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README.md

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----
-title: ClimaAI
-emoji: 🐨
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Climate reasoning demo using live weather + K2-Think model (
----
+# ClimaMind — K2-Think + Live Climate Data (Gradio on Hugging Face Spaces)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Setup
+1) Create a new Space → SDK = **Gradio**.
+2) Upload `app.py` and `requirements.txt` (this README is optional).
+3) In **Settings → Variables / secrets**, set:
+   - `PROVIDER` = `hf_model` (recommended) or `local` or `stub`
+   - `MODEL_ID` = `MBZUAI-IFM/K2-Think-SFT` (default) or `LLM360/K2-Think`
+   - `HF_TOKEN` = your HF token (Read + Inference)
+4) If choosing `local`, switch the Space hardware to **GPU**.
+
+## Notes
+- Uses Open-Meteo + OpenAQ (keyless).
+- If model returns non-JSON, you’ll see a friendly fallback.
+- If rate-limited, temporarily set `PROVIDER=stub` for the demo.

app.py

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+# app.py — ClimaMind on Hugging Face Spaces (Gradio)
+import os, time, json, random
+import requests
+import gradio as gr
+
+PROVIDER = os.getenv("PROVIDER", "hf_model").strip()
+MODEL_ID = os.getenv("MODEL_ID", "MBZUAI-IFM/K2-Think-SFT").strip()
+HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
+
+def _get(url, params=None, headers=None, timeout=12, retries=2, backoff=1.6):
+    for i in range(retries + 1):
+        try:
+            r = requests.get(url, params=params, headers=headers, timeout=timeout)
+            r.raise_for_status()
+            return r
+        except Exception:
+            if i == retries:
+                raise
+            time.sleep((backoff ** i) + random.random() * 0.25)
+
+def geocode_city(city:str):
+    r = _get("https://nominatim.openstreetmap.org/search",
+             params={"q": city, "format": "json", "limit": 1},
+             headers={"User-Agent": "climamind-space"})
+    j = r.json()
+    if not j:
+        raise RuntimeError("City not found")
+    return {"lat": float(j[0]["lat"]), "lon": float(j[0]["lon"]), "name": j[0]["display_name"]}
+
+def fetch_open_meteo(lat, lon):
+    r = _get("https://api.open-meteo.com/v1/forecast", params={
+        "latitude": lat, "longitude": lon,
+        "current": "temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation,uv_index",
+        "hourly": "temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation_probability,uv_index",
+        "timezone": "auto"
+    })
+    return r.json()
+
+def fetch_openaq_pm25(lat, lon):
+    r = _get("https://api.openaq.org/v3/latest",
+             params={"coordinates": f"{lat},{lon}", "radius": 10000, "limit": 1, "parameter": "pm25"},
+             headers={"User-Agent": "climamind-space"})
+    j = r.json()
+    pm25 = None
+    if j.get("results"):
+        ms = j["results"][0].get("measurements", [])
+        for m in ms:
+            if m.get("parameter") == "pm25":
+                pm25 = m.get("value")
+                break
+    return pm25
+
+def fetch_factors(lat, lon):
+    wx = fetch_open_meteo(lat, lon)
+    cur = wx.get("current", {})
+    factors = {
+        "temp_c": cur.get("temperature_2m"),
+        "rh": cur.get("relative_humidity_2m"),
+        "wind_kmh": cur.get("wind_speed_10m"),
+        "precip_mm": cur.get("precipitation"),
+        "uv": cur.get("uv_index"),
+        "pm25": fetch_openaq_pm25(lat, lon)
+    }
+    return {"factors": factors, "raw": wx}
+
+def drying_index(temp_c, rh, wind_kmh, cloud_frac=None):
+    base = (temp_c or 0) * 1.2 + (wind_kmh or 0) * 0.8 - (rh or 0) * 0.9
+    if cloud_frac is not None:
+        base -= 20 * cloud_frac
+    return max(0, min(100, round(base)))
+
+def heat_stress_index(temp_c, rh, wind_kmh):
+    hs = (temp_c or 0) * 1.1 + (rh or 0) * 0.3 - (wind_kmh or 0) * 0.2
+    return max(0, min(100, round(hs)))
+
+PROMPT = """You are ClimaMind, a climate reasoning assistant. Use ONLY the observations provided and return STRICT JSON.
+
+Location: {loc} (lat={lat}, lon={lon}), local time: {t_local}
+Observations: temp={temp_c}°C, rh={rh}%, wind={wind_kmh} km/h, precip={precip_mm} mm, uv={uv}, pm25={pm25}
+Derived: drying_index={d_idx}, heat_stress_index={hs_idx}
+
+Task: Answer the user’s query: "{query}" for the next 24 hours.
+Steps:
+ 1) Identify the relevant factors.
+ 2) Reason causally (2–3 steps).
+ 3) Give a concise recommendation with time window(s) and a confidence.
+ 4) Output a short WHY-TRACE (3 bullets).
+Return JSON ONLY:
+{{
+ "answer": "...",
+ "why_trace": ["...", "...", "..."],
+ "risk_badge": "Low"|"Moderate"|"High"
+}}"""
+
+def call_stub(_prompt:str)->str:
+    return json.dumps({
