| |
| """ |
| 🎯 EKALAVYA - The Ultimate AI Teaching Assistant |
| 🌟 Multi-Modal • Multi-Lingual • Memory-Powered |
| 🛡️ Safe • 🎓 Educational • 💝 Friendly |
| """ |
|
|
| from fastapi import FastAPI, HTTPException, UploadFile, File, Form |
| from fastapi.responses import HTMLResponse |
| from pydantic import BaseModel |
| from typing import Optional, List, Dict |
| import json |
| import os |
|
|
| |
| from model.teaching import TeachingMode |
| from model.safety import SafetyRules |
| from model.memory import MemorySystem |
|
|
| |
| app = FastAPI( |
| title="🎯 EKALAVYA API", |
| description="🌟 The Ultimate AI Teaching Assistant - Multi-Modal, Multi-Lingual, Memory-Powered", |
| version="3.0.0", |
| docs_url="/docs", |
| redoc_url="/redoc" |
| ) |
|
|
| |
| safety = SafetyRules() |
|
|
| |
| teaching_mode = TeachingMode(style="friend") |
|
|
| |
| DATA_DIR = "data" |
| os.makedirs(DATA_DIR, exist_ok=True) |
|
|
|
|
| |
| class TeachingRequest(BaseModel): |
| """📚 Teaching request model""" |
| input_text: str |
| user_id: str = "default_user" |
| conversation_style: str = "friend" |
| language: str = "english" |
|
|
|
|
| class SafetyCheckRequest(BaseModel): |
| """🛡️ Safety check request model""" |
| content: str |
| check_type: str = "all" |
|
|
|
|
| class ProgressRequest(BaseModel): |
| """📊 Progress request model""" |
| user_id: str |
|
|
|
|
| class StyleRequest(BaseModel): |
| """💝 Conversation style request model""" |
| style: str |
| user_id: str = "default_user" |
|
|
|
|
| |
| @app.get("/", response_class=HTMLResponse) |
| async def root(): |
| """🎯 Welcome page with emojis""" |
| return """ |
| <!DOCTYPE html> |
| <html> |
| <head> |
| <title>🎯 EKALAVYA - AI Teaching Assistant</title> |
| <style> |
| body { |
| font-family: Arial, sans-serif; |
| max-width: 800px; |
| margin: 50px auto; |
| padding: 20px; |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); |
| color: white; |
| } |
| .card { |
| background: rgba(255,255,255,0.95); |
| color: #333; |
| padding: 30px; |
| border-radius: 20px; |
| box-shadow: 0 10px 40px rgba(0,0,0,0.3); |
| margin: 20px 0; |
| } |
| h1 { font-size: 3em; text-align: center; } |
| .emoji { font-size: 1.5em; } |
| .feature { |
| background: #f0f0f0; |
| padding: 15px; |
| margin: 10px 0; |
| border-radius: 10px; |
| border-left: 5px solid #667eea; |
| } |
| .stats { |
| display: flex; |
| justify-content: space-around; |
| margin: 30px 0; |
| } |
| .stat { |
| text-align: center; |
| padding: 20px; |
| background: rgba(255,255,255,0.1); |
| border-radius: 15px; |
| flex: 1; |
| margin: 0 10px; |
| } |
| .stat-number { font-size: 2.5em; font-weight: bold; } |
| a { color: #667eea; text-decoration: none; font-weight: bold; } |
| a:hover { text-decoration: underline; } |
| </style> |
| </head> |
| <body> |
| <div class="card"> |
| <h1>🎯 EKALAVYA</h1> |
| <p style="text-align: center; font-size: 1.3em;"> |
| 🌟 The Ultimate AI Teaching Assistant 🌟 |
| </p> |
| |
| <div class="stats"> |
| <div class="stat"> |
| <div class="stat-number">🌍 23</div> |
| <div>Indian Languages</div> |
| </div> |
| <div class="stat"> |
| <div class="stat-number">🧠 1M</div> |
| <div>Token Context</div> |
| </div> |
| <div class="stat"> |
| <div class="stat-number">🛡️ 100%</div> |
| <div>Safe & Private</div> |
| </div> |
| </div> |
| |
| <h2>✨ Features</h2> |
| |
| <div class="feature"> |
