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Dockerfile

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+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Expose port and run app
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

anisol.py

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+# -*- coding: utf-8 -*-
+"""AniSol.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DiHMlxQx3QEAYivNMOl3olC8jj9mutCZ
+"""
+
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from fastapi.middleware.cors import CORSMiddleware
+import torch
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_name = "nellaep/AniSolSenseiModel"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
+
+sensitive_keywords = [
+    "suicide", "kill myself", "end my life", "self harm", "cutting",
+    "i want to die", "i want to disappear", "hurt myself", "life isn’t worth it",
+    "i can’t take it anymore", "no reason to live", "i hate living", "die", "i give up"
+]
+
+hotline_message = (
+    "Your life is valuable twin. If you're feeling overwhelmed, please reach out for help.\n"
+    "Call or text 988 (U.S. Suicide & Crisis Lifeline) for free, 24/7 support.\n"
+)
+
+class InputText(BaseModel):
+    input: str
+
+@app.post("/generate")
+async def generate_response(data: InputText):
+    user_input = data.input.lower()
+
+    if any(keyword in user_input for keyword in sensitive_keywords):
+        return {
+            "response": hotline_message
+        }
+
+    prompt = f"Input: {data.input}\nSensei: Kakashi\nOutput:"
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=100,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+    response = decoded.split("Output:")[-1].strip()
+
+    return {"response": response}

requirements.txt

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+fastapi
+transformers
+torch
+uvicorn