Delete app.py

Delete Old app.py

app.py

@@ -1,129 +0,0 @@
-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!"}