Create app.py

app.py

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+import torch
+import gradio as gr
+import speech_recognition as sr
+import time
+import difflib
+import random
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from happytransformer import HappyTextToText, TTSettings
+
+# Constants
+MODEL_NAME = "prithivida/grammar_error_correcter_v1"
+CSS = """
+.gradio-container { max-width: 1400px !important; }
+.header { text-align: center; padding: 2rem; background: linear-gradient(135deg, #3b82f6, #6366f1); color: white; border-radius: 15px; }
+#container { height: 500px; width: 100%; background: #1a1a1a; border-radius: 10px; }
+.diff-ins { color: #10b981; background: #d1fae5; padding: 2px 4px; border-radius: 4px; }
+.diff-del { color: #ef4444; background: #fee2e2; padding: 2px 4px; border-radius: 4px; }
+"""
+
+# Three.js Template
+THREEJS_TEMPLATE = """
+<div id="container"></div>
+<script async src="https://unpkg.com/[email protected]/dist/es-module-shims.js"></script>
+<script type="importmap">
+{
+  "imports": {
+    "three": "https://unpkg.com/[email protected]/build/three.module.js",
+    "three/addons/": "https://unpkg.com/[email protected]/examples/jsm/"
+  }
+}
+</script>
+
+<script type="module">
+import * as THREE from 'three';
+import { OrbitControls } from 'three/addons/controls/OrbitControls.js';
+
+class GrammarVisualizer {
+  constructor() {
+    this.initScene();
+    this.addLights();
+    this.createGrammarSphere();
+    this.setupControls();
+    this.animate();
+  }
+
+  initScene() {
+    this.scene = new THREE.Scene();
+    this.camera = new THREE.PerspectiveCamera(75, 500/400, 0.1, 1000);
+    this.renderer = new THREE.WebGLRenderer({ antialias: true, alpha: true });
+    document.getElementById('container').appendChild(this.renderer.domElement);
+    this.renderer.setSize(500, 400);
+    this.camera.position.z = 5;
+  }
+
+  addLights() {
+    const ambient = new THREE.AmbientLight(0x404040);
+    const directional = new THREE.DirectionalLight(0xffffff, 1);
+    directional.position.set(5, 5, 5);
+    this.scene.add(ambient, directional);
+  }
+
+  createGrammarSphere() {
+    const geometry = new THREE.SphereGeometry(2, 32, 32);
+    this.material = new THREE.MeshPhongMaterial({ 
+      color: 0x3b82f6,
+      transparent: true,
+      opacity: 0.9
+    });
+    this.sphere = new THREE.Mesh(geometry, this.material);
+    this.scene.add(this.sphere);
+  }
+
+  setupControls() {
+    this.controls = new OrbitControls(this.camera, this.renderer.domElement);
+    this.controls.enableDamping = true;
+    this.controls.dampingFactor = 0.05;
+  }
+
+  animate() {
+    requestAnimationFrame(() => this.animate());
+    this.controls.update();
+    this.renderer.render(this.scene, this.camera);
+  }
+
+  updateVisuals(score) {
+    const hue = score / 100;
+    this.material.color.setHSL(hue, 0.8, 0.5);
+    this.sphere.rotation.x = (score / 50) * Math.PI;
+    this.sphere.rotation.y = (score / 75) * Math.PI;
+  }
+}
+
+let visualizer;
+window.addEventListener('DOMContentLoaded', () => {
+  visualizer = new GrammarVisualizer();
+});
+
+window.updateGrammarVisuals = (score) => {
+  if(visualizer) visualizer.updateVisuals(score);
+};
+</script>
+"""
+
+# Load models
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+happy_tt = HappyTextToText("T5", MODEL_NAME)
+
+def create_diff_html(original, corrected):
+    d = difflib.Differ()
+    diff = d.compare(original.split(), corrected.split())    
+    return " ".join([
+        f'<span class="diff-ins">{p[2:]}</span> ' if p.startswith('+ ') else
+        f'<span class="diff-del">{p[2:]}</span> ' if p.startswith('- ') else
+        f'{p[2:]} ' for p in diff
+    ])
+
+def analyze_grammar(text):
+    inputs = tokenizer.encode("gec: " + text, return_tensors="pt", max_length=256, truncation=True)
+    with torch.no_grad():
+        outputs = model.generate(inputs, max_length=256, num_beams=5)
+    correction = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    args = TTSettings(num_beams=5, min_length=1)
+    happy_correction = happy_tt.generate_text("gec: " + text, args=args).text
+    
+    final_correction = happy_correction if len(happy_correction) > len(correction) else correction
+    changes = sum(1 for a, b in zip(text.split(), final_correction.split()) if a != b)
+    score = max(0, 100 - (changes * 2))
+    
+    return {
+        "original": text,
+        "corrected": final_correction,
+        "score": score,
+        "diff_html": create_diff_html(text, final_correction)
+    }
+
+def process_input(audio_path, text):
+    if audio_path:
+        recognizer = sr.Recognizer()
+        with sr.AudioFile(audio_path) as source:
+            audio = recognizer.record(source)
+        try:
+            text = recognizer.recognize_google(audio)
+        except sr.UnknownValueError:
+            return "Could not understand audio", 0, "", ""
+    
+    if not text.strip():
+        return "No input", 0, "", ""
+    
+    results = analyze_grammar(text)
+    return [
+        results['original'],
+        results['score'],
+        results['diff_html'],
+        f"<script>window.updateGrammarVisuals({results['score']})</script>"
+    ]
+
+with gr.Blocks(css=CSS) as app:
+    gr.Markdown("""
+    <div class="header">
+        <h1>🌍 3D Grammar Analyzer Pro</h1>
+        <p>Interactive AI-Powered Writing Assistant with 3D Visualization</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### Input Section")
+            audio_input = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Voice Input")
+            text_input = gr.Textbox(lines=5, placeholder="📝 Type your text here...", label="Text Input")
+            submit_btn = gr.Button("🚀 Analyze Text", variant="primary")
+            
+        with gr.Column(scale=2):
+            gr.Markdown("### 3D Visualization")
+            threejs = gr.HTML(THREEJS_TEMPLATE)
+            
+            with gr.Row():
+                grammar_score = gr.Number(label="📊 Grammar Score", precision=0)
+                score_gauge = gr.BarPlot(x=["Score"], y=[0], color="#3b82f6", height=150)
+                
+            diff_output = gr.HTML(label="📝 Text Corrections")
+            hidden_trigger = gr.HTML(visible=False)
+
+    gr.Markdown("### Example Sentences")
+    gr.Examples(
+        examples=[
+            ["I is going to the park yesterday."],
+            ["Their happy about there test results."],
+            ["She dont like apples, but she like bananas."]
+        ],
+        inputs=[text_input],
+        outputs=[text_input, grammar_score, diff_output, hidden_trigger],
+        fn=process_input,
+        cache_examples=True
+    )
+    
+    submit_btn.click(
+        fn=process_input,
+        inputs=[audio_input, text_input],
+        outputs=[text_input, grammar_score, diff_output, hidden_trigger]
+    )
+    
+    text_input.change(
+        lambda x: analyze_grammar(x)["score"] if x else 0,
+        inputs=text_input,
+        outputs=grammar_score
+    )
+
+if __name__ == "__main__":
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        favicon_path="https://raw.githubusercontent.com/gradio-app/gradio/main/guides/assets/logo.png"
+    )