Create app.py

app.py

@@ -0,0 +1,32 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+app = FastAPI()
+
+# 1. Define your specific model details
+REPO_ID = "SarmaHighOnAI/physics-tutor-gguf"
+FILENAME = "llama-3.2-3b-instruct.Q4_K_M.gguf"
+
+print("Downloading your fine-tuned model...")
+model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+print("Loading model...")
+# n_threads=2 ensures it runs smoothly on the free tier CPU
+llm = Llama(model_path=model_path, n_ctx=2048, n_threads=2)
+
+class Request(BaseModel):
+    prompt: str
+
+@app.get("/")
+def home():
+    return {"status": "Running", "message": "Your Fine-Tuned Physics API is Live!"}
+
+@app.post("/generate")
+def generate(request: Request):
+    # Standard prompt format for Llama 3
+    formatted_prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{request.prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    
+    output = llm(formatted_prompt, max_tokens=512, stop=["<|eot_id|>"], echo=False)
+    return {"response": output["choices"][0]["text"]}