Upload app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List
+from transformers import pipeline
+import torch
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Determine device (use GPU if available, otherwise CPU)
+device = 0 if torch.cuda.is_available() else -1
+
+# Initialize the NER pipeline
+ner_pipeline = pipeline(
+    "ner",
+    model="dbmdz/bert-large-cased-finetuned-conll03-english",
+    aggregation_strategy="simple",
+    device=device
+)
+
+# Initialize the QA pipeline
+qa_pipeline = pipeline(
+    "question-answering",
+    model="deepset/roberta-base-squad2",
+    device=device
+)
+
+# Allowed domains for filtering
+allowed_domains = [
+    "clothing", "fashion", "shopping", "accessories", "sustainability", "shoes", "hats", "shirts",
+    "dresses", "pants", "jeans", "skirts", "jackets", "coats", "t-shirts", "sweaters", "hoodies",
+    "activewear", "formal wear", "casual wear", "sportswear", "outerwear", "swimwear", "underwear",
+    "lingerie", "socks", "scarves", "gloves", "belts", "ties", "caps", "beanies", "boots", "sandals",
+    "heels", "sneakers", "materials", "cotton", "polyester", "wool", "silk", "leather", "denim",
+    "linen", "athleisure", "ethnic wear", "fashion trends", "custom clothing", "tailoring",
+    "sustainable materials", "recycled clothing", "fashion brands", "streetwear",
+    "footwear", "handbags", "jewelry", "watches", "eyewear", "cosmetics", "beauty products",
+    "personal care", "fragrances", "home decor", "lifestyle", "luxury goods", "vintage clothing",
+    "second-hand clothing", "upcycled fashion", "ethical fashion", "eco-friendly products",
+    "fashion technology", "textile innovation", "fashion marketing", "fashion retail"
+]
+
+# Context for the QA pipeline
+context_msg = (
+    "Hutter Products GmbH provides a wide array of services to help businesses create high-quality, sustainable products. "
+    "Their offerings include comprehensive product design, ensuring items are both visually appealing and functional, and product consulting, "
+    "which provides expert advice on features, materials, and design elements. They also offer sustainability consulting to integrate eco-friendly practices, "
+    "such as using recycled materials and Ocean Bound Plastic. Additionally, they manage customized production to ensure products meet the highest standards "
+    "and offer product animation services, creating realistic rendered images and animations to enhance online engagement. These services collectively enable "
+    "businesses to develop products that are sustainable, market-responsive, and aligned with their brand identity."
+)
+
+# Pydantic models for structured responses
+class Entity(BaseModel):
+    word: str
+    entity_group: str
+    score: float
+
+class NERResponse(BaseModel):
+    entities: List[Entity]
+    words: List[str]  # List of extracted words (added)
+
+class QAResponse(BaseModel):
+    question: str
+    answer: str
+    score: float
+
+class CombinedRequest(BaseModel):
+    text: str  # The input text prompt
+
+class CombinedResponse(BaseModel):
+    ner: NERResponse  # NER output
+    qa: QAResponse  # QA output
+
+# Function to check if the input text belongs to allowed domains
+def is_text_in_allowed_domain(text: str, domains: List[str]) -> bool:
+    for domain in domains:
+        if domain in text.lower():
+            return True
+    return False
+
+# Combined endpoint for NER and QA with domain filtering
+@app.post("/process/", response_model=CombinedResponse)
+async def process_request(request: CombinedRequest):
+    input_text = request.text
+
+    # Check if the input text belongs to the allowed domains
+    if not is_text_in_allowed_domain(input_text, allowed_domains):
+        raise HTTPException(
+            status_code=400,
+            detail="The input text does not match the allowed domains. Please provide a query related to clothing, fashion, or accessories."
+        )
+
+    # Perform Named Entity Recognition (NER)
+    ner_entities = ner_pipeline(input_text)
+
+    # Process NER results into a structured response
+    formatted_entities = [
+        {
+            "word": entity["word"],
+            "entity_group": entity["entity_group"],
+            "score": float(entity["score"]),
+        }
+        for entity in ner_entities
+    ]
+    ner_words = [entity["word"] for entity in ner_entities]  # Collect only the words
+
+    ner_response = {
+        "entities": formatted_entities,
+        "words": ner_words  # Include the list of words
+    }
+
+    # Perform Question Answering (QA)
+    qa_result = qa_pipeline(question=input_text, context=context_msg)
+    qa_result["score"] = float(qa_result["score"])  # Convert numpy.float32 to Python float
+
+    qa_response = {
+        "question": input_text,
+        "answer": qa_result["answer"],
+        "score": qa_result["score"]
+    }
+
+    # Return both NER and QA responses
+    return {"ner": ner_response, "qa": qa_response}
+
+# Root endpoint
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the NER and QA API!"}