Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Optional
+import os
+from groq import Groq
+
+# Initialize FastAPI app
+app = FastAPI(title="AI Chat API", description="FastAPI backend for AI chat")
+
+# Initialize Groq client
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
+
+# Request/Response models
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    message: str
+    history: Optional[List[Message]] = []
+
+class ChatResponse(BaseModel):
+    reply: str
+
+@app.get("/")
+def root():
+    """Welcome endpoint"""
+    return {
+        "message": "Welcome to AI Chat API",
+        "endpoints": {
+            "/chat": "POST - Send a message and get AI response",
+            "/docs": "GET - API documentation"
+        }
+    }
+
+@app.post("/chat", response_model=ChatResponse)
+def chat(request: ChatRequest):
+    """
+    Chat endpoint that processes messages and returns AI responses
+
+    Args:
+        request: ChatRequest containing message and conversation history
+
+    Returns:
+        ChatResponse with AI reply
+    """
+    # Build messages array
+    messages = []
+
+    # Add conversation history
+    if request.history:
+        for msg in request.history:
+            messages.append({
+                "role": msg.role,
+                "content": msg.content
+            })
+
+    # Add current message
+    messages.append({
+        "role": "user",
+        "content": request.message
+    })
+
+    # Call Groq API
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model="llama-3.3-70b-versatile",
+    )
+
+    # Extract response
+    ai_reply = chat_completion.choices[0].message.content
+
+    return ChatResponse(reply=ai_reply)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)