Update app.py

app.py

@@ -2,7 +2,7 @@ import io
 import requests
 import pandas as pd
 import matplotlib
-matplotlib.use("Agg")  # headless backend for Hugging Face
+matplotlib.use("Agg")  # headless backend for Hugging Face / servers
 import matplotlib.pyplot as plt
 import gradio as gr
 
@@ -11,20 +11,23 @@ FORECAST_URL = "https://api.open-meteo.com/v1/forecast"
 
 
 def geocode_city(city: str):
+    city = (city or "").strip()
+    if not city:
+        raise ValueError("Please enter a city name.")
     r = requests.get(GEOCODE_URL, params={"name": city, "count": 1, "language": "en"}, timeout=10)
     r.raise_for_status()
     data = r.json()
     if not data.get("results"):
         raise ValueError(f"City '{city}' not found.")
     res = data["results"][0]
-    return res["latitude"], res["longitude"], f"{res['name']}, {res.get('country','')}"
+    return float(res["latitude"]), float(res["longitude"]), f"{res['name']}, {res.get('country','')}"
 
 
 def fetch_forecast(lat, lon, days, mode, temp_unit, precip_unit):
     params = {
         "latitude": lat,
         "longitude": lon,
-        "forecast_days": days,
+        "forecast_days": int(days),
         "timezone": "auto",
         "temperature_unit": "fahrenheit" if temp_unit == "°F" else "celsius",
         "precipitation_unit": "inch" if precip_unit == "in" else "mm",
@@ -40,10 +43,18 @@ def fetch_forecast(lat, lon, days, mode, temp_unit, precip_unit):
 
 
 def make_plot(x, ys, labels, title, ylabel):
+    # ensure x is datetime-like for nice plotting
+    try:
+        x = pd.to_datetime(x)
+    except Exception:
+        # fall back to plain strings
+        x = list(map(str, x))
+
     plt.figure(figsize=(8, 3.5))
     for y, lbl in zip(ys, labels):
         plt.plot(x, y, label=lbl)
-    plt.legend()
+    if labels:
+        plt.legend()
     plt.title(title)
     plt.xlabel("Time")
     plt.ylabel(ylabel)
@@ -57,10 +68,17 @@ def make_plot(x, ys, labels, title, ylabel):
 
 def run(city, mode, days, temp_unit, precip_unit):
     try:
+        # validate small things
+        days = int(days)
+        if days < 1 or days > 14:
+            raise ValueError("Days must be between 1 and 14.")
+
         lat, lon, place = geocode_city(city)
         data = fetch_forecast(lat, lon, days, mode, temp_unit, precip_unit)
 
         if mode == "Daily":
+            if "daily" not in data:
+                raise ValueError("Daily data not available for this location.")
             d = data["daily"]
             df = pd.DataFrame({
                 "date": d["time"],
@@ -68,44 +86,18 @@ def run(city, mode, days, temp_unit, precip_unit):
                 "temp_min": d["temperature_2m_min"],
                 "precip": d["precipitation_sum"],
             })
+            # return types: (markdown, DataFrame, bytes)
             img = make_plot(df["date"], [df["temp_max"], df["temp_min"]],
                             ["Max", "Min"], f"Daily Temperatures — {place}", f"Temp ({temp_unit})")
-            md = f"### Daily forecast for **{place}**\nNext {days} day(s)"
+            md = f"### Daily forecast for **{place}**\nNext {days} day(s)\n\nUnits: Temperature **{temp_unit}**, Precipitation **{precip_unit}**"
             return md, df, img
 
         else:  # Hourly
+            if "hourly" not in data:
+                raise ValueError("Hourly data not available for this location.")
             h = data["hourly"]
+            # Ensure we don't slice beyond available length
+            max_hours = len(h.get("time", []))
+            requested_hours = min(days * 24, max_hours)
             df = pd.DataFrame({
-                "time": h["time"][:days*24],
-                "temperature": h["temperature_2m"][:days*24],
-                "humidity_%": h["relativehumidity_2m"][:days*24],
-                "precip": h["precipitation"][:days*24],
-            })
-            img = make_plot(df["time"], [df["temperature"]],
-                            ["Temperature"], f"Hourly Temperatures — {place}", f"Temp ({temp_unit})")
-            md = f"### Hourly forecast for **{place}**\nNext {days} day(s)"
-            return md, df, img
-
-    except Exception as e:
-        return f"**Error:** {e}", pd.DataFrame(), None
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🌦️ Weather Forecast (No API Key Needed)\nUsing **Open-Meteo** free API")
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            city = gr.Textbox(label="City", value="Karachi")
-            mode = gr.Radio(["Daily", "Hourly"], value="Daily", label="Forecast type")
-            days = gr.Slider(1, 14, value=3, step=1, label="Days")
-            temp_unit = gr.Radio(["°C", "°F"], value="°C", label="Temperature unit")
-            precip_unit = gr.Radio(["mm", "in"], value="mm", label="Precipitation unit")
-            btn = gr.Button("Get Forecast")
-
-        with gr.Column(scale=3):
-            md = gr.Markdown()
-            table = gr.Dataframe()
-            img = gr.Image()
-
-    btn.click(run, [city, mode, days, temp_unit, precip_unit], [md, table, img])
-
+                "time": h["time"][:requested_hours]_]()