app.py

import streamlit as st
import numpy as np
from transformers import AutoModel
import plotly.graph_objects as go
from sklearn.manifold import MDS
import pandas as pd
import torch

# Page configuration
st.set_page_config(
    page_title="Jina Embeddings Explorer",
    page_icon="🔮",
    layout="wide"
)

# Custom CSS
st.markdown("""
    <style>
    .title-font {
        font-size: 28px !important;
        font-weight: bold;
        color: #2c3e50;
    }
    </style>
    """, unsafe_allow_html=True)

@st.cache_resource
def load_model():
    return AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)

model = load_model()

def get_embeddings(texts, task="text-matching"):
    """Get embeddings using Jina v3 model"""
    with torch.no_grad():
        embeddings = model.encode(texts, task=task)
    return embeddings

def create_similarity_based_visualization(texts, task="text-matching"):
    """Create visualization based on similarity distances"""
    n = len(texts)
    
    # Get embeddings
    embeddings = get_embeddings(texts, task=task)
    
    # Calculate similarity matrix using cosine similarity
    similarity_matrix = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            similarity_matrix[i][j] = np.dot(embeddings[i], embeddings[j]) / (
                np.linalg.norm(embeddings[i]) * np.linalg.norm(embeddings[j]))
    
    # Convert similarities to distances
    distance_matrix = 1 - similarity_matrix
    
    # Use MDS for visualization
    mds = MDS(n_components=3, dissimilarity='precomputed', random_state=42)
    coords = mds.fit_transform(distance_matrix)
    
    # Create 3D visualization
    fig = go.Figure()
    
    # Add points
    fig.add_trace(go.Scatter3d(
        x=coords[:, 0],
        y=coords[:, 1],
        z=coords[:, 2],
        mode='markers+text',
        text=texts,
        textposition='top center',
        marker=dict(
            size=10,
            color=list(range(len(texts))),
            colorscale='Viridis',
            opacity=0.8
        ),
        name='Texts'
    ))
    
    # Add lines between points
    for i in range(n):
        for j in range(i+1, n):
            opacity = max(0.1, min(1.0, similarity_matrix[i,j]))
            fig.add_trace(go.Scatter3d(
                x=[coords[i,0], coords[j,0]],
                y=[coords[i,1], coords[j,1]],
                z=[coords[i,2], coords[j,2]],
                mode='lines',
                line=dict(
                    color='gray',
                    width=2
                ),
                opacity=opacity,
                showlegend=False,
                hoverinfo='skip'
            ))
    
    fig.update_layout(
        title=f"3D Similarity Visualization (Task: {task})",
        scene=dict(
            xaxis_title="Dimension 1",
            yaxis_title="Dimension 2",
            zaxis_title="Dimension 3",
            camera=dict(
                up=dict(x=0, y=0, z=1),
                center=dict(x=0, y=0, z=0),
                eye=dict(x=1.5, y=1.5, z=1.5)
            )
        ),
        height=700
    )
    return fig, similarity_matrix

def main():
    st.title("🔮 Jina Embeddings v3 Explorer")
    st.markdown("<p class='title-font'>Explore text similarities using state-of-the-art embeddings</p>", 
                unsafe_allow_html=True)
    
    with st.expander("ℹ️ About Jina Embeddings v3", expanded=True):
        st.markdown("""
        This tool uses Jina Embeddings v3, a powerful multilingual embedding model that supports:
        - Multiple tasks: text-matching, retrieval, classification, separation
        - Long sequences: up to 8192 tokens
        - 30+ languages
        - State-of-the-art performance
        """)
    
    # Task selection
    task = st.selectbox(
        "Select Task",
        ["text-matching", "retrieval.query", "retrieval.passage", "separation", "classification"],
        help="Different tasks optimize embeddings for specific use cases"
    )
    
    # Example templates
    examples = {
        "Similar Concepts": [
            "I love programming in Python",
            "Coding with Python is amazing",
            "Software development is fun",
            "I enjoy writing code"
        ],
        "Multilingual": [
            "Hello, how are you?",
            "Hola, ¿cómo estás?",
            "Bonjour, comment allez-vous?",
            "你好，你好吗？"
        ],
        "Technical Concepts": [
            "Machine learning is a subset of artificial intelligence",
            "AI systems can learn from data",
            "Neural networks process information",
            "Deep learning models require training"
        ]
    }
    
    col1, col2 = st.columns([3, 1])
    with col1:
        selected_example = st.selectbox("Choose an example set:", list(examples.keys()))
    with col2:
        if st.button("Load Example"):
            st.session_state.texts = examples[selected_example]
    
    # Text input
    num_texts = st.slider("Number of texts:", 2, 6, 4)
    texts = []
    
    for i in range(num_texts):
        default_text = (examples[selected_example][i] 
                       if selected_example in examples and i < len(examples[selected_example])
                       else f"Example text {i+1}")
        text = st.text_area(
            f"Text {i+1}",
            value=default_text,
            height=100,
            key=f"text_{i}"
        )
        texts.append(text)
    
    if st.button("Analyze Texts", type="primary"):
        if all(texts):
            fig, similarity_matrix = create_similarity_based_visualization(texts, task)
            
            # Display visualization
            st.plotly_chart(fig, use_container_width=True)
            
            # Show similarity matrix
            st.markdown("### Similarity Matrix")
            fig_matrix = go.Figure(data=go.Heatmap(
                z=similarity_matrix,
                x=[f"Text {i+1}" for i in range(len(texts))],
                y=[f"Text {i+1}" for i in range(len(texts))],
                colorscale='Viridis',
                text=np.round(similarity_matrix, 3),
                texttemplate='%{text}',
                textfont={"size": 12},
            ))
            
            fig_matrix.update_layout(
                title=f"Similarity Matrix (Task: {task})",
                height=400
            )
            
            st.plotly_chart(fig_matrix, use_container_width=True)
            
            # Interpretation
            st.markdown("### 📊 Similarity Analysis")
            for i in range(len(texts)):
                for j in range(i+1, len(texts)):
                    similarity = similarity_matrix[i][j]
                    interpretation = (
                        "🟢 Very Similar" if similarity > 0.8
                        else "🟡 Moderately Similar" if similarity > 0.5
                        else "🔴 Different"
                    )
                    st.write(f"{interpretation} ({similarity:.3f}): Text {i+1} vs Text {j+1}")

if __name__ == "__main__":
    main()