step 6

appvv2.py

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+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPProcessor, CLIPModel
+from datasets import load_dataset
+import torch
+import io
+
+app = FastAPI()
+
+# Load the pre-trained CLIP model and its tokenizer
+model_name = "openai/clip-vit-base-patch32"
+processor = CLIPProcessor.from_pretrained(model_name)
+model = CLIPModel.from_pretrained(model_name)
+
+# Load the fashion product images dataset from Hugging Face
+dataset = load_dataset("ashraq/fashion-product-images-small")
+deepfashion_database = dataset["train"]
+
+def preprocess_image(image):
+    pil_image = Image.fromarray(image.astype('uint8'), 'RGB')
+    preprocess = transforms.Compose([
+        transforms.Resize(224),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    return preprocess(pil_image).unsqueeze(0)
+
+def encode_text(text):
+    inputs = processor(text=[text], return_tensors="pt", padding=True, truncation=True)
+    return inputs
+
+def encode_image(image):
+    inputs = processor(images=[image], return_tensors="pt", padding=True, truncation=True)
+    return inputs
+
+def calculate_similarities(query_image, query_text):
+    query_image_features = model.get_image_features(query_image)
+    query_text_features = model.get_text_features(query_text)
+
+    similarities = []
+    for product in deepfashion_database:
+        product_image_features = torch.Tensor(product["image_features"])
+        product_text_features = torch.Tensor(product["text_features"])
+
+        image_similarity = torch.nn.CosineSimilarity(dim=-1)(query_image_features, product_image_features)
+        text_similarity = torch.nn.CosineSimilarity(dim=-1)(query_text_features, product_text_features)
+        
+        similarity_score = image_similarity * text_similarity
+        similarities.append(similarity_score)
+
+    return similarities
+
+def initial_query(image, text):
+    query_image = encode_image(image)
+    query_text = encode_text(text)
+
+    similarities = calculate_similarities(query_image, query_text)
+    sorted_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)
+    top_3_indices = sorted_indices[:3]
+
+    top_3_products = [deepfashion_database[i] for i in top_3_indices]
+    return top_3_products
+
+def send_message(txt, btn):
+    if btn is not None:
+        image = Image.open(btn)
+        image = preprocess_image(image)
+    else:
+        image = None
+    top_3_products = initial_query(image, txt)
+    output_html = generate_output_html(top_3_products)
+    chatbot.append_message("You", txt)
+    chatbot.append_message("AI", output_html)
+
+
+chatbot = gr.Chatbot([]).style(height=750)
+txt = gr.Textbox(placeholder="Enter text and press enter, or upload an image", show_label=False)
+btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
+
+gr.Interface(send_message, inputs=[txt, btn], outputs=chatbot).launch()
+@app.post("/initial_query/")
+async def api_initial_query(text: str, image: UploadFile = File(None)):
+    if image is not None:
+        image_content = await image.read()
+        image = Image.open(io.BytesIO(image_content))
+        image = preprocess_image(image)
+    else:
+        image = None
+    top_3_products = initial_query(image, text)
+    return {"top_3_products": top_3_products}