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app.py

@@ -1,11 +1,9 @@
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
-import asyncio
-import os
 from datetime import datetime
 
-app = FastAPI(title="AI Model Runner", version="1.0.0")
+app = FastAPI(title="AI Model Runner - Simple", version="1.0.0")
 
 # Enable CORS
 app.add_middleware(
@@ -16,57 +14,17 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-class ChatRequest(BaseModel):
-    message: str
-    history: list = []
-
-class CodeRequest(BaseModel):
-    code: str
-    task: str = "explain"
-
-class ReasoningRequest(BaseModel):
-    problem: str
-    context: str = ""
-
-class SentimentRequest(BaseModel):
-    text: str
-
 class AgenticRequest(BaseModel):
     task: str
     context: str = ""
-    available_tools: list = None
-
-class ToolDefinition(BaseModel):
-    name: str
-    description: str
-    parameters: dict
-
-class ToolCall(BaseModel):
-    id: str
-    function: dict
-
-# Simple in-memory models storage
-class ModelInfo:
-    def __init__(self, name, status, description):
-        self.name = name
-        self.status = status
-        self.description = description
 
 @app.get("/")
 async def root():
     return {
-        "message": "AI Model Runner API",
+        "message": "AI Model Runner - Simple",
         "version": "1.0.0",
         "status": "running",
-        "endpoints": {
-            "chat": "/chat",
-            "code": "/code", 
-            "reasoning": "/reasoning",
-            "sentiment": "/analyze-sentiment",
-            "agentic": "/agentic",
-            "upload": "/upload",
-            "models": "/models"
-        }
+        "endpoints": ["/", "/health", "/models", "/agentic"]
     }
 
 @app.get("/health")
@@ -75,257 +33,76 @@ async def health_check():
 
 @app.get("/models")
 async def get_models():
-    models = [
-        ModelInfo("lightweight-chat", "loaded", "Lightweight conversational model"),
-        ModelInfo("simple-code", "loaded", "Basic code analysis model"),
-        ModelInfo("text-reasoner", "loaded", "Problem solving assistant"),
-        ModelInfo("sentiment-analyzer", "loaded", "Text sentiment analysis"),
-        ModelInfo("agentic-model", "loaded", "Agentic model with exceptional tool use and Interleaved Thinking")
-    ]
     return {
         "models": [
             {
-                "name": model.name,
-                "status": model.status,
-                "description": model.description
-            } for model in models
+                "name": "agentic-model",
+                "status": "loaded", 
+                "description": "Agentic model with exceptional tool use and Interleaved Thinking"
+            }
         ]
     }
 
