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from fastapi import FastAPI |
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from gemiwrap import GeminiWrapper |
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from google import genai |
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from google.genai import types |
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import json_repair |
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from functools import partial |
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import os |
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if os.path.exists(".env"): |
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from dotenv import load_dotenv |
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load_dotenv() |
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app = FastAPI() |
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geminiWrapper = partial(GeminiWrapper, |
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model_name="gemini-flash-lite-latest", |
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schema=genai.types.Schema( |
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type = genai.types.Type.OBJECT, |
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required = ["title", "content"], |
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properties = { |
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"title": genai.types.Schema( |
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type = genai.types.Type.STRING, |
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), |
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"content": genai.types.Schema( |
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type = genai.types.Type.STRING, |
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), |
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}, |
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), |
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delete_files=True |
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) |
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@app.get("/") |
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def greet_json(): |
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return { |
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"available_endpoints": [ |
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"http://127.0.0.1:8000/", |
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"http://127.0.0.1:8000/breaking_news", |
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"http://127.0.0.1:8000/hacker_news", |
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"http://127.0.0.1:8000/hackers_law", |
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"http://127.0.0.1:8000/pdf_crux?name=assembly", |
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"http://127.0.0.1:8000/pdf_crux?name=arm", |
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"http://127.0.0.1:8000/pdf_crux?name=cpumemory", |
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"http://127.0.0.1:8000/pdf_crux?name=c" |
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] |
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} |
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@app.get("/breaking_news") |
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def breaking_news(): |
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user_prompt = None |
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with open("breaking_news.md", 'r') as file: |
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user_prompt = file.read() |
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grounding_tool = types.Tool( |
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google_search=types.GoogleSearch() |
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) |
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model_responses = geminiWrapper(tools=[grounding_tool], response_mime_type="text/plain").send_message(user_prompt=user_prompt) |
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return json_repair.loads(model_responses[0]) |
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@app.get("/hacker_news") |
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def hacker_news(): |
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with open("test.txt", 'w') as file: |
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file.write("Hey, Hey") |
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text = None |
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with open("test.txt", 'r') as file: |
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text = file.read() |
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return {"Hello": text} |
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@app.get("/hackers_law") |
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def hackers_law(): |
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user_prompt = None |
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with open("hackers_law.md", 'r') as file: |
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user_prompt = file.read() |
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model_responses = geminiWrapper().send_message(user_prompt=user_prompt) |
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return json_repair.loads(model_responses[0]) |
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@app.get("/pdf_crux") |
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def pdf_crux(name: str): |
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system_prompt = None |
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with open("LearningPointsExtractor.md", 'r') as file: |
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system_prompt = file.read() |
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file_path = "ProgrammingGroundUp-1-0-booksize.pdf" |
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if name == "assembly": |
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file_path = "ProgrammingGroundUp-1-0-booksize.pdf" |
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elif name == "arm": |
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file_path = "arm-baremetal-ebook.pdf" |
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elif name == "cpumemory": |
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file_path = "cpumemory.pdf" |
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elif name == "c": |
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file_path = "The C Programming Language (Kernighan Ritchie).pdf" |
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model_responses = geminiWrapper().send_message(user_prompt="", system_instruction=system_prompt, file_path=file_path) |
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return json_repair.loads(model_responses[0]) |