| <span class="emoji">🎓</span> <strong>Teaching Mode</strong> |
| <p>Learn English with real-time mistake detection and correction</p> |
| </div> |
| |
| <div class="feature"> |
| <span class="emoji">🧠</span> <strong>Memory System</strong> |
| <p>Remembers your mistakes and tracks your learning progress</p> |
| </div> |
| |
| <div class="feature"> |
| <span class="emoji">💝</span> <strong>Conversation Styles</strong> |
| <p>Choose: Friend 👫, Teacher 👨🏫, Lover 💕, or Mentor 🎓</p> |
| </div> |
| |
| <div class="feature"> |
| <span class="emoji">🛡️</span> <strong>Safety First</strong> |
| <p>Scam detection, hacking prevention, privacy protection</p> |
| </div> |
| |
| <div class="feature"> |
| <span class="emoji">🌐</span> <strong>Multi-Modal</strong> |
| <p>Supports text, images, video, and audio</p> |
| </div> |
| |
| <div class="feature"> |
| <span class="emoji">🔒</span> <strong>100% Private</strong> |
| <p>All data stays on your device, no tracking</p> |
| </div> |
| |
| <h2>📚 API Endpoints</h2> |
| |
| <div class="feature"> |
| <strong>POST /teach</strong> - Start learning session |
| </div> |
| |
| <div class="feature"> |
| <strong>POST /safety/check</strong> - Check content safety |
| </div> |
| |
| <div class="feature"> |
| <strong>GET /safety/tips</strong> - Get safety tips |
| </div> |
| |
| <div class="feature"> |
| <strong>POST /progress</strong> - View learning progress |
| </div> |
| |
| <div class="feature"> |
| <strong>POST /style</strong> - Change conversation style |
| </div> |
| |
| <h2>🔗 Quick Links</h2> |
| <p> |
| 📖 <a href="/docs">Interactive API Docs</a> | |
| 📊 <a href="/redoc">Alternative Docs</a> | |
| 🎯 <a href="https://huggingface.co/hackerbhai/vinaymodel">HuggingFace Model</a> |
| </p> |
| |
| <p style="text-align: center; margin-top: 30px; font-size: 1.2em;"> |
| 🎯 Built with ❤️ for learners everywhere 🌍 |
| </p> |
| </div> |
| </body> |
| </html> |
| """ |
|
|
|
|
| |
| @app.post("/teach") |
| async def teach(request: TeachingRequest): |
| """🎓 Start teaching session with mistake detection""" |
| try: |
| |
| safety_check = safety.check_content(request.input_text) |
| if not safety_check['is_safe']: |
| return { |
| "status": "🛡️ safety_warning", |
| "message": safety_check['warnings'][0], |
| "suggestions": safety_check['suggestions'] |
| } |
| |
| |
| result = teaching_mode.process_teaching_request( |
| user_input=request.input_text, |
| user_id=request.user_id, |
| conversation_style=request.conversation_style, |
| language=request.language |
| ) |
| |
| return { |
| "status": "✅ success", |
| "response": result['response'], |
| "mistakes_found": result['mistakes'], |
| "corrections": result['corrections'], |
| "explanation": result['explanation'], |
| "encouragement": result['encouragement'], |
| "next_steps": result['next_steps'], |
| "emoji": "🎉" if result['mistakes'] else "✨" |
| } |
| |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
|
|
| |
| @app.post("/safety/check") |
| async def check_safety(request: SafetyCheckRequest): |
| """🛡️ Check if content is safe""" |
| result = safety.check_content(request.content) |
| |
| return { |
| "is_safe": result['is_safe'], |
| "violations": result['violations'], |
| "warnings": result['warnings'], |
| "suggestions": result['suggestions'], |
| "emoji": "✅" if result['is_safe'] else "⚠️", |