-@app.post("/chat")
-async def chat_endpoint(request: ChatRequest):
-    try:
-        # Simulated lightweight response
-        if "hello" in request.message.lower():
-            response = "Hello! I'm your AI assistant. How can I help you today?"
-        elif "code" in request.message.lower():
-            response = "I can help analyze code! Please provide the code you'd like me to review."
-        elif "reason" in request.message.lower():
-            response = "I'm here to help with problem-solving and reasoning. What's the challenge you're facing?"
-        else:
-            response = f"You said: '{request.message}'. This is a response from the lightweight AI model."
-        
-        return {
-            "response": response,
-            "conversation_id": f"conv_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-            "status": "success"
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/code")
-async def code_endpoint(request: CodeRequest):
-    try:
-        # Simulated lightweight code analysis
-        if len(request.code.strip()) == 0:
-            return {"error": "No code provided"}
-        
-        analysis = {
-            "language": "Unknown",
-            "complexity": "Low",
-            "issues": [],
-            "suggestions": [],
-            "explanation": f"Code analysis for: {request.code[:100]}..."
-        }
-        
-        if "def " in request.code:
-            analysis["language"] = "Python"
-            analysis["explanation"] = "This appears to be a Python function definition"
-        elif "function" in request.code or "function(" in request.code:
-            analysis["language"] = "JavaScript"
-            analysis["explanation"] = "This appears to be a JavaScript function"
-        
-        return analysis
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/reasoning")
-async def reasoning_endpoint(request: ReasoningRequest):
-    try:
-        # Simulated reasoning response
-        problem = request.problem
-        if "math" in problem.lower() or any(char.isdigit() for char in problem):
-            response = "This appears to be a mathematical problem. Let me break it down step by step."
-        elif "logic" in problem.lower():
-            response = "This is a logical reasoning problem. Let's analyze the given information and draw conclusions."
-        else:
-            response = f"Analyzing your problem: '{problem}'. This requires careful step-by-step reasoning."
-        
-        return {
-            "reasoning": response,
-            "steps": [
-                "Understand the problem",
-                "Identify key information", 
-                "Apply logical reasoning",
-                "Draw conclusions"
-            ],
-            "confidence": 0.85
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/analyze-sentiment")
-async def sentiment_endpoint(request: SentimentRequest):
-    try:
-        text = request.text.lower()
-        
-        # Simple sentiment analysis
-        positive_words = ["good", "great", "excellent", "amazing", "wonderful", "fantastic", "love", "like"]
-        negative_words = ["bad", "terrible", "awful", "horrible", "hate", "dislike", "worst", "disaster"]
-        
-        pos_score = sum(1 for word in positive_words if word in text)
-        neg_score = sum(1 for word in negative_words if word in text)
-        
-        if pos_score > neg_score:
-            sentiment = "positive"
-            confidence = 0.8
-        elif neg_score > pos_score:
-            sentiment = "negative"
-            confidence = 0.8
-        else:
-            sentiment = "neutral"
-            confidence = 0.6
-        
-        return {
-            "sentiment": sentiment,
-            "confidence": confidence,
-            "text": request.text
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/upload")
-async def upload_endpoint():
-    return {
-        "message": "File upload endpoint ready",
-        "supported_formats": ["txt", "py", "js", "html", "md"],
-        "max_size": "10MB"
-    }
-
 @app.post("/agentic")
 async def agentic_endpoint(request: AgenticRequest):
-    try:
-        # Default available tools if none provided
-        available_tools = request.available_tools or [
-            {
+    # Simulate Interleaved Thinking process
+    task_lower = request.task.lower()
+    reasoning_details = ""
+    tool_calls = []
+    content = ""
+    
+    if "search" in task_lower or "find" in task_lower:
+        reasoning_details = "I need to search for information to complete this task. Let me use the search_web tool."
+        tool_calls = [{
+            "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            "function": {
                 "name": "search_web",
-                "description": "Search the web for information",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string", "description": "Search query"}
-                    },
-                    "required": ["query"]
-                }
-            },
-            {
+                "arguments": f'{{"query": "{request.task}"}}'
+            }
+        }]
+        content = f"I've analyzed your task: '{request.task}'. I'm searching for relevant information."
+    elif "code" in task_lower:
+        reasoning_details = "This task requires code analysis. I need to analyze the code structure."
+        tool_calls = [{
+            "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            "function": {
                 "name": "analyze_code",
-                "description": "Analyze code for issues and improvements",
-                "parameters": {
-                    "type": "object", 
-                    "properties": {
-                        "code": {"type": "string", "description": "Code to analyze"},
-                        "task": {"type": "string", "description": "Analysis task"}
-                    },
-                    "required": ["code"]
-                }
-            },
-            {
+                "arguments": f'{{"code": "{request.context}", "task": "comprehensive_analysis"}}'
+            }
+        }]
+        content = f"Analyzing your code-related task: '{request.task}'."
