Delete app.py

app.py

@@ -1,121 +0,0 @@
-import streamlit as st
-from PIL import Image
-import requests
-from io import BytesIO
-from transformers import (
-    ViTFeatureExtractor, 
-    ViTForImageClassification, 
-    pipeline,
-    AutoFeatureExtractor, 
-    AutoModelForObjectDetection,
-    CLIPTokenizerFast,
-    CLIPTextModel
-)
-import torch
-from torchvision.transforms import functional as F
-import emoji
-
-# Load models
-@st.cache_resource
-def load_models():
-    age_model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
-    age_transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
-    
-    gender_model = ViTForImageClassification.from_pretrained('rizvandwiki/gender-classification-2')
-    gender_transforms = ViTFeatureExtractor.from_pretrained('rizvandwiki/gender-classification-2')
-    
-    emotion_model = ViTForImageClassification.from_pretrained('dima806/facial_emotions_image_detection')
-    emotion_transforms = ViTFeatureExtractor.from_pretrained('dima806/facial_emotions_image_detection')
-    
-    object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
-    
-    action_model = ViTForImageClassification.from_pretrained('rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224')
-    action_transforms = ViTFeatureExtractor.from_pretrained('rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224')
-    
-    prompt_generator = pipeline("text2text-generation", model="succinctly/text2image-prompt-generator")
-    
-    clip_tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32")
-    clip_model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
-    
-    return (age_model, age_transforms, gender_model, gender_transforms, 
-            emotion_model, emotion_transforms, object_detector, 
-            action_model, action_transforms, prompt_generator, 
-            clip_tokenizer, clip_model)
-
-models = load_models()
-(age_model, age_transforms, gender_model, gender_transforms, 
- emotion_model, emotion_transforms, object_detector, 
- action_model, action_transforms, prompt_generator, 
- clip_tokenizer, clip_model) = models
-
-def predict(image, model, transforms):
-    inputs = transforms(image, return_tensors='pt')
-    output = model(**inputs)
-    proba = output.logits.softmax(1)
-    return proba.argmax(1).item()
-
-def detect_attributes(image):
-    age = predict(image, age_model, age_transforms)
-    gender = predict(image, gender_model, gender_transforms)
-    emotion = predict(image, emotion_model, emotion_transforms)
-    action = predict(image, action_model, action_transforms)
-    
-    objects = object_detector(image)
-    
-    return {
-        'age': age_model.config.id2label[age],
-        'gender': gender_model.config.id2label[gender],
-        'emotion': emotion_model.config.id2label[emotion],
-        'action': action_model.config.id2label[action],
-        'objects': [obj['label'] for obj in objects]
-    }
-
-def generate_prompt(attributes):
-    prompt = f"A {attributes['age']} {attributes['gender']} person feeling {attributes['emotion']} "
-    prompt += f"while {attributes['action']}. "
-    if attributes['objects']:
-        prompt += f"Surrounded by {', '.join(attributes['objects'])}. "
-    return prompt
-
-def generate_emoji(prompt):
-    inputs = clip_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
-    outputs = clip_model(**inputs)
-    embedding = outputs.last_hidden_state.mean(dim=1)
-    
-    # Simple emoji mapping based on embedding features
-    if embedding[0, 0] > 0:
-        return emoji.emojize(":grinning_face:")
-    elif embedding[0, 1] > 0:
-        return emoji.emojize(":smiling_face_with_heart-eyes:")
-    elif embedding[0, 2] > 0:
-        return emoji.emojize(":face_with_tears_of_joy:")
-    elif embedding[0, 3] > 0:
-        return emoji.emojize(":thinking_face:")
-    else:
-        return emoji.emojize(":neutral_face:")
-
-st.title("Image Attribute Detection and Emoji Generation")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image', use_column_width=True)
-    
-    if st.button('Analyze and Generate Emoji'):
-        with st.spinner('Detecting attributes...'):
-            attributes = detect_attributes(image)
-        
-        st.write("Detected Attributes:")
-        for key, value in attributes.items():
-            st.write(f"{key.capitalize()}: {value}")
-        
-        with st.spinner('Generating prompt...'):
-            prompt = generate_prompt(attributes)
-        st.write("Generated Prompt:")
-        st.write(prompt)
-        
-        with st.spinner('Generating emoji...'):
-            emoji_result = generate_emoji(prompt)
-        st.write("Generated Emoji:")
-        st.write(emoji_result)