food recommender

Website/pages/feature_3.py

@@ -1,10 +1,127 @@
+import os
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import col, udf, concat_ws, lower
+from pyspark.sql.types import StringType
+import re
+from datetime import datetime
 import streamlit as st
+from dotenv import load_dotenv
+import json
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
 
+# Set environment variables
+os.environ["PYSPARK_PYTHON"] = "/home/pce/anaconda3/envs/spark_env/bin/python3.11"
+os.environ["PYSPARK_DRIVER_PYTHON"] = "/home/pce/anaconda3/envs/spark_env/bin/python3.11"
+os.environ["SPARK_LOCAL_IP"] = "127.0.0.1"
 
-def show_feature3():
-    st.header("Feature 3")
-    st.write(
-        """
-        This is the SpicyBytes Website FEATURE 3
-        """
-    )
+load_dotenv()
+# Set page config for a better appearance
+st.set_page_config(page_title="Food Recommender System", layout="wide")
+
+def create_spark_session():
+    spark = SparkSession.builder \
+        .appName("RecipeProcessing") \
+        .config("spark.driver.host", "127.0.0.1") \
+        .config("spark.hadoop.fs.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem") \
+        .config("spark.hadoop.fs.AbstractFileSystem.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS") \
+        .config("spark.hadoop.google.cloud.auth.service.account.enable", "true") \
+        .config("spark.hadoop.google.cloud.auth.service.account.json.keyfile", "/home/pce/Documents/VBP_Joint_Project-main/formal-atrium-418823-7fbbc75ebbc6.json") \
+        .getOrCreate()
+    spark.sparkContext.setLogLevel("ERROR")
+    return spark
+
+def clean_ingredients(ingredient_list):
+    ingredient_string = ', '.join(ingredient_list)
+    cleaned_ingredients = re.sub(r'\d+\s*(g|oz|ml|tsp|tbs|cups|pint|quart|l|lb|kg|teaspoon|tablespoon|medium|cup|/|-)?\s*|¼\s*|½\s*|¾\s*|to serve|Handful\s*', '', ingredient_string, flags=re.IGNORECASE)
+    cleaned_ingredients = re.sub(r'[\s,-]*$', '', cleaned_ingredients).strip()
+    return cleaned_ingredients
+
+clean_ingredients_udf = udf(clean_ingredients, StringType())
+
+def preprocess_data(input_path):
+    spark = create_spark_session()
+    try:
+        df = spark.read.parquet(input_path)
+
+        # Convert ingredients array to a single string
+        df = df.withColumn("ingredients_string", concat_ws(", ", col("ingredients")))
+
+        # Apply lower function and clean_ingredients function
+        processed_df = df.withColumn("clean_ingredients", clean_ingredients_udf(col("ingredients"))) \
+                         .withColumn("ingredients_lower", lower(col("ingredients_string"))) \
+                         .drop("ingredients", "ingredients_string")
+
+        return processed_df
+    except Exception as e:
+        print(f"Error during processing: {e}")
+        return None
+
+def setup_langchain():
+    model_id = "meta-llama/Meta-Llama-3-8B"
+    # Create a pipeline for text generation
+    text_generator = pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
+
+    def generate_recipe(ingredients):
+        prompt = f"As a food recommender, create different delicious food from these ingredients: {ingredients}. Include the food name, detailed steps, and estimated time."
+        response = text_generator(prompt, max_length=500, num_return_sequences=1)
+        return response[0]['generated_text']
+
+    return generate_recipe
+
+generate_recipe = setup_langchain()
+
+def find_or_generate_recipes(processed_df, ingredients):
+    pattern = '|'.join([f"(?i){re.escape(ingredient)}" for ingredient in ingredients])
+    matched_recipes = processed_df.filter(processed_df.clean_ingredients.rlike(pattern))
+    if matched_recipes.count() > 0:
+        recipes = [{"Food name": row.food_name, "Ingredients": row.clean_ingredients, "Description": row.description} for row in matched_recipes.collect()]
+        return recipes
+    else:
+        generated_recipe = generate_recipe(', '.join(ingredients))
+        return [{"generated_recipe": generated_recipe}]
+
+def initialize():
+    input_path = "gs://spicy_1/mealdb_20240407221823.parquet"
+    processed_df = preprocess_data(input_path)
+    return processed_df
+
+processed_df = initialize()
+
+st.title("Food Generator")
+
+user_ingredients = st.text_input("Enter ingredients, separated by commas", "rice, tomatoes")
+ingredients_list = [ingredient.strip() for ingredient in user_ingredients.split(',')]
+
+if st.button("Generate Recipe"):
+    if processed_df is not None:
+        recipes_or_generated = find_or_generate_recipes(processed_df, ingredients_list)
+        if 'generated_recipe' in recipes_or_generated[0]:
+            st.write("Generated Recipe:")
+            st.write(recipes_or_generated[0]['generated_recipe'])
+        else:
+            st.write("Recipes Found:")
+            for recipe in recipes_or_generated:
+                st.subheader(recipe['Food name'])
+                st.write("Description:", recipe['Description'])
+    else:
+        st.error("Failed to load data.")
+
+# Custom CSS for footer
+st.markdown("""
+    <style>
+        footer {visibility: hidden;}
+        .footer {
+            position: fixed;
+            left: 0;
+            bottom: 0;
+            width: 100%;
+            background-color: #f1f1f1;
+            color: black;
+            text-align: center;
+        }
+    </style>
+    <div class="footer">
+        <p>Developed by SpicyBytes</p>
+    </div>
+""", unsafe_allow_html=True)