+    elif "weather" in task_lower:
+        location = request.context or "current location"
+        reasoning_details = "This task requires weather information. I need to get current weather data."
+        tool_calls = [{
+            "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            "function": {
                 "name": "get_weather",
-                "description": "Get current weather information",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {"type": "string", "description": "Location to get weather for"}
-                    },
-                    "required": ["location"]
-                }
-            },
-            {
+                "arguments": f'{{"location": "{location}"}}'
+            }
+        }]
+        content = f"Getting weather information for: '{request.task}'."
+    elif any(char.isdigit() for char in task_lower) and ("+" in task_lower or "-" in task_lower or "*" in task_lower):
+        reasoning_details = "This is a mathematical computation task. I need to evaluate the expression."
+        tool_calls = [{
+            "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            "function": {
                 "name": "math_calc",
-                "description": "Perform mathematical calculations",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "expression": {"type": "string", "description": "Mathematical expression to evaluate"}
-                    },
-                    "required": ["expression"]
-                }
+                "arguments": f'{{"expression": "{request.task}"}}'
             }
-        ]
-        
-        # Generate reasoning and determine tool usage
-        task_lower = request.task.lower()
-        reasoning_details = ""
-        tool_calls = []
-        content = ""
-        
-        # Simulate Interleaved Thinking process
-        if "search" in task_lower or "find" in task_lower or "look up" in task_lower:
-            reasoning_details = "I need to search for information to complete this task. Let me use the search_web tool to find relevant information."
-            tool_calls = [{
-                "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                "function": {
-                    "name": "search_web",
-                    "arguments": f'{{"query": "{request.task}"}}'
-                }
-            }]
-            content = f"I've analyzed your task: '{request.task}'. I'm searching for relevant information to provide a comprehensive response."
-            
-        elif "code" in task_lower or "analyze" in task_lower:
-            reasoning_details = "This task requires code analysis. I need to analyze the code structure and identify potential improvements. Let me use the analyze_code tool."
-            tool_calls = [{
-                "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                "function": {
-                    "name": "analyze_code", 
-                    "arguments": f'{{"code": "{request.context}", "task": "comprehensive_analysis"}}'
-                }
-            }]
-            content = f"Analyzing your code-related task: '{request.task}'. I'm performing a detailed code analysis to provide insights and recommendations."
-            
-        elif "weather" in task_lower or "temperature" in task_lower:
-            reasoning_details = "This task requires current weather information. I need to get the weather data for the specified location using the get_weather tool."
-            # Extract location from task or context
-            location = "current location"  # default
-            if request.context:
-                location = request.context
-            tool_calls = [{
-                "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                "function": {
-                    "name": "get_weather",
-                    "arguments": f'{{"location": "{location}"}}'
-                }
-            }]
-            content = f"Getting weather information for your request: '{request.task}'."
-            
-        elif any(char.isdigit() for char in task_lower) and ("calculate" in task_lower or "compute" in task_lower or "+" in task_lower or "-" in task_lower or "*" in task_lower or "/" in task_lower):
-            reasoning_details = "This is a mathematical computation task. I need to evaluate the mathematical expression to provide an accurate result."
-            tool_calls = [{
-                "id": f"call_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                "function": {
-                    "name": "math_calc",
-                    "arguments": f'{{"expression": "{request.task}"}}'
-                }
-            }]
-            content = f"Processing your mathematical task: '{request.task}'."
-            
-        else:
-            # General reasoning task
-            reasoning_details = "This is a complex reasoning task that requires analysis and step-by-step thinking. Let me break this down systematically."
-            content = f"Working on your task: '{request.task}'. I'm applying systematic reasoning to provide a comprehensive solution."
-            # In a real implementation, this might trigger multiple tool calls
-        
-        return {
-            "reasoning_details": reasoning_details,
-            "content": content,
-            "tool_calls": tool_calls,
-            "available_tools": available_tools,
-            "status": "success",
-            "thinking_process": "Interleaved thinking enabled - reasoning performed between tool interactions"
-        }
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        }]
+        content = f"Processing mathematical task: '{request.task}'."
+    else:
+        reasoning_details = "This is a complex reasoning task requiring systematic analysis."
+        content = f"Working on your task: '{request.task}'. Applying systematic reasoning."
+    
+    return {
+        "reasoning_details": reasoning_details,
+        "content": content,
+        "tool_calls": tool_calls,
+        "status": "success",
+        "thinking_process": "Interleaved thinking enabled - reasoning performed between tool interactions"
+    }
 
 if __name__ == "__main__":
     import uvicorn