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app.py

@@ -1,73 +1,132 @@
 
 import gradio as gr
-from datetime import datetime
+import cv2
+import numpy as np
+from PIL import Image
+import mediapipe as mp
+import torch
+from torchvision import transforms
+from torchvision.models import resnet18
+import torch.nn.functional as F
+import openai
+import os
 
-# PAGE 1: Daily Plan Generator
-def generate_daily_plan(wake_time, swim_duration, gym_today, sleep_hours, goal):
+# Load OpenAI API Key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# --------- Daily Plan Logic ---------
+def generate_smart_plan(wake_time, swim_time, swim_duration, gym_today, sleep_hours, goal):
     plan = []
     if sleep_hours < 7:
-        plan.append("⚠️ Less than 7 hours of sleep — consider adjusting recovery.")
-    plan.append(f"🌅 Wake at {wake_time}")
-    plan.append(f"🏊‍♂️ Swim: {swim_duration} min — Focus: {goal}")
+        plan.append("⚠️ Less than 7 hours of sleep — prioritize recovery or a nap.")
+    plan.append(f"🌅 Wake-up time: {wake_time}")
+    plan.append(f"🏊 Swim: {swim_time} for {swim_duration} minutes — Focus: {goal.capitalize()}")
     if gym_today:
-        plan.append("🏋️‍♂️ Gym: Yes — tailor to today's goal")
+        if goal == "speed":
+            plan.append("🏋️ Gym Plan: Explosive lifts — med ball slams, box jumps.")
+        elif goal == "endurance":
+            plan.append("🏋️ Gym Plan: Circuits with light weights and high reps.")
+        else:
+            plan.append("🏋️ Gym Plan: Core, mobility, and injury prevention.")
     else:
-        plan.append("💪 Gym: Rest or mobility work")
-    plan.append("🍽️ Nutrition: Eat protein + carbs within 30 min post-swim")
-    plan.append("🌙 Sleep target: 8+ hours")
+        plan.append("💪 No gym — light stretching and mobility recommended.")
+    plan.append("🍽️ Eat protein + carbs within 30 minutes post-swim.")
+    plan.append("🌙 Target 8–9 hours of sleep tonight.")
     return "\n".join(plan)
 
-daily_plan = gr.Interface(
-    fn=generate_daily_plan,
-    inputs=[
-        gr.Textbox(label="Wake Time (e.g. 5:30 AM)"),
-        gr.Slider(label="Swim Duration (min)", minimum=0, maximum=180),
-        gr.Checkbox(label="Gym Today?"),
-        gr.Slider(label="Sleep Last Night (hrs)", minimum=4, maximum=10, step=0.5),
-        gr.Dropdown(["speed", "endurance", "technique", "recovery"], label="Today's Goal")
-    ],
-    outputs=gr.Textbox(label="AI Daily Plan"),
-    title="🧠 SwimCoach+ AI — Daily Plan"
-)
+# --------- Food Macro Estimator + GPT ---------
+food_classes = ["pizza", "salad", "burger", "sushi", "spaghetti", "steak", "pancakes"]
+food_macros = {
+    "pizza": {"cal": 285, "protein": 12, "carbs": 36, "fat": 10},
+    "salad": {"cal": 150, "protein": 5, "carbs": 10, "fat": 7},
+    "burger": {"cal": 500, "protein": 25, "carbs": 40, "fat": 30},
+    "sushi": {"cal": 200, "protein": 10, "carbs": 30, "fat": 5},
+    "spaghetti": {"cal": 350, "protein": 12, "carbs": 60, "fat": 8},
+    "steak": {"cal": 400, "protein": 40, "carbs": 0, "fat": 25},
+    "pancakes": {"cal": 350, "protein": 6, "carbs": 45, "fat": 12}
+}
 
-# PAGE 2: Nutrition and Sleep Tracker (placeholder)
-def diet_sleep_tracker(sleep, image=None, food_name=""):
-    feedback = f"🛌 Sleep: {sleep} hrs — "
-    feedback += "Great recovery!" if sleep >= 8 else "Try to get more rest tonight."
-    if image:
-        feedback += "\n🍽️ Food: [AI Image Analysis Coming Soon]"
-    elif food_name:
-        feedback += f"\n🍽️ Food entered: {food_name} (Calorie/macros estimation coming soon)"
-    else:
-        feedback += "\n🍽️ No food data provided."
-    return feedback
+model = resnet18(pretrained=True)
+model.eval()
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
 
-diet_sleep = gr.Interface(
-    fn=diet_sleep_tracker,
-    inputs=[
-        gr.Slider(label="Sleep Hours", minimum=4, maximum=10, step=0.5),
-        gr.Image(type="filepath", label="Upload Food Photo (optional)"),
-        gr.Textbox(label="Or Type Food Name (e.g. '2 eggs and toast')")
-    ],
-    outputs=gr.Textbox(label="Nutrition & Sleep Feedback"),
-    title="🍽️ Nutrition + 💤 Sleep Tracker"
-)
+def predict_macros_with_explanation(img):
+    image = Image.fromarray(img)
+    x = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        logits = model(x)
+    probs = torch.nn.functional.softmax(logits, dim=1).squeeze().numpy()
+    pred_idx = np.argmax(probs)
+    food = food_classes[pred_idx % len(food_classes)]
+    macros = food_macros.get(food, {"cal": 0, "protein": 0, "carbs": 0, "fat": 0})
 