| "message": "✅ Content is safe!" if result['is_safe'] else "⚠️ Safety issues detected" |
| } |
|
|
|
|
| |
| @app.get("/safety/tips") |
| async def get_safety_tips(): |
| """💡 Get safety tips with emojis""" |
| tips = safety.get_safety_tips() |
| |
| emoji_tips = [ |
| f"🔒 {tips['privacy_protection'][0]}", |
| f"🛡️ {tips['privacy_protection'][1]}", |
| f"🚫 {tips['prohibited_actions'][0]}", |
| f"⚠️ {tips['prohibited_actions'][1]}", |
| f"💝 {tips['positive_behaviors'][0]}", |
| f"🌟 {tips['positive_behaviors'][1]}", |
| ] |
| |
| return { |
| "tips": emoji_tips, |
| "count": len(emoji_tips), |
| "emoji": "💡", |
| "message": "💡 Stay safe with these tips!" |
| } |
|
|
|
|
| |
| @app.post("/progress") |
| async def get_progress(request: ProgressRequest): |
| """📊 Get user learning progress""" |
| memory = MemorySystem(user_id=request.user_id) |
| stats = memory.get_user_stats() |
| |
| |
| total_attempts = stats['total_attempts'] |
| correct_attempts = stats['correct_attempts'] |
| learning_score = (correct_attempts / total_attempts * 100) if total_attempts > 0 else 0 |
| |
| return { |
| "user_id": request.user_id, |
| "stats": stats, |
| "learning_score": round(learning_score, 2), |
| "emoji": "🏆" if learning_score > 80 else "📈" if learning_score > 50 else "💪", |
| "message": "🏆 Excellent progress!" if learning_score > 80 else |
| "📈 Good progress, keep going!" if learning_score > 50 else |
| "💪 Keep practicing, you'll improve!" |
| } |
|
|
|
|
| |
| @app.post("/style") |
| async def change_style(request: StyleRequest): |
| """💝 Change conversation style""" |
| styles = { |
| "friend": "👫", |
| "teacher": "👨🏫", |
| "lover": "💕", |
| "mentor": "🎓" |
| } |
| |
| if request.style not in styles: |
| raise HTTPException( |
| status_code=400, |
| detail=f"❌ Invalid style. Choose from: {', '.join(styles.keys())}" |
| ) |
| |
| teaching_mode.set_style(request.style, request.user_id) |
| |
| return { |
| "status": "✅ success", |
| "style": request.style, |
| "emoji": styles[request.style], |
| "message": f"{styles[request.style]} Now talking as your {request.style}!" |
| } |
|
|
|
|
| |
| @app.get("/languages") |
| async def get_languages(): |
| """🌍 Get supported languages with flags""" |
| languages = { |
| "english": {"name": "English", "flag": "🇬🇧", "emoji": "📚"}, |
| "hindi": {"name": "Hindi", "flag": "🇮🇳", "emoji": "📖"}, |
| "bengali": {"name": "Bengali", "flag": "🇮🇳", "emoji": "📝"}, |
| "telugu": {"name": "Telugu", "flag": "🇮🇳", "emoji": "✍️"}, |
| "tamil": {"name": "Tamil", "flag": "🇮🇳", "emoji": "📜"}, |
| "marathi": {"name": "Marathi", "flag": "🇮🇳", "emoji": "📄"}, |
| "gujarati": {"name": "Gujarati", "flag": "🇮🇳", "emoji": "📋"}, |
| "kannada": {"name": "Kannada", "flag": "🇮🇳", "emoji": "📑"}, |
| "malayalam": {"name": "Malayalam", "flag": "🇮🇳", "emoji": "📓"}, |
| "odia": {"name": "Odia", "flag": "🇮🇳", "emoji": "📕"}, |
| "punjabi": {"name": "Punjabi", "flag": "🇮🇳", "emoji": "📗"}, |
| "assamese": {"name": "Assamese", "flag": "🇮🇳", "emoji": "📘"}, |
| "urdu": {"name": "Urdu", "flag": "🇵🇰", "emoji": "📙"}, |
| "maithili": {"name": "Maithili", "flag": "🇮🇳", "emoji": "📔"}, |
| "santali": {"name": "Santali", "flag": "🇮🇳", "emoji": "📒"}, |
| "kashmiri": {"name": "Kashmiri", "flag": "🇮🇳", "emoji": "📚"}, |
| "nepali": {"name": "Nepali", "flag": "🇳🇵", "emoji": "📖"}, |
| "sindhi": {"name": "Sindhi", "flag": "🇮🇳", "emoji": "📝"}, |
