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.gitattributes

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+Food_Recipe.csv filter=lfs diff=lfs merge=lfs -text

Food_Recipe.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03a20c91ce5b5e3faf0ebecd233bb38e586099e29a7091e08a2f4ade0d7b7009
+size 16251035

README.md

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+---
+title: Personal Recipe Generator
+emoji: 🐢
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 5.9.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: a bot that generates recipes for you
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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()

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+Food_Recipe.csv filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+transformers
+tensorflow
+datasets
+fuzzywuzzy
+pandas
+gradio
+torch
+tf-keras