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from fastapi import APIRouter, HTTPException
from fastapi.responses import FileResponse
from fpdf import FPDF
from pydantic import BaseModel
import os
import uuid
import httpx
import cloudinary
import cloudinary.uploader
cloudinary.config(
cloud_name = "dgdxa7qqg",
api_key = "376418913322648",
api_secret = "ut-74eisi_NAFxfrEUDhER2szgM", # Your Cloudinary API secret
secure=True
)
router = APIRouter()
class FIRPDF(FPDF):
def header(self):
self.set_font("Arial", "B", 14)
self.cell(0, 10, "FIRST INFORMATION REPORT (FIR)", align="C", ln=True)
self.ln(10)
def generate_fir_pdf(data: dict) -> str:
pdf = FIRPDF()
pdf.add_page()
pdf.set_font("Arial", size=11)
# Add content
pdf.cell(0, 10, f"Book No.: {data['book_no']}", ln=True)
pdf.cell(0, 10, f"Form No.: {data['form_no']}", ln=True)
pdf.cell(0, 10, f"Police Station: {data['police_station']}", ln=True)
pdf.cell(0, 10, f"District: {data['district']}", ln=True)
pdf.cell(0, 10, f"Date and Hour of Occurrence: {data['date_hour_occurrence']}", ln=True)
pdf.cell(0, 10, f"Date and Hour when Reported: {data['date_hour_reported']}", ln=True)
pdf.cell(0, 10, f"Name and Residence of Informer/Complainant: {data['informer_name']}", ln=True)
pdf.multi_cell(0, 10, f"Brief Description of Offense (with Section) and Property Carried Off (if any): {data['description_offense']}")
pdf.cell(0, 10, f"Place of Occurrence and Distance/Direction from Police Station: {data['place_occurrence']}", ln=True)
pdf.cell(0, 10, f"Name and Address of the Criminal: {data['criminal_name']}", ln=True)
pdf.multi_cell(0, 10, f"Steps Taken Regarding Investigation/Explanation of Delay: {data['investigation_steps']}")
pdf.cell(0, 10, f"Date and Time of Dispatch from Police Station: {data['dispatch_time']}", ln=True)
pdf.cell(0, 10, f"Signature of Writer: ..............................", ln=True)
temp_file = f"FIR_Report_{uuid.uuid4().hex}.pdf"
pdf.output(temp_file)
# Upload the PDF to Cloudinary
try:
print(f"Uploading file: {temp_file}") # Debug print statement
response = cloudinary.uploader.upload(
temp_file,
resource_type="raw", # PDFs are treated as raw files in Cloudinary
folder="fir_reports/"
)
os.remove(temp_file) # Clean up the local file
# Generate a downloadable and viewable URL
view_url = response['secure_url']
download_url = f"{view_url}?attachment=true"
return {"view_url": view_url, "download_url": download_url}
except Exception as e:
os.remove(temp_file) # Clean up in case of failure
print(f"Error uploading to Cloudinary: {str(e)}") # Debug print statement
raise HTTPException(status_code=500, detail=f"Error uploading to Cloudinary: {str(e)}")
class FIRDetails(BaseModel):
book_no: str
form_no: str
police_station: str
district: str
date_hour_occurrence: str
date_hour_reported: str
informer_name: str
description_offense: str
place_occurrence: str
criminal_name: str
investigation_steps: str
dispatch_time: str
# Function to get LawGPT response
async def get_lawgpt_response(description_offense: str) -> str:
"""
Sends the description_offense to an external service and retrieves the response.
"""
url = "https://aniudupa-fir-gen.hf.space/lawgpt/chat/" # Replace with the actual URL
try:
# Construct the question to send to the LawGPT service
question = f"Based on this incident: '{description_offense}', please provide a concise description of the offense, including the sections of the Indian Penal Code that apply."
# Make the API request to LawGPT
async with httpx.AsyncClient() as client:
response = await client.post(url, json={"question": question, "chat_history": "what"})
response.raise_for_status()
data = response.json()
concise_description = data.get("answer", "").split("\n")[0]
return concise_description
except httpx.HTTPStatusError as e:
print(f"HTTP error: {e.response.status_code}, {e.response.text}") # Debug print statement
raise HTTPException(status_code=e.response.status_code, detail=e.response.text)
except Exception as e:
print(f"Failed to get response from LawGPT: {str(e)}") # Debug print statement
raise HTTPException(status_code=500, detail=f"Failed to get response from LawGPT: {str(e)}")
# Endpoint to generate FIR
@router.post("/")
async def generate_fir(details: FIRDetails):
try:
print(f"Received FIR details: {details}") # Debug print statement
detailed_offense = await get_lawgpt_response(details.description_offense)
details.description_offense = detailed_offense
urls = generate_fir_pdf(details.dict())
return {
"message": "FIR PDF generated successfully!",
"view_url": urls["view_url"], # Cloudinary view URL
"download_url": urls["download_url"] # Cloudinary download URL
}
except Exception as e:
print(f"Error generating FIR: {str(e)}") # Debug print statement
raise HTTPException(status_code=500, detail=str(e)) |