Update app.py

app.py

@@ -1,7 +1,7 @@
 import numpy as np 
 import pandas as pd  # type: ignore
 import os
-from tensorflow import keras
+import keras
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 import pymongo
@@ -20,6 +20,9 @@ st.set_page_config(
     page_icon="🍴"
 )
 
+st.write(tf.__version__)
+st.write(keras.__version__)
+
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
 mongo_uri = os.getenv("MONGO_URI_RAG_RECIPE")
 
@@ -173,182 +176,3 @@ def get_response(query):
 ##############################################
 # Classifier
 img_size = 224
-
-@st.cache_resource
-def loadModel():
-    model = load_model('efficientnet-fine-6.keras')
-    return model
-
-model = loadModel()
-
-class_names = [
-    "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", 
-    "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", 
-    "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "chicken_curry", 
-    "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", 
-    "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", 
-    "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", 
-    "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", 
-    "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", 
-    "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", 
-    "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", 
-    "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", 
-    "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", 
-    "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", 
-    "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", 
-    "tuna_tartare", "waffles"
-]
-
-def classifyImage(input_image):
-    input_image = input_image.resize((img_size, img_size))
-    input_array = tf.keras.utils.img_to_array(input_image)
-
-    # Add a batch dimension 
-    input_array = tf.expand_dims(input_array, 0)  # (1, 224, 224, 3)
-    
-    predictions = model.predict(input_array)[0]
-    print(f"Predictions: {predictions}")
-
-    # Sort predictions to get top 5
-    top_indices = np.argsort(predictions)[-5:][::-1]
-    
-    # Prepare the top 5 predictions with their class names and percentages
-    top_predictions = [(class_names[i], predictions[i] * 100) for i in top_indices]
-    for i, (class_name, confidence) in enumerate(top_predictions, 1):
-        print(f"{i}. Predicted {class_name} with {confidence:.2f}% Confidence")
-
-    return top_predictions
-
-def capitalize_after_number(input_string):
-    # Split the string on the first period
-    if ". " in input_string:
-        num, text = input_string.split(". ", 1)
-        return f"{num}. {text.capitalize()}"
-    return input_string
-##############################################
-
-#for displaying RAG recipe response
-def display_response(response):
-    """
-    Function to format a JSON response into Streamlit's `st.write()` format.
-    """
-    if isinstance(response, str):
-        # Convert JSON string to dictionary if necessary
-        response = json.loads(response)
-    
-    st.write("### Recipe Details")
-    st.write(f"**Name:** {response['name'].capitalize()}")
-    st.write(f"**Preparation Time:** {response['minutes']} minutes")
-    st.write(f"**Description:** {response['description'].capitalize()}")
-    st.write(f"**Tags:** {', '.join(response['tags'])}")
-    st.write("### Ingredients")
-    st.write(", ".join([ingredient.capitalize() for ingredient in response['ingredients']]))
-    st.write(f"**Total Ingredients:** {response['n_ingredients']}")
-    st.write("### Nutrition Information (per serving)")
-    st.write(", ".join(response['formatted_nutrition']))
-    st.write(f"**Number of Steps:** {response['n_steps']}")
-    st.write("### Steps")
-    for step in response['formatted_steps']:
-        st.write(capitalize_after_number(step))
-
-def display_dishes_in_grid(dishes, cols=3):
-    rows = len(dishes) // cols + int(len(dishes) % cols > 0)
-    for i in range(rows):
-        cols_data = dishes[i*cols:(i+1)*cols]
-        cols_list = st.columns(len(cols_data))
-        for col, dish in zip(cols_list, cols_data):
-            with col:
-                st.sidebar.write(dish.replace("_", " ").capitalize())
-# #Streamlit
-
-#Left sidebar title
-st.sidebar.markdown(
-    "<h1 style='font-size:32px;'>RAG Recipe</h1>", 
-    unsafe_allow_html=True
-)
-
-st.sidebar.write("Upload an image and/or enter a query to get started! Explore our trained dish types listed below for guidance.")
-
-uploaded_image = st.sidebar.file_uploader("Choose an image:", type="jpg")
-query = st.sidebar.text_area("Enter your query:", height=100)
-
-# gap
-st.sidebar.markdown("<br><br><br>", unsafe_allow_html=True)
-selected_dish = st.sidebar.selectbox(
-    "Search for a dish that our model can classify:", 
-    options=class_names,
-    index=0  
-)
-
-# Right title
-st.title("Results")
-#################
-
-# Image Classification Section
-if uploaded_image and query:
-    # Open the image
-    input_image = Image.open(uploaded_image)
-
-    # Display the image
-    st.image(input_image, caption="Uploaded Image.", use_container_width=True)
-        
-    predictions = classifyImage(input_image)
-    fpredictions = ""
-
-    # Show the top predictions with percentages
-    st.write("Top Predictions:")
-    for class_name, confidence in predictions:
-        if int(confidence) > 0.05:
-            fpredictions += f"{class_name}: {confidence:.2f}%,"
-        st.write(f"{class_name}: {confidence:.2f}%")
-    print(fpredictions)
-
-    # call openai to pick the best classification result based on query
-    openAICall = [
-        SystemMessage(
-            content = "You are a helpful assistant that identifies the best match between classified food items and a user's request based on provided classifications and keywords."
-        ),
-        HumanMessage( 
-            content = f"""
-                Based on the following image classification with percentages of each food:
-                {fpredictions}
-                And the following user request:
-                {query}
-                Return to me JUST ONE of the classified images that most relates to the user request, based on the relevance of the user query.
-                in the format: [dish]
-            """
-        ),
-    ]
-
-    # Call the OpenAI API
-    openAIresponse = llm.invoke(openAICall)
-    print("AI CALL RESPONSE: ", openAIresponse.content)
-
-    # RAG the openai response and display
-    print("RAG INPUT", openAIresponse.content + " " + query)
-    RAGresponse = get_response(openAIresponse.content + " " + query)
-    display_response(RAGresponse)
-elif uploaded_image is not None:
-    # Open the image
-    input_image = Image.open(uploaded_image)
-
-    # Display the image
-    st.image(input_image, caption="Uploaded Image.", use_column_width=True)
-
-    # Classify the image and display the result
-    predictions = classifyImage(input_image)
-    fpredictions = ""
-
-    # Show the top predictions with percentages
-    st.write("Top Predictions:")
-    for class_name, confidence in predictions:
-        if int(confidence) > 0.05:
-            fpredictions += f"{class_name}: {confidence:.2f}%,"
-        st.write(f"{class_name}: {confidence:.2f}%")
-    print(fpredictions)
-
-elif query:
-    response = get_response(query)
-    display_response(response)
-else:
-    st.write("Please input an image and/or a prompt.")