app.py

import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import gradio as gr
from transformers import pipeline

# Load emotion classes
classes = ['Angry', 'Disgust', 'Fear', 'Sad', 'Surprise', 'Neutral', 'Happy']

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Define the actual architecture used for training
class EmotionModel(nn.Module):
    def __init__(self):
        super(EmotionModel, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(1, 32, 3, padding=1),   # Match 32 out_channels
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, 3, padding=1),  # Match 64 out_channels
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Flatten(),
            nn.Linear(64 * 12 * 12, 128),     # 64 filters * 12x12 features = 9216
            nn.ReLU(),
            nn.Linear(128, 7)
        )

    def forward(self, x):
        return self.model(x)

# Load the model
model = EmotionModel().to(device)
model.load_state_dict(torch.load("emotion_model.pth", map_location=device))
model.eval()

# Transformation
transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((48, 48)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# NLP pipeline
gen = pipeline("text-generation", model="distilgpt2")

# Templates with emojis included now
templates = {
    "Friendly": {
        "Happy": "You seem cheerful 😊! ",
        "Sad": "You look down 😢. Here's something to cheer you up... ",
        "Angry": "Feeling heated 😠? Let's shift the vibe... ",
        "Fear": "A little nervous 😨? No worries, try this... ",
        "Disgust": "Something bugging you 🤢?",
        "Surprise": "Whoa! 😲 That was unexpected?",
        "Neutral": "Keeping it chill 😐?"
    },
    "Professional": {
        "Happy": "You look ready to go 😊.",
        "Sad": "You seem thoughtful 😢.",
        "Angry": "Tense vibes 😠.",
        "Fear": "Unsure? 😨",
        "Disgust": "Hey! Reset your focus 🤢",
        "Surprise": "Unexpected moment 😲?",
        "Neutral": "Yo! Are you redy for this 😐?"
    },
    "Funny": {
        "Happy": "You’re all smiles 😊!",
        "Sad": "You need a laugh 😢.",
        "Angry": "Cool it down 😠",
        "Fear": "Let’s laugh the fear away 😨: ",
        "Disgust": "Weird day 🤢?",
        "Surprise": "Surprise! 😲",
        "Neutral": "Let’s wake things up 😐 with a joke. "
    }
}

# Prediction logic
# Emotion to Emoji mapping
emotion_emojis = {
    "Angry": "😠",
    "Disgust": "🤢",
    "Fear": "😨",
    "Happy": "😄",
    "Sad": "😢",
    "Surprise": "😲",
    "Neutral": "😐"
}

def predict_emotion_and_icebreaker(image, tone):
    image = Image.fromarray(image).convert("RGB")
    image = transform(image).unsqueeze(0).to(device)

    with torch.no_grad():
        output = model(image)
        pred = output.argmax(dim=1).item()

    emotion = classes[pred]

    # Emojis per emotion
    emotion_emojis = {
        "Happy": "😊", "Sad": "😢", "Angry": "😠",
        "Fear": "😨", "Disgust": "🤢", "Surprise": "😲", "Neutral": "😐"
    }

    emoji = emotion_emojis[emotion]
    tone_prefix = templates[tone][emotion]

    # Prompt for generation
    prompt = f"{tone_prefix} {emoji} Try saying:"

    response = gen(prompt, max_length=40, num_return_sequences=1, do_sample=True, temperature=0.7, top_k=50)[0]['generated_text']
    clean_response = response.replace(prompt, "").strip().split("\n")[0]

    return f"🧠 Emotion Detected: {emotion} {emoji}\n💬 Icebreaker ({tone}):\n{clean_response}"

    # Generate icebreaker with better sampling
    generated = gen(
        prompt,
        max_length=60,
        num_return_sequences=1,
        do_sample=True,
        temperature=0.9,
        top_p=0.95
    )[0]['generated_text']

    # Remove the prompt part from the response
    response = generated[len(prompt):].strip()

    # Fallback if no content was generated
    if not response:
        response = "Here's a fun question to get started: What's your hidden talent?"

    return f"🧠 Emotion Detected: {emotion}\n💬 Icebreaker ({tone}):\n{response}"
# Interface
webcam_input = gr.Image(type="numpy", label="Upload or Take a Photo")
tone_dropdown = gr.Dropdown(choices=["Friendly", "Professional", "Funny"], value="Friendly", label="Tone")

demo = gr.Interface(
    fn=predict_emotion_and_icebreaker,
    inputs=[webcam_input, tone_dropdown],
    outputs="text",
    title="Emotion + Icebreaker Generator",
    description="Upload or capture a face photo. AI will predict the emotion and generate a tone-specific conversation starter."
)

demo.launch()