Create main.py

main.py

@@ -0,0 +1,75 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import requests
+import uuid
+from datetime import datetime
+
+app = FastAPI()
+
+class ChatRequest(BaseModel):
+    model: str
+    messages: list
+
+@app.post("/v1/chat/completions")
+async def chat_completions(chat_request: ChatRequest):
+    # Extract the user prompt from the last user message
+    user_prompt = ""
+    for msg in reversed(chat_request.messages):
+        if msg["role"] == "user":
+            user_prompt = msg["content"]
+            break
+
+    # Prepare the payload for blackbox.ai
+    payload = {
+        "query": user_prompt, 
+        "agentMode": True
+    }
+
+    # Define headers (replicating from the original cURL as needed)
+    url = "https://www.blackbox.ai/api/image-generator"
+    headers = {
+        "sec-ch-ua-full-version-list": "\"Google Chrome\";v=\"131.0.6778.266\", \"Chromium\";v=\"131.0.6778.266\", \"Not_A Brand\";v=\"24.0.0.0\"",
+        "sec-ch-ua-platform": "\"Windows\"",
+        "Referer": "https://www.blackbox.ai/agent/create/new",
+        "sec-ch-ua": "\"Google Chrome\";v=\"131\", \"Chromium\";v=\"131\", \"Not_A Brand\";v=\"24\"",
+        "sec-ch-ua-bitness": "\"64\"",
+        "sec-ch-ua-model": "\"\"",
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-arch": "\"x86\"",
+        "sec-ch-ua-full-version": "\"131.0.6778.266\"",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36",
+        "Content-Type": "text/plain;charset=UTF-8",
+        "sec-ch-ua-platform-version": "\"19.0.0\""
+    }
+
+    response = requests.post(url, json=payload, headers=headers, timeout=30)
+    response_json = response.json()
+    markdown_result = response_json.get("markdown", "")
+
+    # Post-process the response from Blackbox if needed
+    cleaned_full_response = markdown_result.strip()
+
+    # Very simplistic token counting
+    prompt_tokens = len(user_prompt.split())
+    completion_tokens = len(cleaned_full_response.split())
+
+    # Build an OpenAI-like response
+    request_id = str(uuid.uuid4())
+    return {
+        "id": request_id,
+        "object": "chat.completion",
+        "created": int(datetime.now().timestamp()),
+        "model": chat_request.model,
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": cleaned_full_response},
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        },
+    }