Upload 3 files

app.py

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+
+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from gtts import gTTS
+import os
+import random
+import requests
+
+# Load model
+model = load_model("emotion_model.h5")
+emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+quotes = {
+    'Happy': ["Keep shining!", "You look great!"],
+    'Sad': ["Better days are coming.", "Stay strong."],
+    'Angry': ["Take a deep breath.", "Calm down, champ."],
+    'Surprise': ["Wow! Didn't expect that."],
+    'Fear': ["You're safe.", "Be brave!"],
+    'Disgust': ["Let it go."],
+    'Neutral': ["Stay focused."]
+}
+
+def get_weather(city='Islamabad'):
+    api_key = "your_openweather_api_key"
+    url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
+    try:
+        data = requests.get(url).json()
+        return f"{data['main']['temp']}°C, {data['weather'][0]['description']}"
+    except:
+        return "Weather unavailable"
+
+def detect_emotion(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    resized = cv2.resize(gray, (48, 48)).reshape(1, 48, 48, 1) / 255.0
+    pred = model.predict(resized)
+    emotion = emotion_labels[np.argmax(pred)]
+    quote = random.choice(quotes[emotion])
+    weather = get_weather()
+    return f"Emotion: {emotion}\nQuote: {quote}\nWeather: {weather}"
+
+interface = gr.Interface(
+    fn=detect_emotion,
+    inputs=gr.Image(type="numpy", label="Upload Face Image"),
+    outputs="text",
+    title="Smart Mirror Emotion Detector"
+)
+
+interface.launch()

emotion_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75ab46e587da3fc851be06b0c39604759b9e778b8bb719905d39feb11a9bec3d
+size 10111040

requirements.txt

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+
+gradio
+tensorflow
+opencv-python
+gTTS
+matplotlib
+requests