f5;
-            color: #333;
-            font-size: 16px;
-        }
-
-        .input-text:focus, .radio:focus, .button:focus {
-            border-color: #006064;
-        }
-
-        .button {
-            background-color: #006064;
-            color: white;
-            font-weight: bold;
-            cursor: pointer;
-        }
-
-        .button:hover {
-            background-color: #004d40; /* Darker shade of blue on hover */
-        }
-
-        /* Output Box */
-        .output-box {
-            background-color: #ffffff;
-            padding: 20px;
-            border-radius: 8px;
-            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
-            font-size: 16px;
-            color: #333;
-            max-height: 350px;
-            overflow-y: auto;
-            white-space: pre-wrap;
-        }
-
-        .output-box img {
-            max-width: 100%;
-            border-radius: 8px;
-        }
-
-        /* Tab Title */
-        .tab-title {
-            font-size: 22px;
-            font-weight: bold;
-            color: #006064;
-            margin-bottom: 10px;
-        }
-    """) as app:
-        
-        with gr.Tab("Recipe Generator"):
-            gr.HTML("<div class='header'>Recipe Generator</div>")
-            with gr.Column(visible=True):
-                with gr.Box(elem_classes=["tab-container"]):
-                    recipe_input = gr.Textbox(label="Enter Recipe Name or Ingredients", placeholder="e.g., Chicken curry or chicken, garlic, onions", elem_classes=["input-text"])
-                    selected_cuisine = gr.Radio(choices=cuisines, label="Cuisine", value="Indian", elem_classes=["radio"])
-                    selected_model = gr.Radio(choices=list(models.keys()), label="Model", value="Flan-T5 Small", elem_classes=["radio"])
-                    generate_button = gr.Button("Generate Recipe", elem_classes=["button"])
-                    recipe_output = gr.Textbox(label="Recipe", lines=15, elem_classes=["output-box"])
-
-                    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='header'>Recipe Finder from Image</div>")
-            with gr.Column(visible=True):
-                with gr.Box(elem_classes=["tab-container"]):
-                    image_input = gr.Image(type="filepath", label="Upload an Image")
-                    image_output = gr.Textbox(label="Recipe Details", lines=10, elem_classes=["output-box"])
-                    image_input.change(get_recipe_details, inputs=image_input, outputs=image_output)
-
-        with gr.Tab("Recipe Image Search"):
-            gr.HTML("<div class='header'>Recipe Image Search</div>")
-            with gr.Column(visible=True):
-                with gr.Box(elem_classes=["tab-container"]):
-                    recipe_name_input = gr.Textbox(label="Recipe Name", placeholder="e.g., Mixed Sprouts in Chettinad Masala Recipe", elem_classes=["input-text"])
-                    fetch_image_button