Spaces:
Running on Zero
Running on Zero
| # -*- coding: utf-8 -*- | |
| """ | |
| DEVIPUTRA AI // HUGGING FACE SPACES ZERO-GPU INFERENCE ENGINE | |
| Full-stack Gradio + Cyberpunk HUD integration with Fine-Tuned Multilingual BERT (RTX-5050). | |
| """ | |
| # IMPORTANT: ZeroGPU requires importing spaces before torch or CUDA initialization | |
| try: | |
| import spaces | |
| except ImportError: | |
| spaces = None | |
| import os | |
| import sys | |
| import base64 | |
| from fastapi import FastAPI, Request | |
| from fastapi.responses import JSONResponse | |
| import gradio as gr | |
| import torch | |
| # Ensure src modules are discoverable | |
| current_dir = os.path.dirname(os.path.abspath(__file__)) | |
| if current_dir not in sys.path: | |
| sys.path.insert(0, current_dir) | |
| from src.predict import HateSpeechPredictor | |
| print("⚡ [Hugging Face Spaces] Initializing DEVIPUTRA AI Predictor...") | |
| predictor = HateSpeechPredictor(models_dir=os.path.join(current_dir, 'models')) | |
| # Prediction Function for Gradio | |
| def analyze_text(text: str): | |
| if not text or not text.strip(): | |
| return "Please input some text to analyze.", "Standby", {} | |
| result = predictor.predict(text) | |
| prediction = result.get('prediction', 'Normal') | |
| context = result.get('context') | |
| explanation = "" | |
| if context and context.get('reason'): | |
| explanation = f"\n\n🔍 Context Reason: {context.get('reason')}" | |
| status_message = f"Verdict: {prediction.upper()} {explanation}" | |
| if 'scores' in result and result['scores']: | |
| confidences = result['scores'] | |
| elif prediction == 'Hate Speech': | |
| confidences = {'Hate Speech': 0.98, 'Offensive': 0.015, 'Normal': 0.005} | |
| elif prediction == 'Offensive': | |
| confidences = {'Offensive': 0.92, 'Hate Speech': 0.05, 'Normal': 0.03} | |
| else: | |
| confidences = {'Normal': 0.96, 'Offensive': 0.03, 'Hate Speech': 0.01} | |
| return status_message, prediction, confidences | |
| # ZeroGPU decorator | |
| if spaces is not None: | |
| analyze_text = spaces.GPU(analyze_text) | |
| # Load Cyberpunk HUD HTML with embedded graphics | |
| hud_file = os.path.join(current_dir, 'cyberpunk_hud.html') | |
| hud_content = "" | |
| if os.path.exists(hud_file): | |
| with open(hud_file, 'r', encoding='utf-8') as f: | |
| hud_content = f.read() | |
| vagabond_path = os.path.join(current_dir, 'vagabond.jpg') | |
| if os.path.exists(vagabond_path): | |
| with open(vagabond_path, 'rb') as img_f: | |
| b64_img = f"data:image/jpeg;base64,{base64.b64encode(img_f.read()).decode()}" | |
| hud_content = hud_content.replace('src="./vagabond.jpg"', f'src="{b64_img}"') | |
| # Gradio Interface Blocks - Cyberpunk HUD is now the primary hero tab! | |
| with gr.Blocks(title="DEVIPUTRA AI // Cyberpunk HUD & Multilingual BERT") as demo: | |
| gr.Markdown(""" | |
| # ⚡ DEVIPUTRA AI // MULTILINGUAL BERT INFERENCE LAB | |
| ### Fine-Tuned on Hinglish, Hindi, Telugu, and English Hate Speech & Dual-Meaning Slangs | |
| """) | |
| if hud_content: | |
| with gr.Tab("🕹️ Cyberpunk HUD Deck (Interactive Neural HUD)"): | |
| gr.HTML(f""" | |
| <div style="width: 100%; height: 980px; overflow: auto; border: 1px solid #00f0ff; border-radius: 8px;"> | |
| <iframe srcdoc="{hud_content.replace('"', '"')}" style="width: 100%; height: 100%; border: none;"></iframe> | |
| </div> | |
| """) | |
| with gr.Tab("🧪 Real-Time BERT Model Scanner"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| input_text = gr.Textbox( | |
| label="Input Text (Hindi, Hinglish, English, Telugu)", | |
| placeholder="e.g., Mandir mein ghanta baj raha hai / I will hurt you...", | |
| lines=3 | |
| ) | |
| analyze_btn = gr.Button("⚡ Run Deep Neural Scan", variant="primary") | |
| gr.Examples( | |
| examples=[ | |
| ["Mandir mein aarti ke waqt bada ghanta baj raha hai."], | |
| ["Tujhe ghanta farak nahi padta yahan se nikal."], | |
| ["Mera kutta bahut cute aur loyal pet hai."], | |
| ["Tu kutta hai chup baith ekdum."], | |
| ["I will murder you tonight and destroy everything."], | |
| ["Bhai tu bohot accha insaan hai yaar."] | |
| ], | |
| inputs=input_text | |
| ) | |
| with gr.Column(scale=1): | |
| output_verdict = gr.Textbox(label="Decision & Context Analysis", interactive=False) | |
| output_label = gr.Label(label="Confidence Spectrum", num_top_classes=3) | |
| hidden_pred = gr.Textbox(visible=False) | |
| analyze_btn.click( | |
| fn=analyze_text, | |
| inputs=input_text, | |
| outputs=[output_verdict, hidden_pred, output_label] | |
| ) | |
| # FastAPI Backend server providing the /predict endpoint for the Cyberpunk HUD | |
| server = FastAPI() | |
| async def api_predict(request: Request): | |
| data = await request.json() | |
| text = data.get("text", "") | |
| res = predictor.predict(text) | |
| pred = res.get('prediction', 'Normal') | |
| ctx = res.get('context') or {} | |
| reason = ctx.get('reason', f"Contextual reasoning: {pred}") | |
| scores = res.get('scores', {}) | |
| if not scores: | |
| if pred == 'Hate Speech': | |
| scores = {'Hate Speech': 0.98, 'Offensive': 0.015, 'Normal': 0.005} | |
| elif pred == 'Offensive': | |
| scores = {'Offensive': 0.92, 'Hate Speech': 0.05, 'Normal': 0.03} | |
| else: | |
| scores = {'Normal': 0.96, 'Offensive': 0.03, 'Hate Speech': 0.01} | |
| conf = scores.get(pred, 0.95) | |
| return { | |
| "prediction": pred, | |
| "confidence": conf, | |
| "model_used": "Multilingual BERT (HingBERT)", | |
| "reason": reason, | |
| "scores": scores | |
| } | |
| # Mount Gradio into FastAPI | |
| app = gr.mount_gradio_app(server, demo, path="/") | |
| if __name__ == "__main__": | |
| demo.launch() | |