app.py

import streamlit as st
import os
import google.generativeai as genai
from dotenv import load_dotenv
import time
from typing import Any, List, Optional
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import Adam

# Load environment variables
load_dotenv()
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# Configure Generative AI model
if GOOGLE_API_KEY:
    genai.configure(api_key=GOOGLE_API_KEY)
else:
    st.error(
        "Google AI Studio API key not found. Please add it to your .env file.  "
        "You can obtain an API key from https://makersuite.google.com/."
    )
    st.stop()

st.title("Embeddings and Vector Search Demo")
st.subheader("Explore Embeddings and Vector Databases")

# Sidebar for explanations
with st.sidebar:
    st.header("Embeddings and Vector Search")
    st.markdown(
        """
        This app demonstrates how embeddings and vector databases can be used for various tasks.
        """
    )
    st.subheader("Key Concepts:")
    st.markdown(
        """
        -   **Embeddings**: Numerical representations of text, capturing semantic meaning.
        -   **Vector Databases**:  Databases optimized for storing and querying vectors (simulated here).
        -   **Retrieval Augmented Generation (RAG)**:  Combining retrieval with LLM generation.
        -   **Cosine Similarity**:  A measure of similarity between two vectors.
        -   **Neural Networks**: Using embeddings as input for classification.
        """
    )
    st.subheader("Whitepaper Insights")
    st.markdown(
        """
        -   Efficient similarity search using vector indexes (e.g., ANN).
        -   Handling large datasets and scalability considerations.
        -   Applications of embeddings: search, recommendation, classification, etc.
        """
    )

# --- Helper Functions ---
def code_block(text: str, language: str = "text") -> None:
    """Displays text as a formatted code block in Streamlit."""
    st.markdown(f"```{language}\n{text}\n```", unsafe_allow_html=True)

def display_response(response: Any) -> None:
    """Displays the model's response."""
    if response and hasattr(response, "text"):
        st.subheader("Generated Response:")
        st.markdown(response.text)
    else:
        st.error("Failed to generate a response.")

def generate_embeddings(texts: List[str], model_name: str = "models/embedding-001") -> Optional[List[List[float]]]:
    """Generates embeddings for a list of texts using a specified model.
    Args:
        texts: List of text strings.
        model_name: Name of the embedding model.
    Returns:
        List of embeddings (list of floats) or None on error.
    """
    try:
        # Use the embedding model directly
        embeddings = []
        for text in texts:
            result = genai.embed_content(
                model=model_name,
                content=text,
                task_type="retrieval_document"  # or "retrieval_query" for queries
            )
            embeddings.append(result['embedding'])
        return embeddings
    except Exception as e:
        st.error(f"Error generating embeddings with model '{model_name}': {e}")
        return None

def generate_with_retry(prompt: str, model_name: str, generation_config: genai.types.GenerationConfig, max_retries: int = 3, delay: int = 5) -> Any:
    """Generates content with retry logic and error handling."""
    for i in range(max_retries):
        try:
            model = genai.GenerativeModel(model_name)
            response = model.generate_content(prompt, generation_config=generation_config)
            return response
        except Exception as e:
            error_message = str(e)
            st.warning(f"Error during generation (attempt {i + 1}/{max_retries}): {error_message}")
            if "404" in error_message and "not found" in error_message:
                st.error(
                    f"Model '{model_name}' is not available or not supported.  Please select a different model."
                )
                return None
            elif i < max_retries - 1:
                st.info(f"Retrying in {delay} seconds...")
                time.sleep(delay)
            else:
                st.error(f"Failed to generate content after {max_retries} attempts.  Please check your prompt and model.")
                return None
    return None

def calculate_similarity(embedding1: List[float], embedding2: List[float]) -> float:
    """Calculates the cosine similarity between two embeddings."""
    return cosine_similarity(np.array(embedding1).reshape(1, -1), np.array(embedding2).reshape(1, -1))[0][0]

def create_and_train_model(
    embeddings: List[List[float]],
    labels: List[int],
    num_classes: int,
    epochs: int,
    batch_size: int,
    learning_rate: float,
    optimizer_str: str
) -> tf.keras.Model:
    """Creates and trains a neural network for classification."""
    model = Sequential([
        Input(shape=(len(embeddings[0]),),  # Fixed the double comma here
        Dense(64, activation='relu'),
        Dense(32, activation='relu'),
        Dense(num_classes, activation='softmax')
              ])

    if optimizer_str.lower() == 'adam':
        optimizer = Adam(learning_rate=learning_rate)
    elif optimizer_str.lower() == 'sgd':
        optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
    elif optimizer_str.lower() == 'rmsprop':
        optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
    else:
        optimizer = Adam(learning_rate=learning_rate)

    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
    encoded_labels = to_categorical(labels, num_classes=num_classes)
    model.fit(np.array(embeddings), encoded_labels, epochs=epochs, batch_size=batch_size, verbose=0)
    return model
# --- RAG Question Answering ---
st.header("RAG Question Answering")
rag_model_name = st.selectbox("Select model for RAG:", ["gemini-pro"], index=0)
rag_embedding_model = st.selectbox("Select embedding model for RAG:", ["models/embedding-001"], index=0)
rag_context = st.text_area(
    "Enter your context documents:",
    "Relevant information to answer the question.  Separate documents with newlines.",
    height=150,
)
rag_question = st.text_area("Ask a question about the context:", "What is the main topic?", height=70)
rag_max_context_length = st.number_input("Maximum Context Length", min_value=100, max_value=2000, value=500, step=100)

if st.button("Answer with RAG"):
    if not rag_context or not rag_question:
        st.warning("Please provide both context and a question.")
    else:
        with st.spinner("Generating answer..."):
            try:
                # 1. Generate embeddings for the context
                context_embeddings = generate_embeddings(rag_context.split('\n'), rag_embedding_model)
                if not context_embeddings:
                    st.stop()

