Spaces:
Sleeping
Sleeping
| import torch | |
| import gradio as gr | |
| import speech_recognition as sr | |
| import difflib | |
| import json | |
| import os | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| MODEL_NAME = "prithivida/grammar_error_correcter_v1" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) | |
| D3_TEMPLATE = """ | |
| <div id="d3-dashboard"> | |
| <svg id="main-chart"></svg> | |
| <div id="text-vis" class="text-visualization"></div> | |
| </div> | |
| <script src="https://d3js.org/d3.v7.min.js"></script> | |
| <script> | |
| const container = d3.select("#d3-dashboard"); | |
| const width = 800; | |
| const height = 500; | |
| const margin = {top: 20, right: 30, bottom: 30, left: 40}; | |
| const svg = container.select("#main-chart") | |
| .attr("width", width) | |
| .attr("height", height) | |
| .style("background", "#f8fafc") | |
| .style("border-radius", "12px"); | |
| const textVis = container.select("#text-vis") | |
| .style("margin-top", "20px") | |
| .style("min-height", "100px") | |
| .style("padding", "20px") | |
| .style("background", "white") | |
| .style("border-radius", "12px") | |
| .style("box-shadow", "0 2px 4px rgba(0,0,0,0.1)"); | |
| function updateDashboard(data) { | |
| // Clear previous elements | |
| svg.selectAll("*").remove(); | |
| textVis.html(""); | |
| // Score Arc | |
| const arc = d3.arc() | |
| .innerRadius(80) | |
| .outerRadius(120) | |
| .startAngle(0) | |
| .endAngle((Math.PI * 2 * data.score) / 100); | |
| svg.append("path") | |
| .attr("transform", `translate(${width/2},${height/3})`) | |
| .attr("d", arc) | |
| .attr("fill", "#3b82f6") | |
| .transition() | |
| .duration(1000) | |
| .attrTween("d", function(d) { | |
| const interpolate = d3.interpolate(0, data.score/100); | |
| return function(t) { | |
| arc.endAngle(Math.PI * 2 * interpolate(t)); | |
| return arc(); | |
| }; | |
| }); | |
| // Error Distribution | |
| const errorTypes = data.errors; | |
| const x = d3.scaleBand() | |
| .domain(errorTypes.map(d => d.type)) | |
| .range([margin.left, width - margin.right]) | |
| .padding(0.2); | |
| const y = d3.scaleLinear() | |
| .domain([0, d3.max(errorTypes, d => d.count)]) | |
| .range([height/2 - margin.bottom, margin.top]); | |
| svg.selectAll(".error-bar") | |
| .data(errorTypes) | |
| .enter().append("rect") | |
| .attr("class", "error-bar") | |
| .attr("x", d => x(d.type)) | |
| .attr("y", d => y(d.count)) | |
| .attr("width", x.bandwidth()) | |
| .attr("height", d => height/2 - margin.bottom - y(d.count)) | |
| .attr("fill", "#ef4444") | |
| .attr("rx", 4) | |
| .on("mouseover", function(event, d) { | |
| d3.select(this).attr("fill", "#dc2626"); | |
| }) | |
| .on("mouseout", function(event, d) { | |
| d3.select(this).attr("fill", "#ef4444"); | |
| }); | |
| // Interactive Text Visualization | |
| const textBox = textVis.selectAll(".word") | |
| .data(data.corrections) | |
| .enter().append("div") | |
| .attr("class", "word") | |
| .style("display", "inline-block") | |
| .style("margin", "2px") | |
| .style("padding", "4px 8px") | |
| .style("border-radius", "4px") | |
| .style("background", d => d.correct ? "#d1fae5" : "#fee2e2") | |
| .style("color", d => d.correct ? "#065f46" : "#991b1b") | |
| .style("cursor", "pointer") | |
| .on("mouseover", function(event, d) { | |
| d3.select(this).style("filter", "brightness(90%)"); | |
| }) | |
| .on("mouseout", function(event, d) { | |
| d3.select(this).style("filter", "brightness(100%)"); | |
| }) | |
| .html(d => d.original); | |
| textBox.append("div") | |
| .attr("class", "tooltip") | |
| .style("position", "absolute") | |