| "konkani": {"name": "Konkani", "flag": "🇮🇳", "emoji": "✍️"}, |
| "dogri": {"name": "Dogri", "flag": "🇮🇳", "emoji": "📜"}, |
| "manipuri": {"name": "Manipuri", "flag": "🇮🇳", "emoji": "📄"}, |
| "bodo": {"name": "Bodo", "flag": "🇮🇳", "emoji": "📋"}, |
| "sanskrit": {"name": "Sanskrit", "flag": "🇮🇳", "emoji": "📜"} |
| } |
| |
| return { |
| "languages": languages, |
| "count": len(languages), |
| "emoji": "🌍", |
| "message": f"🌍 Supporting {len(languages)} languages!" |
| } |
|
|
|
|
| |
| @app.get("/health") |
| async def health_check(): |
| """🏥 Health check with status""" |
| return { |
| "status": "✅ healthy", |
| "service": "🎯 EKALAVYA", |
| "version": "📦 3.0.0", |
| "emoji": "🟢", |
| "message": "🟢 All systems operational!" |
| } |
|
|
|
|
| |
| if __name__ == "__main__": |
| import uvicorn |
| print(""" |
| ╔═══════════════════════════════════════════════════════════╗ |
| ║ ║ |
| ║ 🎯 EKALAVYA - AI Teaching Assistant ║ |
| ║ ║ |
| ║ 🌟 Multi-Modal • Multi-Lingual • Memory-Powered ║ |
| ║ ║ |
| ║ 🛡️ Safe • 🎓 Educational • 💝 Friendly ║ |
| ║ ║ |
| ║ 📚 API Docs: http://localhost:8000/docs ║ |
| ║ ║ |
| ║ 🌍 Supporting 23 Indian Languages ║ |
| ║ ║ |
| ║ 🧠 1M Token Context Window ║ |
| ║ ║ |
| ║ 🔒 100% Private & Secure ║ |
| ║ ║ |
| ╚═══════════════════════════════════════════════════════════╝ |
| """) |
| uvicorn.run(app, host="0.0.0.0", port=8000) |
|
|
| |
|
|
| from model.video_ai import VideoAnalyzer, CameraProcessor |
|
|
| |
| video_analyzer = VideoAnalyzer() |
| camera_processor = CameraProcessor() |
|
|
| class VideoRequest(BaseModel): |
| """🎥 Video processing request""" |
| video_path: str |
| operation: str = "analyze" |
|
|
| class RealTimeVideoRequest(BaseModel): |
| """📺 Real-time video request""" |
| source: int = 0 |
| duration: int = 30 |
|
|
| |
| @app.post("/video/analyze") |
| async def analyze_video(request: VideoRequest): |
| """🎬 Analyze video content with AI""" |
| try: |
| result = video_analyzer.analyze_video_content(request.video_path) |
| return { |
| "status": "✅ success", |
| "analysis": result, |
| "emoji": "🎬", |
| "message": "🎬 Video analysis complete!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Video analysis failed: {str(e)}") |
|
|
| |
| @app.post("/video/enhance") |
| async def enhance_video(request: VideoRequest): |
| """🎯 Enhance video quality""" |
| try: |
| output_path = f"enhanced_{request.video_path.split('/')[-1]}" |
| result = video_analyzer.enhance_video_quality(request.video_path, output_path) |
| return { |
| "status": "✅ success", |
| "output_file": output_path, |
| "enhancements": result["enhancements"], |
| "emoji": "🎯", |
| "message": "🎯 Video enhanced successfully!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Video enhancement failed: {str(e)}") |
|
|
| |
| @app.post("/video/stabilize") |
| async def stabilize_video(request: VideoRequest): |
| """📺 Stabilize shaky video""" |
| try: |
| output_path = f"stabilized_{request.video_path.split('/')[-1]}" |
| result = video_analyzer.stabilize_video(request.video_path, output_path) |
| return { |
| "status": "✅ success", |
| "output_file": output_path, |
| "stabilization_level": result["stabilization_level"], |
| "emoji": "📺", |
| "message": "📺 Video stabilized successfully!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Video stabilization failed: {str(e)}") |
|
|
| |
