Update app.py

app.py

@@ -10,7 +10,7 @@ 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  # Import Adam optimizer
+from tensorflow.keras.optimizers import Adam
 
 # Load environment variables
 load_dotenv()
@@ -21,7 +21,7 @@ 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.  "
+        "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()
@@ -40,19 +40,11 @@ with st.sidebar:
     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.
+        - **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.
         """
     )
 
@@ -61,7 +53,6 @@ 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"):
@@ -70,276 +61,4 @@ def display_response(response: Any) -> None:
     else:
         st.error("Failed to generate a response.")
 
-
-
-def generate_embeddings(texts: List[str], model_name: str) -> 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:
-        model = genai.GenerativeModel(model_name)
-        response = model.generate_embeddings(texts=texts)  # Use generate_embeddings
-        return [embedding.values for embedding in response.embeddings] # changed
-    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.
-
-    Args:
-        prompt: The prompt string.
-        model_name: The name of the language model.
-        generation_config: The generation configuration.
-        max_retries: Maximum number of retries.
-        delay: Delay in seconds between retries.
-
-    Returns:
-        The generated response or None on error.
-    """
-    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  # Return None to signal a non-retryable error
-            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 after max retries
-    return None  #Should never reach here
-
-
-
-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.
-
-    Args:
-        embeddings: List of input embeddings.
-        labels: List of integer labels.
-        num_classes: Number of classes.
-        epochs: Number of training epochs.
-        batch_size: Batch size for training.
-        learning_rate: Learning rate for the optimizer.
-        optimizer_str: Name of the optimizer ('adam', 'sgd', 'rmsprop')
-
-    Returns:
-        Trained Keras model.
-    """
-    model = Sequential([
-        Input(shape=(len(embeddings[0]),)),
-        Dense(64, activation='relu'),  # Increased hidden layer size
-        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) #default
-
-    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:", ["gemini-pro"], 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]
-                #truncate context
-                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:", ["gemini-pro"], 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:
-                # 1. Generate embeddings
-                embeddings = generate_embeddings([text1, text2], similarity_embedding_model)
-                if not embeddings:
-                    st.stop()
-
-                # 2. Calculate cosine similarity
-                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:", ["gemini-pro"], 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.
-
-    Args:
-        data:  The input string.
-
-    Returns:
-       A tuple of (texts, labels) or None on error
-    """
-    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:
-                # 1. Process the training data
-                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))
-
-                # 2. Generate embeddings for training data
-                train_embeddings = generate_embeddings(train_texts, classification_embedding_model)
-                if not train_embeddings:
-                    st.stop()
-
-                # 3. Create and train the model
-                model = create_and_train_model(
-                    train_embeddings, train_labels, num_classes, num_epochs, batch_size, learning_rate, optimizer_str
-                )
-
-                # 4. Generate embedding for the text to classify
-                predict_embedding = generate_embeddings([classification_prompt], classification_embedding_model)
-                if not predict_embedding:
-                    st.stop()
-
-                # 5. Make the prediction
-                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}")
-
+def generate_embeddings(texts: List[str], model_name