-# PAGE 3: Swim Technique Analyzer (placeholder)
-def analyze_swim(video, stroke_type):
-    return f"Received video for stroke: {stroke_type}. AI analysis coming soon."
+    base = f"🍽️ Predicted: {food}\nCalories: {macros['cal']} kcal\nProtein: {macros['protein']}g\nCarbs: {macros['carbs']}g\nFat: {macros['fat']}g"
 
-technique_analyzer = gr.Interface(
-    fn=analyze_swim,
-    inputs=[
-        gr.Video(label="Upload Swim Video"),
-        gr.Dropdown(["freestyle", "backstroke", "breaststroke", "butterfly"], label="Stroke Type")
-    ],
-    outputs="text",
-    title="🏊 AI Swim Technique Analyzer"
-)
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "You are a helpful sports nutritionist."},
+                {"role": "user", "content": f"This food is {food} with macros: {macros}. Give a short explanation on how this helps a swimmer recover after training."}
+            ]
+        )
+        gpt_reply = response['choices'][0]['message']['content']
+    except Exception as e:
+        gpt_reply = f"⚠️ GPT error: {str(e)}"
+
+    return base + "\n\n💡 GPT Insight:\n" + gpt_reply
+
+# --------- Swim Technique Analyzer + GPT ---------
+mp_pose = mp.solutions.pose
+
+def analyze_swim_video_with_gpt(video_path):
+    cap = cv2.VideoCapture(video_path)
+    pose = mp_pose.Pose()
+    issues_detected = set()
+    frame_count = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret or frame_count > 60:
+            break
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(frame_rgb)
+        if results.pose_landmarks:
+            l_shoulder = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER]
+            r_shoulder = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER]
+            l_hand = results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST]
+            r_hand = results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST]
+            if abs(l_hand.y - l_shoulder.y) < 0.1:
+                issues_detected.add("Left hand enters too flat")
+            if abs(r_hand.y - r_shoulder.y) < 0.1:
+                issues_detected.add("Right hand enters too flat")
+        frame_count += 1
 
-# PAGE 4: Profile
+    cap.release()
+
+    if not issues_detected:
+        return "✅ Stroke technique looks great in the first 60 frames!"
+
+    tips = "\n".join(f"- {issue}" for issue in issues_detected)
+
+    try:
+        gpt_response = openai.ChatCompletion.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "You are a professional swim coach."},
+                {"role": "user", "content": f"The following stroke issues were detected: {tips}. Explain their impact on performance and how to correct them."}
+            ]
+        )
+        gpt_advice = gpt_response['choices'][0]['message']['content']
+    except Exception as e:
+        gpt_advice = f"⚠️ GPT error: {str(e)}"
+
+    return "⚠️ Technique Tips:\n" + tips + "\n\n💡 GPT Advice:\n" + gpt_advice
+
+# --------- Profile ---------
 profile_data = {}
 
 def save_profile(name, age, gender, goal, diet_type, pb100free):
@@ -79,6 +138,35 @@ def save_profile(name, age, gender, goal, diet_type, pb100free):
     profile_data["pb100free"] = pb100free
     return f"✅ Profile saved for {name} (100 Free PB: {pb100free} sec)"
 
+# --------- Gradio Interfaces ---------
+daily_plan = gr.Interface(
+    fn=generate_smart_plan,
+    inputs=[
+        gr.Textbox(label="Wake-up Time (e.g. 5:30 AM)"),
+        gr.Textbox(label="Swim Time (e.g. 6:00 AM)"),
+        gr.Slider(label="Swim Duration (min)", minimum=0, maximum=180),
+        gr.Checkbox(label="Did you go to the gym today?"),
+        gr.Slider(label="Sleep Last Night (hrs)", minimum=4, maximum=10, step=0.5),
+        gr.Dropdown(["speed", "endurance", "technique", "recovery"], label="Today's Focus")
+    ],
+    outputs=gr.Textbox(label="AI Daily Plan"),
+    title="📅 Daily Plan Generator"
+)
+
+macro_analyzer = gr.Interface(
+    fn=predict_macros_with_explanation,
+    inputs=gr.Image(type="numpy", label="Upload Food Image"),
+    outputs="text",
+    title="🥗 Macro Estimator + GPT Insight"
+)
+
+swim_analyzer = gr.Interface(
+    fn=analyze_swim_video_with_gpt,
+    inputs=gr.Video(label="Upload Swim Video"),
+    outputs="text",
+    title="🏊 Swim Analyzer + GPT Advice"
+)
+
 profile = gr.Interface(
     fn=save_profile,
     inputs=[
@@ -93,8 +181,8 @@ profile = gr.Interface(
     title="👤 Swimmer Profile"
 )
 
-# MULTI-PAGE APP
+# Launch Tabbed App
 gr.TabbedInterface(
-    [daily_plan, diet_sleep, technique_analyzer, profile],
-    tab_names=["📅 Daily Plan", "🍴 Nutrition & Sleep", "📹 Technique", "👤 Profile"]
+    [daily_plan, macro_analyzer, swim_analyzer, profile],
+    tab_names=["📅 Daily Plan", "🥗 Macros + GPT", "🏊 Technique + GPT", "👤 Profile"]
 ).launch()

requirements.txt

@@ -1 +1,7 @@
 gradio
+torch
+torchvision
+opencv-python
+mediapipe
+pillow
+numpy