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·
0f73722
1
Parent(s):
17dcd11
add logic
Browse files- app.py +22 -3
- requirements.txt +4 -1
- src/__pycache__/processor.cpython-310.pyc +0 -0
- src/processor.py +82 -0
app.py
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from fastapi import FastAPI
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app = FastAPI()
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from fastapi import FastAPI
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from pydantic import BaseModel
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from typing import List
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from src.processor import *
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from sentence_transformers import SentenceTransformer
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app = FastAPI()
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class Input(BaseModel):
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text1 : List
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text2 : List
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class Output(BaseModel):
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matrix : List
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@app.post("/process", response_model=Output)
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def process(payload: Input):
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saoke_spec = text_to_saoke(payload.text1)
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saoke_patent = text_to_saoke(payload.text2)
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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embeddings1, embeddings2 = text_to_embeddings(saoke_spec, saoke_patent, model)
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matrix = embeddings_to_matrix(embeddings1, embeddings2)
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print({"matrix": matrix})
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return {"matrix": matrix}
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requirements.txt
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fastapi
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uvicorn[standard]
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fastapi
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uvicorn[standard]
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google.genai
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sentence_transformers
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pydantic
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python-dotenv
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src/__pycache__/processor.cpython-310.pyc
ADDED
Binary file (3.39 kB). View file
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src/processor.py
ADDED
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from google.genai import Client, types
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import os
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import numpy as np
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from sentence_transformers import SentenceTransformer, util
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from dotenv import load_dotenv
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load_dotenv()
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def text_to_saoke(data):
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saoke_list = []
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for d in data:
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prompt = get_prompt()
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prompt += d
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result = send_request(prompt)
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saoke_list.append(result)
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return saoke_list
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def text_to_embeddings(list1, list2, model):
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embeddings1 = []
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for l1 in list1:
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embeddings1.append(model.encode(l1))
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embeddings2 = []
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for l2 in list2:
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embeddings2.append(model.encode(l2))
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return embeddings1, embeddings2
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def embeddings_to_matrix(embeddings1, embeddings2):
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matrix = []
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for e1 in embeddings1:
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for e2 in embeddings2:
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cosim = util.cos_sim(e1, e2)
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matrix.append(cosim)
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matrix = np.array(matrix).reshape(len(embeddings1), len(embeddings2))
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return matrix.tolist()
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def send_request(prompt):
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client = Client(api_key=os.getenv("GEMINI_API_KEY"))
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response = client.models.generate_content(
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model="gemini-2.5-flash",
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contents=prompt,
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)
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return response.text
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def get_prompt():
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prompt = """
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You are well educated in S-A-O-K-E decomposition methodology applied to patents:
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**Subject (S):** The entity that performs the action (e.g., device, user, system).
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**Action (A):** This represents the specific intervention, process, or method that the invention performs. It describes what the invention *does* to or with specific objects or systems (e.g., transmits, applies, mixes).
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**Object (O):** The entity or target that the action is performed upon (e.g., signal, data, mixture).
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**Knowledge (K):** This is the body of technical and scientific information that underpins the invention. It is the knowledge that is necessary to design, implement, and operate the action successfully.
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**Effect (E):** This refers to the outcome, result, or consequence of the action. It describes the benefit, improvement, or new capability that the invention provides.
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the entire invention can be mapped as a linked set of S-A–O-K–E units. For example:
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Step 1: (S₁, A₁, O₁, K₁) → E₁
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Step 2: (S₂,A₂, O₂, K₂=E₁+...) → E₂
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...and so on.
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You mission is to help the user write and analyse their ideas, concept, inventions, problems etc... in the form of S-A-O-K-E.
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You must output a JSON object containings all of the SAOKE:
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{
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"subject": "Mobile payment app",
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"action": "interlaces affine and non-linear lookup tables",
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"object": "AES state bytes",
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"knowledge": "space-hard SPNbox design using table incompressibility theory",
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"effect": "prevents code-lifting and key-extraction even under full memory disclosure."
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},
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{
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"subject": "DRM client",
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"action": "randomizes table encodings on every re-installation",
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"object": "symmetric key material",
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"knowledge": "PUF-bound whitening keys with Even-Mansour construction",
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"effect": "renders stolen binaries unusable on non-bound devices."
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},...
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### Document
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"""
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return prompt
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