Spaces:
Sleeping
Sleeping
File size: 3,931 Bytes
942a690 c56dc42 43ae172 942a690 c56dc42 942a690 43ae172 c56dc42 43ae172 942a690 c56dc42 942a690 c56dc42 942a690 cd022f7 942a690 cd022f7 942a690 cd022f7 942a690 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | # main.py
from fastapi import FastAPI
from pydantic import BaseModel, Field, HttpUrl,root_validator, validator
from typing import List, Optional
from fastapi.middleware.cors import CORSMiddleware
# -- 1. Define Pydantic Models for Data Validation --
# This model defines the structure of the incoming request JSON
class RequestPacket(BaseModel):
# 1. Make fields optional by using Optional[<type>] and setting default to None
text: Optional[str] = None
url: Optional[HttpUrl] = None
image: Optional[str] = None
# 2. Add a validator to ensure at least one field is provided
@root_validator(pre=False, skip_on_failure=True)
def check_at_least_one_field_present(cls, values):
"""Ensure at least one of text, url, or image is provided."""
if not any(values.values()):
raise ValueError('At least one of the fields (text, url, image) must be provided.')
return values
# These models define the structure of the outgoing response JSON
class Analysis(BaseModel):
isMisinformation: bool
reasoning: str
confidenceScore: float = Field(
...,
ge=0, # Must be greater than or equal to 0
le=1 # Must be less than or equal to 1
)
class Source(BaseModel):
name: str
description: str
class ResponsePacket(BaseModel):
summary: str
analysis: Analysis
sources: List[Source]
# -- 2. Create the FastAPI Application --
app = FastAPI(
title="Backend Checker API",
description="A mock API to validate requests and send simulated responses."
)
# ⬇️ ADD THIS MIDDLEWARE SECTION
# This tells the API to accept requests from your frontend
origins = [
"https://your-firebase-app-name.web.app", # Your production frontend URL
"http://localhost:3000",
"https://6000-firebase-studio-1758293511500.cluster-6dx7corvpngoivimwvvljgokdw.cloudworkstations.dev/"
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"], # Allows all methods, including POST
allow_headers=["*"], # Allows all headers
)
# -- 3. Define the API Endpoint --
@app.post("/check", response_model=ResponsePacket)
async def check_request_format(request: RequestPacket):
"""
This endpoint receives a request packet, validates its structure,
and returns a mock analysis response.
"""
print("✅ Request received and successfully validated!")
if request.text:
print(f"Text received (length): {len(request.text)}")
if request.url:
print(f"URL received: {request.url}")
if request.image:
print(f"Image data received, starts with: {request.image[:30]}...")
# The mock response logic remains the same
mock_response = ResponsePacket(
summary="The provided content appears to be a satirical piece, but it is being shared in contexts that suggest it is factual news, which can mislead audiences.",
analysis=Analysis(
isMisinformation=True,
reasoning="The core claim originates from a known satirical website. While not intentionally malicious, its presentation lacks clear satirical markers, leading to its spread as genuine misinformation.",
confidenceScore=0.95
),
sources=[
Source(
name="The Daily Satire Times",
description="The original publisher of the article, which is officially listed as a satirical publication."
),
Source(
name="Cross-Platform Fact-Check Initiative",
description="Shows multiple instances where this article was shared on social media without satire labels, leading to user confusion."
),
Source(
name="Media Literacy Hub",
description="Provides guidelines on how to identify satire versus fake news."
)
]
)
return mock_response |