                # 2. Generate embedding for the question
                question_embedding = generate_embeddings([rag_question], rag_embedding_model)
                if not question_embedding:
                    st.stop()

                # 3. Calculate similarity scores
                similarities = cosine_similarity(np.array(question_embedding).reshape(1, -1), np.array(context_embeddings))[0]

                # 4. Find the most relevant document(s)
                most_relevant_index = np.argmax(similarities)
                relevant_context = rag_context.split('\n')[most_relevant_index]
                if len(relevant_context) > rag_max_context_length:
                    relevant_context = relevant_context[:rag_max_context_length]

                # 5. Construct the prompt
                rag_prompt = f"Use the following context to answer the question: '{rag_question}'.\nContext: {relevant_context}"

                # 6. Generate the answer
                response = generate_with_retry(rag_prompt, rag_model_name, generation_config=genai.types.GenerationConfig())
                if response:
                    display_response(response)
            except Exception as e:
                st.error(f"An error occurred: {e}")

# --- Text Similarity ---
st.header("Text Similarity")
similarity_embedding_model = st.selectbox("Select embedding model for similarity:", ["models/embedding-001"], index=0)
text1 = st.text_area("Enter text 1:", "This is the first sentence.", height=70)
text2 = st.text_area("Enter text 2:", "This is a similar sentence.", height=70)

if st.button("Calculate Similarity"):
    if not text1 or not text2:
        st.warning("Please provide both texts.")
    else:
        with st.spinner("Calculating similarity..."):
            try:
                embeddings = generate_embeddings([text1, text2], similarity_embedding_model)
                if not embeddings:
                    st.stop()
                similarity = calculate_similarity(embeddings[0], embeddings[1])
                st.subheader("Cosine Similarity:")
                st.write(similarity)
            except Exception as e:
                st.error(f"An error occurred: {e}")

# --- Neural Classification ---
st.header("Neural Classification with Embeddings")
classification_embedding_model = st.selectbox("Select embedding model for classification:", ["models/embedding-001"], index=0)
classification_data = st.text_area(
    "Enter your training data (text, label pairs), separated by newlines.  Example: text1,0\\ntext2,1",
    "text1,0\ntext2,1\ntext3,0\ntext4,1",
    height=150,
)
classification_prompt = st.text_area("Enter text to classify:", "This is a test text.", height=70)
num_epochs = st.number_input("Number of Epochs", min_value=1, max_value=200, value=10, step=1)
batch_size = st.number_input("Batch Size", min_value=1, max_value=128, value=32, step=1)
learning_rate = st.number_input("Learning Rate", min_value=0.0001, max_value=0.1, value=0.0001, step=0.0001, format="%.4f")
optimizer_str = st.selectbox("Optimizer", ['adam', 'sgd', 'rmsprop'], index=0)

def process_classification_data(data: str) -> Optional[tuple[List[str], List[int]]]:
    """Processes the classification data string into lists of texts and labels."""
    data_pairs = [line.split(',') for line in data.split('\n') if ',' in line]
    if not data_pairs:
        st.error("No valid data pairs found.  Please ensure each line contains 'text,label'.")
        return None
    texts = []
    labels = []
    for i, pair in enumerate(data_pairs):
        if len(pair) != 2:
            st.error(f"Invalid data format in line {i + 1}: '{','.join(pair)}'.  Expected 'text,label'.")
            return None
        text = pair[0].strip()
        label_str = pair[1].strip()
        try:
            label = int(label_str)
            texts.append(text)
            labels.append(label)
        except ValueError:
            st.error(f"Invalid label value in line {i + 1}: '{label_str}'.  Label must be an integer.")
            return None
    return texts, labels

if st.button("Classify"):
    if not classification_data or not classification_prompt:
        st.warning("Please provide training data and text to classify.")
    else:
        with st.spinner("Classifying..."):
            try:
                processed_data = process_classification_data(classification_data)
                if not processed_data:
                    st.stop()
                train_texts, train_labels = processed_data
                num_classes = len(set(train_labels))

                train_embeddings = generate_embeddings(train_texts, classification_embedding_model)
                if not train_embeddings:
                    st.stop()

                model = create_and_train_model(
                    train_embeddings, train_labels, num_classes, num_epochs, batch_size, learning_rate, optimizer_str
                )

                predict_embedding = generate_embeddings([classification_prompt], classification_embedding_model)
                if not predict_embedding:
                    st.stop()

                prediction = model.predict(np.array([predict_embedding]), verbose=0)
                predicted_class = np.argmax(prediction[0])
                st.subheader("Predicted Class:")
                st.write(predicted_class)
                st.subheader("Prediction Probabilities:")
                st.write(prediction)

            except Exception as e:
                st.error(f"An error occurred: {e}")