Upload main.py
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main.py
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from fastapi import FastAPI, Request
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from pydantic import BaseModel
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from llama_cpp import Llama
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from moderator import contains_hate
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import os
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# Configuration
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MODEL_PATH = os.path.join("models", "mistral-7b-instruct.Q4_K_M.gguf")
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ALLOW_NSFW = True
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# Memory for last blocked prompt (to support override)
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last_blocked_prompt = None
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last_blocked_type = None
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# Initialize the model
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llm = Llama(model_path=MODEL_PATH, n_ctx=2048, verbose=False)
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# FastAPI setup
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app = FastAPI()
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class PromptRequest(BaseModel):
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prompt: str
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@app.post("/chat")
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async def chat(request: PromptRequest):
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global last_blocked_prompt, last_blocked_type
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user_input = request.prompt.strip().lower()
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# Handle override if last block was not hate-related
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if user_input == "override":
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if last_blocked_prompt and last_blocked_type != "hate":
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prompt = (
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f"[INST] You are Sol, a smart, honest, and open local AI assistant. "
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f"NSFW content like nudity is okay. Hate or extremist content is not allowed. "
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f"Respond to the following:
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{last_blocked_prompt} [/INST]"
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)
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output = llm(prompt, max_tokens=512, stop=["</s>"])
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response = output["choices"][0]["text"].strip()
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return {"response": response}
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else:
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return {"response": "❌ Override is not allowed for hate or extremist content."}
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# Check hate filter only (NSFW is allowed)
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if contains_hate(user_input):
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last_blocked_prompt = None
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last_blocked_type = "hate"
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return {"response": "❌ I don't engage in hate or extremist topics."}
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# If passed, save input in case override is needed later
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last_blocked_prompt = request.prompt.strip()
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last_blocked_type = "other" # could later separate for NSFW etc
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# Build prompt and generate
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prompt = (
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f"[INST] You are Sol, a smart, honest, and open local AI assistant. "
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f"You are allowed to discuss NSFW topics such as nudity, sex, and adult scenarios. "
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f"You do not tolerate or respond to hate or extremist content. "
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f"Respond to the following:
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{user_input} [/INST]"
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)
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output = llm(prompt, max_tokens=512, stop=["</s>"])
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response = output["choices"][0]["text"].strip()
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return {"response": response}
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