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README.md

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+---
+title: AsianMOM
+emoji: 💢
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 4.31.2
+app_file: app.py
+pinned: true
+---
+
+# AsianMOM 💢
+
+**AsianMOM** is a fun, interactive Gradio Space that uses your webcam to observe what you're doing and then roasts you like a stereotypical Asian mom—complete with high expectations, cousin comparisons, and slipper threats!
+
+## 🚀 Features
+- **Live Webcam Feed**: Observes your actions in real time.
+- **Vision Model**: Describes what it sees using BLIP image captioning.
+- **Roast Generation**: Uses Meta's Llama-3.2-1B-Instruct to generate witty, culturally-inspired "mom roasts".
+- **Text-to-Speech**: Delivers the roast in a mature, motherly voice using Parler-TTS.
+- **Fully Automated**: No button presses needed—just let AsianMOM do her thing!
+
+## 🛠️ How It Works
+1. **Webcam Capture**: The app streams your webcam feed.
+2. **Image Captioning**: BLIP model generates a description of what you're doing.
+3. **Roast Generation**: Llama-3.2-1B-Instruct crafts a humorous, mom-style roast based on the caption.
+4. **Voice Output**: Parler-TTS reads the roast aloud in a fitting voice.
+
+## 📦 Setup & Usage
+1. **Clone or Fork this Space**
+2. Ensure your hardware supports GPU (T4 or better recommended)
+3. All dependencies are managed via `requirements.txt`
+4. Launch the Space and allow webcam access
+5. Enjoy being roasted by AsianMOM!
+
+## 🧩 Models Used
+- [BLIP Image Captioning](https://huggingface.co/Salesforce/blip-image-captioning-base)
+- [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+- [Parler-TTS Mini Expresso](https://huggingface.co/parler-tts/parler-tts-mini-expresso)
+
+## 🙏 Credits
+- Inspired by classic Asian mom humor and memes
+- Built with [Gradio](https://gradio.app/)
+- Powered by Hugging Face models
+
+## ⚠️ Disclaimer
+This app is for entertainment purposes only. Stereotypes are used in a lighthearted, humorous way—please use responsibly and respectfully.
+

app.py

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+import os
+import gradio as gr
+import torch
+import cv2
+from PIL import Image
+import numpy as np
+from transformers import pipeline, AutoProcessor, AutoModelForVision2Seq
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import time
+
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+# Set environment variables
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+def initialize_vision_model():
+    # Using BLIP for image captioning - lightweight but effective
+    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+    model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+    
+    return {
+        "processor": processor,
+        "model": model
+    }
+
+def analyze_image(image, vision_components):
+    processor = vision_components["processor"]
+    model = vision_components["model"]
+    
+    # Convert to RGB if needed
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    inputs = processor(image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=30)
+    
+    caption = processor.decode(outputs[0], skip_special_tokens=True)
+    return caption
+
+def initialize_llm():
+    model_id = "meta-llama/Llama-3.2-1B-Instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto"
+    )
+    
+    return {
+        "model": model,
+        "tokenizer": tokenizer
+    }
+
+def generate_roast(caption, llm_components):
+    model = llm_components["model"]
+    tokenizer = llm_components["tokenizer"]
+    
+    prompt = f"""[INST] You are AsianMOM, a stereotypical Asian mother who always has high expectations. \nYou just observed your child doing this: \"{caption}\"\n    \nRespond with a short, humorous roast (maximum 2-3 sentences) in the style of a stereotypical Asian mother. \nInclude at least one of these elements:\n- Comparison to more successful relatives/cousins\n- High expectations about academic success\n- Mild threats about using slippers\n- Questioning life choices\n- Asking when they'll get married or have kids\n- Commenting on appearance\n- Saying \"back in my day\" and describing hardship\n\nBe funny but not hurtful. Keep it brief. [/INST]"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=300,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract just the response part, not the prompt
+    response = response.split("[/INST]")[1].strip()
+    
+    return response
+
+def initialize_tts_model():
+    tts_pipeline = pipeline(
+        "text-to-speech", 
+        model="parler-tts/parler-tts-mini-expresso"
+    )
+    return tts_pipeline
+
+def text_to_speech(text, tts_pipeline):
+    # Additional prompt to guide the voice style
+    styled_text = f"[[voice:female_mature]] [[speed:0.9]] [[precision:0.8]] {text}"
+    
+    speech = tts_pipeline(styled_text)
+    return (speech["sampling_rate"], speech["audio"])
+
+def process_frame(image, vision_components, llm_components, tts_pipeline):
+    # Step 1: Analyze what's in the image
+    caption = analyze_image(image, vision_components)
+    
+    # Step 2: Generate roast based on the caption
+    roast = generate_roast(caption, llm_components)
+    
+    # Step 3: Convert roast to speech
+    audio = text_to_speech(roast, tts_pipeline)
+    
+    return caption, roast, audio
+
+def setup_processing_chain(video_feed, analysis_output, roast_output, audio_output):
+    # Initialize all models
+    vision_components = initialize_vision_model()
+    llm_components = initialize_llm()
+    tts_pipeline = initialize_tts_model()
+    
+    last_process_time = time.time() - 10  # Initialize with an offset
+    processing_interval = 5  # Process every 5 seconds
+    
+    def process_webcam(image):
+        nonlocal last_process_time
+        
+        current_time = time.time()
+        if current_time - last_process_time >= processing_interval and image is not None:
+            last_process_time = current_time
+            
+            caption, roast, audio = process_frame(
+                image, 
+                vision_components, 
+                llm_components, 
+                tts_pipeline
+            )
+            
+            return image, caption, roast, audio
+        
+        # Return None for outputs that shouldn't update
+        return image, None, None, None
+    
+    video_feed.change(
+        process_webcam,
+        inputs=[video_feed],
+        outputs=[video_feed, analysis_output, roast_output, audio_output]
+    )
+
+def create_app():
+    with gr.Blocks(theme=gr.themes.Soft()) as app:
+        gr.Markdown("# AsianMOM: Asian Mother Observer & Mocker")
+        gr.Markdown("### Camera captures what you're doing and your Asian mom responds appropriately")
+        
+        with gr.Row():
+            with gr.Column():
+                video_feed = gr.Image(source="webcam", streaming=True, label="Camera Feed")
+            
+            with gr.Column():
+                analysis_output = gr.Textbox(label="What AsianMOM Sees", lines=2)
+                roast_output = gr.Textbox(label="AsianMOM's Thoughts", lines=4)
+                audio_output = gr.Audio(label="AsianMOM Says", autoplay=True)
+        
+        # Setup the processing chain
+        setup_processing_chain(video_feed, analysis_output, roast_output, audio_output)
+                
+    return app
+
+if __name__ == "__main__":
+    app = create_app()
+    app.launch() 

requirements.txt

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+gradio==4.26.0
+torch==2.1.0
+torchvision
+transformers==4.36.2
+pillow
+numpy
+accelerate
+git+https://github.com/huggingface/diffusers
+opencv-python