Update app.py

app.py

@@ -1,44 +1,93 @@
 import gradio as gr
-from transformers import pipeline
-import requests
 import spacy
+import requests
+from transformers import pipeline
+from supabase import create_client
+from statistics import mean
 
-# Load free models
-nlp = spacy.load("en_core_web_sm")
-vocab_model = pipeline("text-classification", model="roberta-base")  # Free alternative
+# Configuration
+SUPABASE_URL = "your-supabase-url"
+SUPABASE_KEY = "your-supabase-key"
+HF_TOKEN = "your-hf-token"
+nlp = spacy.load("en_core_web_lg")
 
-def evaluate_ielts(essay):
-    # Vocabulary Assessment
-    try:
-        vocab_score = min(9, vocab_model(essay)[0]['score'] * 9 + 1)  # Adjusted scaling
-    except:
-        vocab_score = 6.0
-    
-    # Grammar Check (Free API)
-    grammar_response = requests.post(
-        "https://api.languagetool.org/v2/check",
-        data={"text": essay, "language": "en-GB"}
-    )
-    grammar_errors = len(grammar_response.json().get('matches', []))
-    grammar_score = max(1, 9 - (grammar_errors // 1.5))  # 1.5 errors = 1 point deduction
+# 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
-    doc = nlp(essay)
-    transition_words = ["however", "moreover", "therefore", "furthermore"]
+    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, transitions + 4)  # 4-9 scale
+    coherence_score = min(9, 4 + (transitions * 0.5) + (len(doc.ents) * 0.2))
     
-    # Overall Band Calculation
-    overall = (vocab_score * 0.4) + (grammar_score * 0.3) + (coherence_score * 0.3)
+    # 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 {
-        "Vocabulary Band": round(vocab_score, 1),
-        "Grammar Band": grammar_score,
-        "Coherence Band": coherence_score,
-        "Overall Band": round(overall, 1)
+        "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)
     }
 
-# Create Interface
-gr.Interface(
-    fn=evaluate_ielts,
-    inputs=gr.Textbox(label="Paste Your Essay",
+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()