Upload app.py

app.py

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+
+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', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+# 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, 16, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2, 2),
+            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2, 2),
+            nn.Flatten(),
+            nn.Linear(32 * 12 * 12, 128),
+            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")
+
+# Icebreaker templates
+templates = {
+    "Friendly": {
+        "Happy": "Looks like you're in a great mood! 🌞 Here's a cheerful icebreaker: ",
+        "Sad": "Hey, if you're feeling down, maybe this will lift your spirits: ",
+        "Angry": "Let’s turn that frown around! Here's something light-hearted: ",
+        "Fear": "Everything okay? Here's a calming question to ease the mood: ",
+        "Disgust": "Here's a quirky icebreaker to distract from the ick: ",
+        "Surprise": "Caught off guard? Here's a fun fact to break the ice: ",
+        "Neutral": "Let's get the conversation started with this: "
+    },
+    "Professional": {
+        "Happy": "You seem upbeat! Here's a smart opener for professional settings: ",
+        "Sad": "Here's a thoughtful question to get engagement going: ",
+        "Angry": "Let's channel focus with this topic: ",
+        "Fear": "Start with a grounded tone: ",
+        "Disgust": "Try this conversation starter to refocus: ",
+        "Surprise": "Introduce with curiosity using this: ",
+        "Neutral": "Here's a balanced opener: "
+    },
+    "Funny": {
+        "Happy": "This joke might make your smile bigger: ",
+        "Sad": "Here’s something silly to flip the mood: ",
+        "Angry": "Try this pun to vent the steam: ",
+        "Fear": "Scared? Laugh it off with this: ",
+        "Disgust": "Try this gross-but-funny line: ",
+        "Surprise": "Unexpected? This might top that: ",
+        "Neutral": "Break the silence with this joke: "
+    }
+}
+
+# Prediction logic
+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]
+
+    prompt = templates[tone][emotion] + " Here's an idea to open the chat:"
+    response = gen(prompt, max_length=40, num_return_sequences=1)[0]['generated_text']
+
+    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()