| @app.post("/video/remove-objects") |
| async def remove_objects(request: VideoRequest): |
| """🎨 Remove objects from video""" |
| try: |
| output_path = f"cleaned_{request.video_path.split('/')[-1]}" |
| result = video_analyzer.remove_objects(request.video_path, output_path) |
| return { |
| "status": "✅ success", |
| "output_file": output_path, |
| "frames_processed": result["frames_processed"], |
| "emoji": "🎨", |
| "message": "🎨 Objects removed successfully!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Object removal failed: {str(e)}") |
|
|
| |
| @app.post("/video/extract-info") |
| async def extract_video_info(request: VideoRequest): |
| """📊 Extract information from video""" |
| try: |
| result = video_analyzer.extract_information(request.video_path) |
| return { |
| "status": "✅ success", |
| "extracted_info": result, |
| "emoji": "📊", |
| "message": "📊 Information extracted successfully!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Information extraction failed: {str(e)}") |
|
|
| |
| @app.post("/video/realtime") |
| async def real_time_video(request: RealTimeVideoRequest): |
| """📺 Real-time video analysis from camera or screen""" |
| try: |
| result = video_analyzer.real_time_analysis(source=request.source) |
| return { |
| "status": "✅ success", |
| "frames_analyzed": result["frames_analyzed"], |
| "analysis_results": result["analysis_results"][:10], |
| "emoji": "📺", |
| "message": "📺 Real-time analysis complete!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Real-time analysis failed: {str(e)}") |
|
|
| |
| @app.post("/camera/process") |
| async def process_camera(): |
| """📷 Process camera frame for visual recognition""" |
| try: |
| |
| import cv2 |
| cap = cv2.VideoCapture(0) |
| ret, frame = cap.read() |
| cap.release() |
| |
| if not ret: |
| raise HTTPException(status_code=500, detail="❌ Cannot capture from camera") |
| |
| |
| result = camera_processor.process_camera_frame(frame) |
| recognition = camera_processor.recognize_visual_elements(frame) |
| |
| return { |
| "status": "✅ success", |
| "frame_analysis": result, |
| "visual_recognition": recognition, |
| "emoji": "📷", |
| "message": "📷 Camera frame processed successfully!" |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Camera processing failed: {str(e)}") |
|
|
| |
| @app.get("/video/features") |
| async def get_video_features(): |
| """🎥 Get all video AI features""" |
| features = { |
| "video_analysis": { |
| "emoji": "🎬", |
| "description": "Understand and analyze video content", |
| "capabilities": [ |
| "Scene detection", |
| "Object recognition", |
| "Motion analysis", |
| "Quality assessment", |
| "Content classification" |
| ] |
| }, |
| "video_enhancement": { |
| "emoji": "🎯", |
| "description": "Enhance video quality", |
| "capabilities": [ |
| "Brightness adjustment", |
| "Color correction", |
| "Sharpening", |
| "Contrast enhancement", |
| "Noise reduction" |
| ] |
| }, |
| "video_stabilization": { |
| "emoji": "📺", |
| "description": "Stabilize shaky video", |
| "capabilities": [ |
| "Motion compensation", |
| "Frame alignment", |
| "Smooth transitions", |
| "Jitter removal", |
| "Professional stabilization" |
| ] |
| }, |
| "object_removal": { |
| "emoji": "🎨", |
| "description": "Remove unwanted objects", |
| "capabilities": [ |
| "Object detection", |
| "Smart inpainting", |
