app.py

import streamlit as st
import torch
import pandas as pd
import numpy as np
from flask import Flask, request, jsonify
from torch_geometric.data import Data
from torch_geometric.nn import GATConv
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

flask_app = Flask(__name__)

class ModeratelySimplifiedGATConvModel(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super().__init__()
        self.conv1 = GATConv(in_channels, hidden_channels, heads=2)
        self.dropout1 = torch.nn.Dropout(0.45)
        self.conv2 = GATConv(hidden_channels * 2, out_channels, heads=1)

    def forward(self, x, edge_index, edge_attr=None):
        x = self.conv1(x, edge_index, edge_attr)
        x = torch.relu(x)
        x = self.dropout1(x)
        x = self.conv2(x, edge_index, edge_attr)
        return x

# Load the dataset and the GATConv model
data = torch.load("graph_data.pt", map_location=torch.device("cpu"))

# Correct the state dictionary's key names
original_state_dict = torch.load("graph_model.pth", map_location=torch.device("cpu"))
corrected_state_dict = {}
for key, value in original_state_dict.items():
    if "lin.weight" in key:
        corrected_state_dict[key.replace("lin.weight", "lin_src.weight")] = value
        corrected_state_dict[key.replace("lin.weight", "lin_dst.weight")] = value
    else:
        corrected_state_dict[key] = value

# Initialize the GATConv model with the corrected state dictionary
gatconv_model = ModeratelySimplifiedGATConvModel(
    in_channels=data.x.shape[1], hidden_channels=32, out_channels=768
)
gatconv_model.load_state_dict(corrected_state_dict)

# Load the BERT-based sentence transformer model
model_bert = SentenceTransformer("all-mpnet-base-v2")

# Ensure the DataFrame is loaded properly
try:
    df = pd.read_json("combined_data.json.gz", orient='records', lines=True, compression='gzip')
except Exception as e:
    st.error(f"Error reading JSON file: {e}")

# Generate GNN-based embeddings
with torch.no_grad():
    all_video_embeddings = gatconv_model(data.x, data.edge_index, data.edge_attr).cpu()

# Function to find the most similar video and recommend the top 10 based on GNN embeddings
def get_similar_and_recommend(input_text):
    # Find the most similar video based on cosine similarity
    embeddings_matrix = np.array(df["embeddings"].tolist())
    input_embedding = model_bert.encode([input_text])[0]
    similarities = cosine_similarity([input_embedding], embeddings_matrix)[0]

    most_similar_index = np.argmax(similarities)  # Find the most similar video

    # Get all features of the most similar video
    most_similar_video_features = df.iloc[most_similar_index].to_dict()

    # Clean up certain fields
    if "text_for_embedding" in most_similar_video_features:
        del most_similar_video_features["text_for_embedding"]
    if "embeddings" in most_similar_video_features:
        del most_similar_video_features["embeddings"]

    # Recommend the top 10 videos based on GNN embeddings
    def recommend_top_10(given_video_index, all_video_embeddings):
        dot_products = [
            torch.dot(all_video_embeddings[given_video_index], all_video_embeddings[i])
            for i in range(all_video_embeddings.shape[0])
        ]
        dot_products[given_video_index] = -float("inf")  # Exclude the most similar video

        top_10_indices = np.argsort(dot_products)[::-1][:10]
        return [df.iloc[idx].to_dict() for idx in top_10_indices]

    top_10_recommended_videos_features = recommend_top_10(most_similar_index, all_video_embeddings)

    # Apply search context to determine weights for GNN results
    user_keywords = input_text.split()  # Create a list of keywords from user input
    video_weights = []
    weight = 1.0  # Initial weight factor

    for keyword in user_keywords:
        if keyword.lower() in df["title"].str.lower().tolist():  # Check for matching keywords
            weight += 0.1  # Increase weight for matching keyword

    # Calculate the weight for each GNN output
    video_weights = [weight] * len(top_10_recommended_videos_features)

    # Clean up certain fields in recommendations
    for recommended_video in top_10_recommended_videos_features:
        if "text_for_embedding" in recommended_video:
            del recommended_video["text_for_embedding"]
        if "embeddings" in recommended_video:
            del recommended_video["embeddings"]

    # Create the output JSON with the most similar video, final recommendations, and weights
    output = {
        "search_context": {
            "input_text": input_text,  # What the user provided
            "weights": video_weights,  # Weights for each GNN-based recommendation
        },
        "most_similar_video": most_similar_video_features,
        "final_recommendations": top_10_recommended_videos_features  # Top 10 recommended videos
    }


    return output

# Create a Streamlit text input widget for entering text and retrieve the most similar video and top 10 recommended videos
user_input = st.text_input("Enter text to find the most similar video")

if user_input:
    recommendations = get_similar_and_recommend(user_input)
    st.json(recommendations)



@flask_app.route('/recommend', methods=['POST'])
def recommend():
    input_text = request.json['input_text']

    recommendations = get_similar_and_recommend(input_text)
    return jsonify(recommendations)


# Create a simple Streamlit interface with instructions
st.title("Video Recommendation API")
st.write("Use POST requests to `/recommend` with JSON data {'input_text': '<your text>'}")

if __name__ == "__main__":
    flask_app.run(host='0.0.0.0', port=8501)