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client_example.py

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+"""
+Example client for the AI Model Runner API
+"""
+
+import json
+import requests
+from typing import List, Dict, Any
+
+class AIModelRunnerClient:
+    def __init__(self, base_url: str = "http://localhost:8000"):
+        self.base_url = base_url.rstrip("/")
+    
+    def get_api_info(self) -> Dict[str, Any]:
+        """Get API information"""
+        response = requests.get(f"{self.base_url}/")
+        return response.json()
+    
+    def health_check(self) -> Dict[str, Any]:
+        """Check API health"""
+        response = requests.get(f"{self.base_url}/health")
+        return response.json()
+    
+    def list_models(self) -> Dict[str, Any]:
+        """List available models"""
+        response = requests.get(f"{self.base_url}/models")
+        return response.json()
+    
+    def chat(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]:
+        """Send chat message"""
+        data = {
+            "messages": messages,
+            "model": kwargs.get("model", "microsoft/DialoGPT-medium"),
+            "max_length": kwargs.get("max_length", 100),
+            "temperature": kwargs.get("temperature", 0.7)
+        }
+        response = requests.post(f"{self.base_url}/chat", json=data)
+        return response.json()
+    
+    def analyze_code(self, code: str, task: str, language: str = "python") -> Dict[str, Any]:
+        """Analyze code"""
+        data = {
+            "code": code,
+            "task": task,
+            "language": language
+        }
+        response = requests.post(f"{self.base_url}/code", json=data)
+        return response.json()
+    
+    def reasoning(self, problem: str, context: str = "", steps: int = 5) -> Dict[str, Any]:
+        """Perform reasoning"""
+        data = {
+            "problem": problem,
+            "context": context,
+            "steps": steps
+        }
+        response = requests.post(f"{self.base_url}/reasoning", json=data)
+        return response.json()
+    
+    def analyze_sentiment(self, text: str) -> Dict[str, Any]:
+        """Analyze sentiment"""
+        data = {"text": text}
+        response = requests.post(f"{self.base_url}/analyze-sentiment", json=data)
+        return response.json()
+
+def demo():
+    """Demonstrate API usage"""
+    client = AIModelRunnerClient()
+    
+    print("=== AI Model Runner API Demo ===\n")
+    
+    # Check API status
+    print("1. API Status:")
+    status = client.health_check()
+    print(f"   Status: {status}")
+    print()
+    
+    # List models
+    print("2. Available Models:")
+    models = client.list_models()
+    for model in models["models"]:
+        print(f"   - {model['name']} ({model['type']}): {'✓' if model['loaded'] else '✗'}")
+    print()
+    
+    # Chat example
+    print("3. Chat Example:")
+    chat_response = client.chat([
+        {"role": "user", "content": "Hello! How can you help me today?"}
+    ])
+    print(f"   User: Hello! How can you help me today?")
+    print(f"   AI: {chat_response['response']}")
+    print()
+    
+    # Code analysis example
+    print("4. Code Analysis Example:")
+    code = """
+def fibonacci(n):
+    if n <= 1:
+        return n
+    return fibonacci(n-1) + fibonacci(n-2)
+"""
+    code_response = client.analyze_code(code, "explain", "python")
+    print("   Original Code:")
+    print(code)
+    print("   Analysis:")
+    print(code_response["result"])
+    print()
+    
+    # Reasoning example
+    print("5. Reasoning Example:")
+    reasoning_response = client.reasoning(
+        problem="How to implement an efficient sorting algorithm?",
+        context="Working with large datasets",
+        steps=3
+    )
+    print(f"   Problem: How to implement an efficient sorting algorithm?")
+    print("   Reasoning:")
+    print(reasoning_response["reasoning"])
+    print()
+    
+    # Sentiment analysis example
+    print("6. Sentiment Analysis Example:")
+    sentiment = client.analyze_sentiment("I love using this AI API! It's fantastic and very helpful.")
+    print(f"   Text: I love using this AI API! It's fantastic and very helpful.")
+    print(f"   Sentiment: {sentiment['sentiment']}")
+    print(f"   Confidence: {sentiment['confidence']:.2%}")
+    print()
+
+if __name__ == "__main__":
+    try:
+        demo()
+    except requests.exceptions.ConnectionError:
+        print("Error: Cannot connect to the API. Make sure the server is running on http://localhost:8000")
+    except Exception as e:
+        print(f"Error: {e}")