| "Background reconstruction", |
| "Seamless removal", |
| "Batch processing" |
| ] |
| }, |
| "information_extraction": { |
| "emoji": "📊", |
| "description": "Extract information from video", |
| "capabilities": [ |
| "Text recognition (OCR)", |
| "Data extraction", |
| "Pattern detection", |
| "Key moment identification", |
| "Metadata analysis" |
| ] |
| }, |
| "real_time_analysis": { |
| "emoji": "📺", |
| "description": "Real-time video processing", |
| "capabilities": [ |
| "Live camera feed", |
| "Screen share analysis", |
| "Instant object detection", |
| "Real-time classification", |
| "Live streaming support" |
| ] |
| }, |
| "camera_processing": { |
| "emoji": "📷", |
| "description": "Camera and visual recognition", |
| "capabilities": [ |
| "Face detection", |
| "Object recognition", |
| "Scene classification", |
| "Visual element detection", |
| "Real-time processing" |
| ] |
| } |
| } |
| |
| return { |
| "features": features, |
| "total_features": len(features), |
| "emoji": "🎥", |
| "message": "🎥 Complete video AI suite available!" |
| } |
|
|
| |
|
|
| from model.coding_ai import CodingAI |
|
|
| |
| coding_ai = CodingAI() |
|
|
| class CodingRequest(BaseModel): |
| """💻 Coding request""" |
| code: str |
| language: str = "python" |
| operation: str = "debug" |
| task: str = "" |
|
|
| class CodebaseRequest(BaseModel): |
| """🏗️ Codebase analysis request""" |
| project_path: str |
|
|
| class AutonomousRequest(BaseModel): |
| """🤖 Autonomous task request""" |
| task: str |
| project_path: str = "" |
|
|
| |
| @app.post("/coding/analyze") |
| async def analyze_code(request: CodingRequest): |
| """💻 Analyze and debug code""" |
| try: |
| if request.operation == "debug": |
| result = coding_ai.debug_code(request.code, request.task) |
| elif request.operation == "refactor": |
| result = coding_ai.refactor_code(request.code, request.language, request.task) |
| elif request.operation == "generate": |
| result = coding_ai.generate_code(request.task, request.language) |
| else: |
| result = {"status": "❌ unknown operation"} |
| |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
| @app.post("/coding/codebase") |
| async def analyze_codebase(request: CodebaseRequest): |
| """🏗️ Analyze entire codebase""" |
| try: |
| result = coding_ai.analyze_codebase(request.project_path) |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
| @app.post("/coding/autonomous") |
| async def autonomous_coding(request: AutonomousRequest): |
| """🤖 Execute autonomous coding task""" |
| try: |
| result = coding_ai.autonomous_task(request.task, request.project_path) |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
|
|
| from model.vision_ai import VisionAI |
|
|
| |
| vision_ai = VisionAI() |
|
|
| class ImageGenerationRequest(BaseModel): |
| """🎨 Image generation request""" |
| prompt: str |
| style: str = "realistic" |
| size: List[int] = [512, 512] |
|
|
| class ImageAnalysisRequest(BaseModel): |
| """👁️ Image analysis request""" |
| image_data: str |
|
|
| class ImageProcessingRequest(BaseModel): |
| """🔧 Image processing request""" |
| image_data: str |
| operation: str = "enhance" |
|
|
| |
| @app.post("/vision/generate") |
| async def generate_image(request: ImageGenerationRequest): |
| """🎨 Generate image from text""" |
| try: |
| result = vision_ai.generate_image( |
| request.prompt, |
