Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from datetime import datetime, timedelta
+import os
+
+CSV_LOG = "baby_log.csv"
+
+# Load or initialize log
+if os.path.exists(CSV_LOG):
+    log_df = pd.read_csv(CSV_LOG)
+    log_df["datetime"] = pd.to_datetime(log_df["datetime"])
+else:
+    log_df = pd.DataFrame(columns=["datetime", "event"])
+
+def save_logs():
+    log_df.to_csv(CSV_LOG, index=False)
+
+def parse_time(hour, minute, ampm):
+    hour = int(hour)
+    minute = int(minute)
+    if ampm == "PM" and hour < 12:
+        hour += 12
+    if ampm == "AM" and hour == 12:
+        hour = 0
+    now = datetime.now()
+    return now.replace(hour=hour, minute=minute, second=0, microsecond=0)
+
+def get_next_feeding_time(last_time):
+    hour = last_time.hour
+    if 21 <= hour or hour < 10:
+        return last_time + timedelta(hours=4)
+    else:
+        return last_time + timedelta(hours=2)
+
+def summarize_today(df):
+    today = datetime.now().date()
+    df_today = df[df["datetime"].dt.date == today]
+    return f"🍼 Feedings today: {len(df_today)}"
+
+def avg_feed_gap(df):
+    df = df.sort_values("datetime")
+    df_today = df[df["datetime"].dt.date == datetime.now().date()]
+    times = df_today["datetime"].tolist()
+    if len(times) < 2:
+        return "⏱️ Avg Gap: N/A"
+    gaps = [(t2 - t1).total_seconds() for t1, t2 in zip(times[:-1], times[1:])]
+    avg_gap = sum(gaps) / len(gaps)
+    avg_td = timedelta(seconds=avg_gap)
+    return f"⏱️ Avg Gap: {str(avg_td).split('.')[0]}"
+
+def log_feeding(hour_str, minute_str, ampm):
+    global log_df
+    try:
+        hour = int(hour_str)
+        minute = int(minute_str)
+    except:
+        return "Invalid time input", pd.DataFrame(), "", ""
+    feed_time = parse_time(hour, minute, ampm)
+    new_entry = pd.DataFrame([{"datetime": feed_time, "event": "Feed"}])
+    log_df = pd.concat([log_df, new_entry], ignore_index=True)
+    save_logs()
+    summary = summarize_today(log_df)
+    gap = avg_feed_gap(log_df)
+    next_feed_time = get_next_feeding_time(feed_time)
+    return next_feed_time.strftime("%I:%M %p"), log_df.tail(50).reset_index(drop=True), summary, gap
+
+def check_next_feeding():
+    if log_df.empty:
+        return "No previous feeding logged."
+    last_time = log_df["datetime"].max()
+    next_time = get_next_feeding_time(last_time)
+    return f"Next feeding should be around: {next_time.strftime('%I:%M %p')}"
+
+def visualize_log():
+    return log_df.tail(50).reset_index(drop=True), summarize_today(log_df), avg_feed_gap(log_df)
+
+with gr.Blocks() as app:
+    gr.Markdown("## 👶 Smart Baby Feed Tracker")
+
+    with gr.Row():
+        hour_input = gr.Textbox(label="Hour (1-12)", value="10")
+        minute_input = gr.Textbox(label="Minute (0-59)", value="00")
+        ampm_dropdown = gr.Dropdown(["AM", "PM"], label="AM/PM", value="AM")
+        log_btn = gr.Button("✔️ Log Feeding")
+
+    next_feed_time = gr.Textbox(label="⏭️ Next Feeding Time")
+    feed_table = gr.Dataframe(label="Recent Feed Log")
+    today_summary = gr.Textbox(label="Today's Feed Count")
+    avg_gap = gr.Textbox(label="Average Feed Gap")
+    log_btn.click(log_feeding, inputs=[hour_input, minute_input, ampm_dropdown],
+                  outputs=[next_feed_time, feed_table, today_summary, avg_gap])
+
+    gr.Markdown("### 📋 View Log & Stats Anytime")
+    view_btn = gr.Button("📊 Visualize Log")
+    view_table = gr.Dataframe(label="Recent Feed Log")
+    view_summary = gr.Textbox(label="Today's Feed Count")
+    view_gap = gr.Textbox(label="Average Feed Gap")
+    view_btn.click(visualize_log, inputs=[], outputs=[view_table, view_summary, view_gap])
+
+    gr.Markdown("### 🔄 Check From Last Feeding")
+    check_btn = gr.Button("⏭️ Predict Next Feeding Time")
+    next_check = gr.Textbox(label="Predicted Time From Log")
+    check_btn.click(check_next_feeding, inputs=[], outputs=next_check)
+
+app.launch()