+        "answer": "Based on 32°C, 50% RH and 12 km/h wind, cotton dries in ~2–3h (faster after 2pm).",
+        "why_trace": [
+            "Higher temperature and wind increase evaporation rate",
+            "Moderate humidity slightly slows drying",
+            "Lower afternoon cloud cover speeds it up"
+        ],
+        "risk_badge": "Low"
+    })
+
+def call_hf_model(prompt:str)->str:
+    from huggingface_hub import InferenceClient
+    client = InferenceClient(model=MODEL_ID, token=(HF_TOKEN or None))
+    out = client.text_generation(
+        prompt,
+        max_new_tokens=200,
+        temperature=0.1,
+        repetition_penalty=1.05,
+        do_sample=False,
+    )
+    return str(out)
+
+_local_loaded = False
+def _ensure_local_loaded():
+    # Optional local load — requires GPU Space
+    global _local_loaded, tokenizer, model
+    if _local_loaded:
+        return
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+    import torch
+    bnb_cfg = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+        device_map="auto",
+        quantization_config=bnb_cfg,
+        low_cpu_mem_usage=True,
+    )
+    _local_loaded = True
+
+def call_local(prompt:str)->str:
+    _ensure_local_loaded()
+    import torch
+    if hasattr(tokenizer, "apply_chat_template"):
+        messages = [{"role":"user","content":prompt}]
+        inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to(model.device)
+    else:
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.1,
+            do_sample=False,
+            repetition_penalty=1.05,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    return tokenizer.decode(out[0], skip_special_tokens=True)
+
+def reason_answer(loc, coords, factors, query):
+    d_idx = drying_index(factors.get("temp_c"), factors.get("rh"), factors.get("wind_kmh"))
+    hs_idx = heat_stress_index(factors.get("temp_c"), factors.get("rh"), factors.get("wind_kmh"))
+    t_local = time.strftime("%Y-%m-%d %H:%M")
+    prompt = PROMPT.format(
+        loc=loc, lat=coords["lat"], lon=coords["lon"], t_local=t_local,
+        temp_c=factors.get("temp_c"), rh=factors.get("rh"), wind_kmh=factors.get("wind_kmh"),
+        precip_mm=factors.get("precip_mm"), uv=factors.get("uv"), pm25=factors.get("pm25"),
+        d_idx=d_idx, hs_idx=hs_idx, query=query
+    )
+
+    if PROVIDER == "hf_model":
+        raw = call_hf_model(prompt)
+    elif PROVIDER == "local":
+        raw = call_local(prompt)
+    else:
+        raw = call_stub(prompt)
+
+    start, end = raw.find("{"), raw.rfind("}")
+    if start == -1 or end == -1:
+        return {
+            "answer": "The reasoning service returned non-JSON text. Please try again.",
+            "why_trace": ["Response formatting issue", "Low temperature helps", "Retry the query"],
+            "risk_badge": "Low"
+        }
+    try:
+        return json.loads(raw[start:end+1])
+    except Exception:
+        return {
+            "answer": "Failed to parse JSON from model output.",
+            "why_trace": ["JSON parsing error", "Reduce tokens/temperature", "Retry once"],
+            "risk_badge": "Low"
+        }
+
+def app(city, question):
+    geo = geocode_city(city)
+    data = fetch_factors(geo["lat"], geo["lon"])
+    ans = reason_answer(
+        geo["name"], {"lat": geo["lat"], "lon": geo["lon"]},
+        data["factors"], question
+    )
+    fx = ", ".join([f"{k}={v}" for k, v in data["factors"].items()])
+    why_list = ans.get("why_trace") or []
+    why = "\n• " + "\n• ".join(why_list) if why_list else "\n• (no trace returned)"
+    md = (
+        f"**Answer:** {ans.get('answer','(no answer)')}\n\n"
+        f"**Why-trace:**{why}\n\n"
+        f"**Risk:** {ans.get('risk_badge','N/A')}\n\n"
+        f"**Factors:** {fx}"
+    )
+    return md
+
+demo = gr.Interface(
+    fn=app,
+    inputs=[
+        gr.Textbox(label="City", value="New Delhi"),
+        gr.Dropdown(
+            choices=[
+                "If I wash clothes now, when will they dry?",
+                "Should I water my plants today or wait?",
+                "What is the heat/wildfire risk today? Explain briefly."
+            ],
+            label="Question",
+            value="If I wash clothes now, when will they dry?"
+        )
+    ],
+    outputs=gr.Markdown(label="ClimaMind"),
+    title="ClimaMind — K2-Think + Live Climate Data",
+    description="Provider = hf_model (Inference API) | local (GPU Space) | stub (offline). Configure env in Space settings.",
+    allow_flagging="never"
+)
+demo.queue(concurrency_count=2, max_size=8)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.44.0
+requests>=2.31.0
+huggingface_hub>=0.23.0
+transformers>=4.43.0
+accelerate>=0.31.0
+bitsandbytes>=0.43.0
+sentencepiece>=0.2.0