| .style("background", "white") | |
| .style("padding", "8px") | |
| .style("border-radius", "6px") | |
| .style("box-shadow", "0 2px 8px rgba(0,0,0,0.1)") | |
| .html(d => ` | |
| <strong>${d.type}</strong><br> | |
| ${d.message}<br> | |
| <em>Suggested:</em> ${d.suggestion} | |
| `); | |
| } | |
| </script> | |
| """ | |
| def analyze_errors(original, corrected): | |
| diff = difflib.SequenceMatcher(None, original.split(), corrected.split()) | |
| errors = [] | |
| for tag, i1, i2, j1, j2 in diff.get_opcodes(): | |
| if tag != 'equal': | |
| error = { | |
| 'type': 'Grammar' if tag == 'replace' else 'Structure', | |
| 'original': ' '.join(original.split()[i1:i2]), | |
| 'suggestion': ' '.join(corrected.split()[j1:j2]), | |
| 'message': 'Improvement suggested' if tag == 'replace' else 'Structural change' | |
| } | |
| errors.append(error) | |
| return errors | |
| def process_input(audio_path, text): | |
| try: | |
| # Handle audio input | |
| if audio_path and os.path.exists(audio_path): | |
| recognizer = sr.Recognizer() | |
| with sr.AudioFile(audio_path) as source: | |
| audio = recognizer.record(source) | |
| text = recognizer.recognize_google(audio) | |
| if not text.strip(): | |
| return {"error": "No input provided"}, "" | |
| # Grammar correction | |
| 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) | |
| corrected = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Analysis | |
| errors = analyze_errors(text, corrected) | |
| score = max(0, 100 - len(errors)*5) | |
| # D3 data format | |
| error_counts = { | |
| "Grammar": sum(1 for e in errors if e['type'] == 'Grammar'), | |
| "Structure": sum(1 for e in errors if e['type'] == 'Structure'), | |
| "Spelling": 0 # Add spelling detection logic if available | |
| } | |
| d3_data = { | |
| "score": score, | |
| "errors": [{"type": k, "count": v} for k, v in error_counts.items()], | |
| "corrections": errors[:10] # Show first 10 corrections | |
| } | |
| return d3_data, corrected | |
| except Exception as e: | |
| return {"error": str(e)}, "" | |
| with gr.Blocks(css=""" | |
| .gradio-container { max-width: 1400px!important; padding: 20px!important; } | |
| #d3-dashboard { background: white; padding: 20px; border-radius: 12px; } | |
| .text-visualization { font-family: monospace; font-size: 16px; } | |
| """) as app: | |
| gr.Markdown("# ✨ AI Writing Analytics Suite") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Input Section") | |
| audio = gr.Audio(sources=["microphone"], type="filepath", | |
| label="🎤 Voice Input") | |
| text = gr.Textbox(lines=5, placeholder="📝 Enter text here...", | |
| label="Text Input") | |
| btn = gr.Button("Analyze", variant="primary") | |
| with gr.Column(scale=2): | |
| gr.Markdown("### Writing Analytics") | |
| visualization = gr.HTML(D3_TEMPLATE) | |
| report = gr.JSON(label="Detailed Report") | |
| gr.Markdown("### Corrected Text") | |
| corrected = gr.Textbox(label="Result", interactive=False) | |
| # Examples handling | |
| gr.Markdown("### Example Inputs") | |
| 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], | |
| outputs=[report, corrected], | |
| fn=lambda t: process_input(None, t), | |
| cache_examples=False | |
| ) | |
| btn.click( | |
| fn=process_input, | |
| inputs=[audio, text], | |
| outputs=[report, corrected] | |
| ) | |
| # JavaScript update | |
| visualization.change( | |
| fn=lambda data: f"<script>updateDashboard({json.dumps(data)})</script>", | |
| inputs=[report], | |
| outputs=[visualization] | |
| ) | |
| if __name__ == "__main__": | |
| app.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False | |
| ) |