app.py

import streamlit as st
import pandas as pd
import numpy as np
from transformers import AutoTokenizer, AutoModel
import torch
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cosine
import joblib

# Load a pre-trained model and tokenizer
model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Function to get embedding
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()

# Function to classify text using cosine similarity
def classify_text_cosine(embedding, mean_embeddings, threshold=0.5):
    distances = {label: cosine(embedding, mean_embedding) for label, mean_embedding in mean_embeddings.items()}
    min_distance = min(distances.values())
    if min_distance > threshold:
        return "neither"
    predicted_label = min(distances, key=distances.get)
    return predicted_label

# Function to classify text using MLP model
def classify_text_mlp(embedding, mlp_model):
    prediction = mlp_model.predict([embedding])[0]
    return list(label_mapping.keys())[prediction]

# Streamlit app
st.title('Biryani, Pizza, Milk, Pasta, Potatos, Tomato, or Neither Classifier')

# Load the embeddings and labels DataFrame
df = pd.read_csv("embeddings_receipes_final.csv")

# Map labels to integers
label_mapping = {'pizza': 0, 'biryani': 1, 'milk': 2, 'pasta': 3, 'potatos': 4, 'tomato': 5, 'neither': 6}
df['label_int'] = df['label'].map(label_mapping)

# Calculate mean embeddings for each class
embeddings = df.iloc[:, 1:-2]
labels = df['label']
mean_embeddings = {label: embeddings[labels == label].mean(axis=0) for label in label_mapping.keys() if label != 'neither'}

# Load the MLP model
try:
    mlp_model = joblib.load("mlp_model2.joblib")
except Exception as e:
    st.error(f"Error loading MLP model: {e}")
    mlp_model = None

# Check if the DataFrame is loaded correctly
if df.shape[1] < 386:  # 384 embeddings + 1 label + 1 recipe_id + 1 label_int
    st.error(f"Expected DataFrame with 386 columns, but got less than that. Please check your CSV file.")
else:
    # Select classification method
    classification_method = st.selectbox("Select classification method", ["Cosine Similarity", "MLP Model"])

    # Input text
    input_text = st.text_area("Enter text to classify")

    if st.button("Classify"):
        if input_text:
            # Get the embedding for the input text
            embedding = get_embedding(input_text)

            # Ensure the embedding is of the correct dimension
            if embedding.shape[0] != 384:
                st.error(f"Expected embedding of dimension 384, but got {embedding.shape[0]}.")
            else:
                # Classify the input text using the selected method
                if classification_method == "Cosine Similarity":
                    predicted_label = classify_text_cosine(embedding, mean_embeddings)
                elif mlp_model is not None:
                    predicted_label = classify_text_mlp(embedding, mlp_model)
                else:
                    st.error("MLP model is not available.")
                    predicted_label = "unknown"

                # Display the result
                st.write(f"The predicted label is: **{predicted_label}**")

                # Map predicted label to corresponding image
                image_mapping = {
                    'pizza': 'pizza.jpg',
                    'biryani': 'biryani.jpg',
                    'milk': 'milk.jpg',
                    'pasta': 'pasta.jpg',
                    'potatos': 'potatos.jpg',
                    'tomato': 'tomato.jpg',
                    'neither': 'other.jpg'
                }
                st.image(image_mapping[predicted_label], caption=f"Predicted Label: {predicted_label}", use_column_width=True)

        else:
            st.write("Please enter text to classify.")

# # Footer
# st.markdown(
#     """
#     <style>
#     .footer {
#         position: fixed;
#         bottom: 0;
#         width: 100%;
#         text-align: center;
#         padding: 10px;
#         background-color: #f1f1f1;
#         color: black;
#     }
#     </style>
#     <div class="footer">
#         <b>&copy; Shubham Kale and Prof Ganesh Baglar, IIIT Delhi</b>
#     </div>
#     """,
#     unsafe_allow_html=True
# )
 # Add a footer
    st.markdown(
        """
        <style>
        .footer {
            position: fixed;
            left: 0;
            bottom: 0;
            width: 100%;
            background-color: #f1f1f1;
            color: black;
            text-align: center;
            padding: 10px;
        }
        .footer p {
            font-size: 1.2em;
            font-weight: bold;
        }
        </style>
        <div class="footer">
        <p>© Shubham Kale and Prof.Ganesh Bagler, IIIT Delhi</p>
        </div>
        """, unsafe_allow_html=True
    )