app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
from tensorflow.keras.preprocessing.sequence import pad_sequences
import re

# Load models and tokenizers
def load_models():
    # Load classifier model and tokenizer
    classifier_model = tf.keras.models.load_model('classifier_model.keras')
    with open('classifier_tokenizer.pkl', 'rb') as handle:
        classifier_tokenizer = pickle.load(handle)
    
    # Load text generator model and tokenizer
    textgen_model = tf.keras.models.load_model('textgen_model.keras')
    with open('textgen_tokenizer.pkl', 'rb') as handle:
        textgen_tokenizer = pickle.load(handle)
    
    return classifier_model, classifier_tokenizer, textgen_model, textgen_tokenizer

# Text cleaning function
def clean_text(text):
    text = re.sub(r'[^\w\s.,!?]', '', text)
    text = re.sub(r'\b\d+\b', '', text)
    text = text.replace('co2', 'carbon dioxide')
    text = text.lower()
    text = ' '.join(text.split())
    return text

# Classification function
def classify_text(text, model, tokenizer):
    cleaned_text = clean_text(text)
    sequence = tokenizer.texts_to_sequences([cleaned_text])
    padded = pad_sequences(sequence, maxlen=255, padding='pre')
    prediction = model.predict(padded)[0]
    
    # Get the highest probability class
    class_idx = np.argmax(prediction)
    classes = ['Science', 'Maths', 'History']
    confidence = prediction[class_idx] * 100
    
    return classes[class_idx], confidence

# Text generation function
def generate_text(prompt, model, tokenizer, max_length=50, temperature=0.7):
    cleaned_prompt = clean_text(prompt)
    input_text = cleaned_prompt
    
    for _ in range(max_length):
        token_list = tokenizer.texts_to_sequences([input_text])[0]
        token_list = pad_sequences([token_list], maxlen=255, padding='pre')
        
        predicted = model.predict(token_list, verbose=0)[0]
        
        # Apply temperature
        predicted = np.log(predicted) / temperature
        exp_preds = np.exp(predicted)
        predicted = exp_preds / np.sum(exp_preds)
        
        # Sample from the distribution
        predicted_index = np.random.choice(len(predicted), p=predicted)
        
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted_index:
                output_word = word
                break
        
        if output_word == "":
            break
            
        input_text += " " + output_word
    
    return input_text

# Load models
classifier_model, classifier_tokenizer, textgen_model, textgen_tokenizer = load_models()

# Create Gradio interface
def classify_interface(text):
    subject, confidence = classify_text(text, classifier_model, classifier_tokenizer)
    return f"Subject: {subject} (Confidence: {confidence:.2f}%)"

def generate_interface(prompt, length=50, temp=0.7):
    return generate_text(prompt, textgen_model, textgen_tokenizer, max_length=int(length), temperature=float(temp))

# Create tabs for different functionalities
with gr.Blocks(title="Science Text Analyzer") as demo:
    gr.Markdown("# Science Text Analyzer")
    
    with gr.Tab("Classify Text"):
        gr.Markdown("## Classify scientific text into Physics, Chemistry, or Biology")
        with gr.Row():
            with gr.Column():
                text_input = gr.Textbox(label="Enter scientific text", lines=5)
                classify_button = gr.Button("Classify")
            with gr.Column():
                output = gr.Textbox(label="Classification Result")
        classify_button.click(fn=classify_interface, inputs=text_input, outputs=output)
    
    with gr.Tab("Generate Text"):
        gr.Markdown("## Generate scientific text based on a prompt")
        with gr.Row():
            with gr.Column():
                prompt_input = gr.Textbox(label="Enter a prompt", lines=3)
                length_slider = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Maximum Length")
                temp_slider = gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature (Creativity)")
                generate_button = gr.Button("Generate")
            with gr.Column():
                generated_output = gr.Textbox(label="Generated Text", lines=8)
        generate_button.click(fn=generate_interface, inputs=[prompt_input, length_slider, temp_slider], outputs=generated_output)

    gr.Markdown("### About")
    gr.Markdown("This app uses deep learning models trained on scientific texts to classify and generate content related to Physics, Chemistry, and Biology.")

# Launch the app
demo.launch()