Upload app.py

app.py

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+from fastapi import FastAPI, HTTPException, Depends, Header, Request
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from typing import List
+from g4f import ChatCompletion
+from slowapi import Limiter
+from slowapi.util import get_remote_address
+
+app = FastAPI()
+
+# Initialize the rate limiter
+limiter = Limiter(key_func=get_remote_address)
+
+# List of available models
+models = [
+    "gpt-4o", "gpt-4o-mini", "gpt-4",
+    "gpt-4-turbo", "gpt-3.5-turbo",
+    "claude-3.7-sonnet", "o3-mini", "o1", "claude-3.5", "llama-3.1-405b", "gemini-flash", "blackboxai-pro", "openchat-3.5", "glm-4-9B", "blackboxai"
+]
+
+# Request model
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    model: str
+    messages: List[Message]
+    streaming: bool = True  # Add streaming support
+
+class ChatResponse(BaseModel):
+    role: str
+    content: str
+
+# Dependency to check API key
+async def verify_api_key(x_api_key: str = Header(...)):
+    if x_api_key != "vs-5wEvIw6vfLKIypGm7uiNoWuXrJcg4vAL":  # Replace with your actual API key
+        raise HTTPException(status_code=403, detail="Invalid API key")
+
+@app.get("/v1/models", tags=["Models"])
+async def get_models():
+    """Endpoint to get the list of available models."""
+    return {"models": models}
+
+@app.post("/v1/chat/completions", tags=["Chat Completion"])
+@limiter.limit("10/minute")  # Rate limit to 10 requests per minute
+async def chat_completion(
+    request: Request,
+    chat_request: ChatRequest,
+    api_key: str = Depends(verify_api_key)
+):
+    # Validate model
+    if chat_request.model not in models:
+        raise HTTPException(status_code=400, detail="Invalid model selected.")
+
+    # Check if messages are provided
+    if not chat_request.messages:
+        raise HTTPException(status_code=400, detail="Messages cannot be empty.")
+
+    # Convert messages to the format expected by ChatCompletion
+    formatted_messages = [{"role": msg.role, "content": msg.content} for msg in chat_request.messages]
+
+    try:
+        if chat_request.streaming:
+            # Stream the response
+            def event_stream():
+                response = ChatCompletion.create(
+                    model=chat_request.model,
+                    messages=formatted_messages,
+                    stream=True  # Enable streaming
+                )
+                
+                for chunk in response:
+                    if isinstance(chunk, dict) and 'choices' in chunk:
+                        for choice in chunk['choices']:
+                            if 'message' in choice:
+                                yield f"data: {choice['message']['content']}\n\n"
+                    else:
+                        yield f"data: {chunk}\n\n"  # Fallback if chunk is not as expected
+
+            return StreamingResponse(event_stream(), media_type="text/event-stream")
+        else:
+            # Non-streaming response
+            response = ChatCompletion.create(
+                model=chat_request.model,
+                messages=formatted_messages
+            )
+
+            if isinstance(response, str):
+                response_content = response  # Directly use if it's a string
+            else:
+                try:
+                    response_content = response['choices'][0]['message']['content']
+                except (IndexError, KeyError):
+                    raise HTTPException(status_code=500, detail="Unexpected response structure.")
+
+            return ChatResponse(role="assistant", content=response_content)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)