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Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Expose port 7860 (HuggingFace Spaces standard)
+EXPOSE 7860
+
+# Run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +1,73 @@
----
-title: Synhack
-emoji: 👀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Architecture Evaluator LLM
+emoji: 🏗️
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+# Architecture Evaluator LLM
+
+An AI-powered system design architecture evaluator using **Groq's FREE ultra-fast inference** with advanced reasoning models.
+
+## Features
+
+- 🚀 **FREE** - Uses Groq's free API (no costs!)
+- ⚡ **FAST** - Groq provides blazing-fast inference
+- 🧠 **Smart** - DeepSeek R1 reasoning model for deep analysis
+- Evaluates system design architectures based on multiple heuristics
+- Provides detailed suggestions and recommendations
+- Returns structured JSON responses
+- Easy to integrate with any frontend
+
+## API Endpoints
+
+### POST /evaluate
+Evaluates a system architecture.
+
+**Request Body:**
+```json
+{
+  "question": "Your question or context about the architecture",
+  "architecture": {
+    // Your architecture JSON with components and links
+  }
+}
+```
+
+**Response:**
+```json
+{
+  "heuristic_scores": {
+    "DURABILITY": 8.5,
+    "AVAILABILITY": 9.0,
+    "ENERGY_EFFICIENCY": 7.5,
+    "CONSISTENCY": 8.0,
+    "MAINTAINABILITY": 7.0,
+    "LATENCY": 8.5,
+    "COST": 6.5,
+    "SECURITY": 8.0,
+    "THROUGHPUT": 8.5,
+    "SCALABILITY": 9.0
+  },
+  "suggestion": "Your architecture shows strong scalability..."
+}
+```
+
+## Environment Variables
+
+- `GROQ_API_KEY`: Your Groq API key (required - get free at https://console.groq.com)
+- `MODEL_NAME`: Model to use (default: deepseek-r1-distill-llama-70b)
+  - Available models:
+    - `deepseek-r1-distill-llama-70b` - Best reasoning (default)
+    - `llama-3.3-70b-versatile` - Great all-rounder
+    - `llama-3.1-70b-versatile` - Fast and capable
+
+## Getting Your Free Groq API Key
+
+1. Go to https://console.groq.com
+2. Sign up for a free account
+3. Navigate to API Keys section
+4. Create a new API key
+5. Copy and use it (completely FREE!)

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Dict, List, Any, Optional
+import os
+from groq import Groq
+
+app = FastAPI(title="Architecture Evaluator LLM")
+
+# Enable CORS for all origins (adjust in production)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize Groq client - FREE and FAST!
+client = Groq(
+    api_key=os.getenv("GROQ_API_KEY", "your-groq-api-key-here")
+)
+
+class EvaluationRequest(BaseModel):
+    question: str
+    architecture: Dict[str, Any]
+
+class HeuristicScores(BaseModel):
+    DURABILITY: float
+    AVAILABILITY: float
+    ENERGY_EFFICIENCY: float
+    CONSISTENCY: float
+    MAINTAINABILITY: float
+    LATENCY: float
+    COST: float
+    SECURITY: float
+    THROUGHPUT: float
+    SCALABILITY: float
+
+class EvaluationResponse(BaseModel):
+    heuristic_scores: HeuristicScores
+    suggestion: str
+
+def create_evaluation_prompt(question: str, architecture: Dict[str, Any]) -> str:
+    """Create a detailed prompt for the LLM to evaluate the architecture."""
+    
+    prompt = f"""You are an expert system architect. Analyze the following system design architecture and provide an evaluation.
+
+**Question/Context:**
+{question}
+
+**Architecture JSON:**
+{architecture}
+
+**Your Task:**
+1. Analyze the overall architecture based on the components and their connections (links)
+2. Calculate aggregate heuristic scores (0-10 scale) for the entire architecture considering:
+   - Individual component scores
+   - How components interact through links
+   - Overall system design patterns
+   - Redundancy and fault tolerance
+   - Data flow efficiency
+
+3. Provide constructive suggestions on:
+   - What's good about this architecture
+   - What could be improved
+   - Specific recommendations
+   - Alternative approaches if applicable
+
+**Important:** You must respond with ONLY a valid JSON object in this exact format:
+{{
+  "heuristic_scores": {{
+    "DURABILITY": <float between 0-10>,
+    "AVAILABILITY": <float between 0-10>,
+    "ENERGY_EFFICIENCY": <float between 0-10>,
+    "CONSISTENCY": <float between 0-10>,
+    "MAINTAINABILITY": <float between 0-10>,
+    "LATENCY": <float between 0-10>,
+    "COST": <float between 0-10>,
+    "SECURITY": <float between 0-10>,
+    "THROUGHPUT": <float between 0-10>,
+    "SCALABILITY": <float between 0-10>
+  }},
+  "suggestion": "<your detailed analysis and suggestions as a string>"
+}}
+
+Do not include any text outside the JSON object. Ensure all scores are numeric values between 0 and 10."""
+    
+    return prompt
+
+@app.get("/")
+async def root():
+    return {
+        "message": "Architecture Evaluator LLM API",
+        "status": "online",
+        "endpoints": {
+            "/evaluate": "POST - Evaluate a system architecture",
+            "/health": "GET - Health check"
+        }
+    }
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy"}
+
+@app.post("/evaluate", response_model=EvaluationResponse)
+async def evaluate_architecture(request: EvaluationRequest):
+    """
+    Evaluate a system design architecture using LLM.
+    
+    Args:
+        request: Contains the question and architecture JSON
+        
+    Returns:
+        EvaluationResponse with heuristic scores and suggestions
+    """
+    try:
+        # Create the prompt
+        prompt = create_evaluation_prompt(request.question, request.architecture)
+        
+        # Call Groq LLM - Using best reasoning model (deepseek-r1-distill-llama-70b)
+        # Alternative models: llama-3.3-70b-versatile, llama-3.1-70b-versatile
+        response = client.chat.completions.create(
+            model=os.getenv("MODEL_NAME", "llama-3.3-70b-versatile"),
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an expert system architect who provides detailed analysis and returns responses in strict JSON format."
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            temperature=0.7,
+            response_format={"type": "json_object"}  # Enforce JSON response
+        )
+        
+        # Parse the response
+        import json
+        result = json.loads(response.choices[0].message.content)
+        
+        # Validate and return
+        return EvaluationResponse(**result)
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error evaluating architecture: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)  # Port 7860 is standard for HuggingFace Spaces

requirements.txt

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+fastapi==0.115.0
+uvicorn[standard]==0.32.0
+pydantic==2.9.2
+groq==0.11.0
+python-multipart==0.0.12