app.py

import gradio as gr
import spacy
import requests
from transformers import pipeline
from supabase import create_client
from statistics import mean

# Configuration
SUPABASE_URL = "your-supabase-url"
SUPABASE_KEY = "your-supabase-key"
HF_TOKEN = "your-hf-token"
nlp = spacy.load("en_core_web_lg")

# Models
MODELS = {
    "vocab": pipeline("text-classification", 
                    model="domenicrosati/IELTS-writing-task-2-rater"),
    "grammar": pipeline("text2text-generation",
                       model="vennify/t5-base-grammar-correction"),
    "coherence": pipeline("feature-extraction",
                        model="sentence-transformers/all-mpnet-base-v2")
}

# Supabase Client
supabase = create_client(SUPABASE_URL, SUPABASE_KEY)

def analyze_text(essay):
    """Comprehensive text analysis using multiple NLP techniques"""
    doc = nlp(essay)
    
    # Vocabulary Analysis
    vocab_score = MODELS["vocab"](essay)[0]["score"] * 9
    
    # Grammar Analysis
    grammar_errors = len(MODELS["grammar"](f"grammar: {essay}")[0]["generated_text"].split("<error>")) - 1
    grammar_score = max(1, 9 - (grammar_errors // 1.2))
    
    # Coherence Analysis
    embeddings = MODELS["coherence"](essay)
    transition_words = ["however", "moreover", "furthermore", "consequently"]
    transitions = sum(1 for token in doc if token.text.lower() in transition_words)
    coherence_score = min(9, 4 + (transitions * 0.5) + (len(doc.ents) * 0.2))
    
    # Task Achievement (Using Mistral-7B)
    task_response = requests.post(
        "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3",
        headers={"Authorization": f"Bearer {HF_TOKEN}"},
        json={"inputs": f"Rate IELTS task achievement (1-9): {essay}"}
    )
    task_score = int(task_response.json()[0]["generated_text"][:1]) if task_response.ok else 6
    
    return {
        "vocab": round(vocab_score, 1),
        "grammar": grammar_score,
        "coherence": round(coherence_score, 1),
        "task": task_score,
        "overall": round(mean([vocab_score, grammar_score, coherence_score, task_score]), 1)
    }

def evaluate_ielts(essay):
    analysis = analyze_text(essay)
    
    # Store results
    supabase.table("ielts_results").insert({
        "essay": essay,
        "scores": analysis,
        "timestamp": "now()"
    }).execute()
    
    return analysis

# Professional Gradio Interface
with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 800px!important}") as demo:
    gr.Markdown("## Professional IELTS Evaluation System")
    
    with gr.Row():
        essay_input = gr.Textbox(label="Enter Essay", lines=10, 
                               placeholder="Discuss both views and give your opinion...")
        output_json = gr.JSON(label="Evaluation Results")
    
    with gr.Row():
        gr.Examples(
            examples=[
                ["While some argue technology improves learning, others believe it distracts students. This essay examines both perspectives before concluding that balanced use offers optimal benefits."],
                ["Environmental protection requires global cooperation. Governments must implement stricter regulations while promoting sustainable energy alternatives to combat climate change effectively."]
            ],
            inputs=essay_input
        )
    
    submit_btn = gr.Button("Evaluate", variant="primary")
    submit_btn.click(fn=evaluate_ielts, inputs=essay_input, outputs=output_json)

demo.launch()