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Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = FastAPI()
+
+MODEL_NAME = "mjpsm/progress-generation-model"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(device)
+
+tokenizer.pad_token = tokenizer.eos_token
+
+class Request(BaseModel):
+    text: str
+
+def generate_response(user_input):
+    prompt = f"""<|system|>
+You describe what progress was achieved in one sentence.
+<|user|>
+{user_input}
+<|assistant|>
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=50,
+            temperature=0.6,
+            top_p=0.9,
+            repetition_penalty=1.2,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return decoded.split("<|assistant|>")[-1].strip()
+
+@app.get("/")
+def root():
+    return {"message": "Progress Model API running"}
+
+@app.post("/predict")
+def predict(req: Request):
+    result = generate_response(req.text)
+    return {"output": result}

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch