Update app.py

app.py

@@ -1,151 +1,151 @@
-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
+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 which should be in bulleted point>"
+}}
+
+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