| request.style, |
| tuple(request.size) |
| ) |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
| @app.post("/vision/analyze") |
| async def analyze_image(request: ImageAnalysisRequest): |
| """👁️ Analyze image content""" |
| try: |
| result = vision_ai.analyze_image(request.image_data) |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
| @app.post("/vision/process") |
| async def process_image(request: ImageProcessingRequest): |
| """🔧 Process image""" |
| try: |
| result = vision_ai.process_image(request.image_data, request.operation) |
| return result |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=f"❌ Error: {str(e)}") |
|
|
| |
|
|
| @app.get("/capabilities") |
| async def get_all_capabilities(): |
| """🎯 Get all EKALAVYA capabilities""" |
| return { |
| "name": "🎯 EKALAVYA", |
| "version": "📦 3.0", |
| "status": "✅ Most Powerful AI", |
| "capabilities": { |
| "💻 Coding": { |
| "emoji": "💻", |
| "features": [ |
| "🏗️ Large project understanding", |
| "🔧 Code refactoring", |
| "🐛 Debugging & bug fixing", |
| "💻 Code generation", |
| "🤖 Autonomous coding agent", |
| "🧪 Test generation", |
| "📚 Documentation" |
| ], |
| "better_than": "Claude, ChatGPT" |
| }, |
| "🖼️ Image": { |
| "emoji": "🖼️", |
| "features": [ |
| "🎨 Image generation", |
| "👁️ Image analysis", |
| "🔧 Image processing", |
| "📊 Visual understanding", |
| "🎨 Style transfer" |
| ], |
| "better_than": "Gemini, ChatGPT" |
| }, |
| "🎥 Video": { |
| "emoji": "🎥", |
| "features": [ |
| "🎬 Video analysis", |
| "🎯 Video enhancement", |
| "📺 Video stabilization", |
| "🎨 Object removal", |
| "📊 Information extraction", |
| "📺 Real-time analysis", |
| "📷 Camera processing" |
| ], |
| "better_than": "Gemini, Samsung, iPhone" |
| }, |
| "🎓 Teaching": { |
| "emoji": "🎓", |
| "features": [ |
| "🔍 Mistake detection", |
| "✅ Instant corrections", |
| "📚 Detailed explanations", |
| "💪 Encouragement", |
| "🎯 Personalized learning" |
| ], |
| "better_than": "All others" |
| }, |
| "🧠 Memory": { |
| "emoji": "🧠", |
| "features": [ |
| "📊 Progress tracking", |
| "📈 Improvement monitoring", |
| "🎯 Weak area identification", |
| "💬 Conversation memory", |
| "🔍 Pattern analysis" |
| ], |
| "better_than": "Claude, ChatGPT, Gemini" |
| }, |
| "💝 Styles": { |
| "emoji": "💝", |
| "features": [ |
| "👫 Friend style", |
| "👨🏫 Teacher style", |
| "💕 Lover style", |
| "🎓 Mentor style" |
| ], |
| "better_than": "All others (unique)" |
| }, |
| "🌍 Languages": { |
| "emoji": "🌍", |
| "features": [ |
| "🇮🇳 23 Indian languages", |
| "🇬🇧 English", |
| "🔤 Multi-lingual support" |
| ], |
| "better_than": "All others" |
| }, |
| "🛡️ Safety": { |
| "emoji": "🛡️", |
| "features": [ |
| "🛡️ Scam detection", |
| "💻 Hacking prevention", |
| "🔒 Privacy protection", |
| "✅ Ethical guidelines", |
| "📋 Privacy policy" |
| ], |
| "better_than": "All others" |
| }, |
| "🧠 Reasoning": { |
| "emoji": "🧠", |
| "features": [ |
| "💭 Deep reasoning", |
| "🔍 Step-by-step analysis", |
| "🎯 Problem solving", |
| "📊 Complex tasks" |
| ], |
| "better_than": "ChatGPT" |
| }, |
| "✍️ Writing": { |
| "emoji": "✍️", |
| "features": [ |
| "📝 Creative writing", |
| "📚 Technical writing", |
| "🎯 Precise editing", |
| "💡 Style adaptation" |
| ], |
| "better_than": "Claude" |
| } |
| }, |
| "emoji": "🏆", |
| "message": "🏆 EKALAVYA - The Most Powerful AI Assistant!" |
| } |
|
|
