| from flask import Flask, request |
| from llama_cpp import Llama |
| import requests |
| from bs4 import BeautifulSoup |
|
|
| ENV_URL = "http://192.168.4.1/" |
|
|
| llm = Llama(model_path="model/llama-3.2-1b-Q4_K_M.gguf") |
|
|
| app = Flask(__name__) |
|
|
| def fetch_envpro_values(): |
| r = requests.get(ENV_URL, timeout=3) |
| soup = BeautifulSoup(r.text, "html.parser") |
| text = soup.get_text() |
|
|
| def extract(label): |
| start = text.find(label) |
| if start == -1: |
| return None |
| value = text[start+len(label):].split("\n")[0].strip() |
| return value |
|
|
| return { |
| "temp": extract("Temp:"), |
| "hum": extract("Hum:"), |
| "press": extract("Press:"), |
| "gas": extract("Gas:"), |
| "co2": extract("CO2eq:"), |
| "voc": extract("VOCeq:"), |
| "iaq": extract("IAQ accuracy:") |
| } |
|
|
| @app.route("/interpret") |
| def interpret(): |
| data = fetch_envpro_values() |
|
|
| prompt = ( |
| "ENV PRO DATA:\n" |
| f"Temp: {data['temp']}\n" |
| f"Hum: {data['hum']}\n" |
| f"Press: {data['press']}\n" |
| f"Gas: {data['gas']}\n" |
| f"CO2eq: {data['co2']}\n" |
| f"VOCeq: {data['voc']}\n" |
| f"IAQ accuracy: {data['iaq']}\n\n" |
| "Context: Indoor environment. Do not assume cooking unless the sensor values indicate it.\n" |
| "Instruction: Base your interpretation ONLY on the ENV PRO DATA values above.\n" |
| "Do NOT invent activities.\n" |
| "Do NOT infer smells that are not supported by the data.\n" |
| "The environment may be a bedroom, living room, hallway, kitchen or any indoor space.\n\n" |
| "Task: Output exactly ONE short sentence.\n" |
| "Rules:\n" |
| "1. The sentence MUST start with one of these words: smell, activity, mood, doing\n" |
| "2. The sentence MUST NOT exceed 100 characters.\n" |
| "3. Do NOT output examples.\n" |
| "4. Do NOT output lists.\n" |
| "5. Do NOT explain anything.\n" |
| "6. Output exactly ONE short sentence.\n\n" |
| "Now output the sentence:\n" |
| ) |
|
|
| out = llm(prompt, max_tokens=160) |
| return out["choices"][0]["text"] |
|
|
| |
| @app.route("/manual", methods=["POST"]) |
| def manual(): |
| data = request.json |
|
|
| prompt = ( |
| "ENV PRO DATA:\n" |
| f"Temp: {data['temp']}\n" |
| f"Hum: {data['hum']}\n" |
| f"Press: {data['press']}\n" |
| f"Gas: {data['gas']}\n" |
| f"CO2eq: {data['co2']}\n" |
| f"VOCeq: {data['voc']}\n" |
| f"IAQ accuracy: {data['iaq']}\n\n" |
| "Context: Indoor environment. Do not assume cooking unless the sensor values indicate it.\n" |
| "Instruction: Base your interpretation ONLY on the ENV PRO DATA values above.\n" |
| "Do NOT invent activities.\n" |
| "Do NOT infer smells that are not supported by the data.\n" |
| "The environment may be a bedroom, living room, hallway, kitchen or any indoor space.\n\n" |
| "Task: Output exactly ONE short sentence.\n" |
| "Rules:\n" |
| "1. The sentence MUST start with one of these words: smell, activity, mood, doing\n" |
| "2. The sentence MUST NOT exceed 100 characters.\n" |
| "3. Do NOT output examples.\n" |
| "4. Do NOT output lists.\n" |
| "5. Do NOT explain anything.\n" |
| "6. Output exactly ONE short sentence.\n\n" |
| "Now output the sentence:\n" |
| ) |
|
|
| out = llm(prompt, max_tokens=160) |
| return out["choices"][0]["text"] |
|
|
| app.run(host="0.0.0.0", port=8000) |
|
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