gradio_app.py

import requests, uuid, json
from openai import AzureOpenAI,OpenAI
import re
import gradio as gr
import csv
import random

client = AzureOpenAI(
    api_key="226e96dc78fb49b3bcd143aa8b191dd2", 
    api_version="2023-07-01-preview",
    azure_endpoint="https://votum.openai.azure.com/",
)

mistral_client = OpenAI(
    api_key='EMPTY',
    base_url="http://20.124.240.6:8083/v1",
)


fact_example = '''In service Mr. In-charge Inspector Mr. City of Police Station Kotwali Mr. G Candidate Chetna Gupta alias Chanchal Gupta Advocate Resident of Flat No., 76/44 Halsey Road Kanpur Nagar and practicing law from a pucca chamber just in front of CMO office at Kachehri. On 2024, at around 8: 30 am, my chamber suddenly caught fire and at around 9: 00 am, I got a call informing me that my chamber was on fire. I reached the chamber immediately. By then, Manish, who was cleaning my chamber, was dousing the fire with water. All my chamber's luggage, files, AC, sofa wings, amalt glass walls, necessary documents, miscellaneous items, etc. have been destroyed. I am informing the concerned post. Please take appropriate action. Dated 29. 2024 Signature English unreadable Candidate Chetna Gupta alias Chanchal Gupta Md. 7376222267 Note I hereby certify that 674 Dhirendra Pratap Singh, permanent & tahrir copy of the complaint was literally typed by me on the computer 5057 Lalit Kumar. That I attest to.''' 

prompt = """Task: Given examples of an FIR and the statutes applied in that case, your objective is to make accurate predictions of the specific charge or statute that is most likely to be applied within the context of the case delimited by triple backticks (```), ensuring exact predictions and learning from the provided examples.You should only include the statutes it is most confident about.The response format should include the statutes applied as in the context.
You should to showcase creativity and knowledge to enhance the accuracy of statute predictions based on the given fact statement.

Context:
-----
Fact Statement:"Copying Tahrir Hindi plaintiff ................... In service Mr. SHO Akrabad Aligarh The request is that I am Rahul Kumar S/0 Gopal resident of Vijaygarh Chauraha Police Station Akrabad Aligarh, today on 30/4/2021 at around 7 o'clock in the evening I was sitting at my coke shop, Deepu, Kalu, Karthik, Dinesh, Saunu, came to me and started saying that you ask for a lot of money, now tell you that then the above people called their colleagues and called  And all of them unanimously started beating me and my sister Neelam, due to which my sister's clothes were torn, hearing the noise, Ramu Sunil, many people came from nearby, then all these people started running threatening to kill and then Kalu S/0 Dinanath resident of Kuagaon, Karthik S/0 Devendra,  Deepu fired at me with the intention of killing me, in which a fire has hit the thumb of my left hand, due to which I have suffered a lot of injury and bleeding, so I request sir to please file my report Signature Rahul Applicant Rahul S/0 Gopal R/o Vijaygarh Chauraha Police Station Akhrabad Aligarh Mo0 8126303026 Date30/4/2021 Author: Narasimma Pawar S/0 Rambabu Powerhouse, Karhala Road, Mau0 908400582 Note: I am CC 551 Sanjeev Kumar certifying that the copy of Tahrir has been recorded on the computer word and word"

Statutes:['IPC_323', 'IPC_354', 'IPC_307', 'IPC_506']
-----

###

Format your response as follows:
"Statutes applied: [List of applicable statutes]"

Instructions:

Learn from the examples provided in the context to understand the task of charge or statute prediction.
Your response should be focused on providing the exact statute or charge that aligns with the legal principles and precedents applicable to the given facts.
In your response, include only the statutes you are most confident about.Ensure that the statutes generated as responses are valid and recognized legal statutes applicable in FIRs. In certain cases you can also apply sections from special acts including but not limited to 'The_Arms_Act_27' , 'The_Motor_Vehicles_Act_1988', 'Dowry_Prohibition_Act_1961', like 'Dowry_Prohibition_Act_1961_3'. Avoid generating fabricated or invalid statutes.
Think step by step to cover all possible statutes that are relevant to the fact statement.

Fact Statement: ```{fact}```
"""

def generate(input_text):
    com = prompt.format(fact=input_text)
    print(input_text)
    chat_completion = mistral_client.chat.completions.create(
        # model="gpt-4-turbo",
        model='Qwen/Qwen1.5-72B-Chat-GPTQ-Int4',
        temperature=0.5,
        messages=[
                {"role": "system", "content": "You are a helpful assistant who is expert in tagging FIRs with relevant statutes from IPC among other special acts."},
            {
                "role": "user",
                "content": com,
            }
        ],
    )
    print(chat_completion)
    return chat_completion.choices[0].message.content

def extract_statutes(gpt_output):  
    # Regular expression to match statutes within brackets  
    statutes = re.findall(r'\[([^\]]+)\]', gpt_output)  
    if statutes:  
        # Split the string into a list on comma followed by space  
        return statutes
    return []  

def translate(text):
    # Add your key and endpoint
    key = "8760fcb757fe44a19d3ec590cb80836f"
    endpoint = "https://api.cognitive.microsofttranslator.com"

    # location, also known as region.
    # required if you're using a multi-service or regional (not global) resource. It can be found in the Azure portal on the Keys and Endpoint page.
    location = "centralindia"

    path = '/translate'
    constructed_url = endpoint + path

    params = {
        'api-version': '3.0',
        'from': 'hi',
        'to': 'en',
    }

    headers = {
        'Ocp-Apim-Subscription-Key': key,
        'Ocp-Apim-Subscription-Region': location,
        'Content-type': 'application/json',
        'X-ClientTraceId': str(uuid.uuid4())
    }

    # You can pass more than one object in body.
    body = [{
        'text': text
    }]

    request = requests.post(constructed_url, params=params, headers=headers, json=body)
    return request.json()[0]['translations'][0]['text']


  
  

# def get_random_sample():
#     filename = "Apr.csv"  # Replace 'your_file.csv' with your actual file path
#     with open(filename, 'r', newline='') as csvfile:
#         # Step 3: Read all rows into a list
#         reader = csv.reader(csvfile)
#         rows = [row for row in reader]

#         # Step 4: Generate a random index
#         random_index = random.randint(0, len(rows) - 1)
#         print(ra)

#         # Step 5: Retrieve the row at the random index
#         random_row = rows[random_index]

#         # Step 6: Print or process the random row
#         return random_row

# example = get_random_sample()


def predict_statutes(fir_text,language):  
    if language == 'Hindi':
        text = translate(fir_text)
    else:
        text = fir_text
    
    
    if text:  
        gpt_output = generate(text)  
        statutes_list = extract_statutes(gpt_output)  
        if statutes_list:  
            return "\n".join(f"- {statute}" for statute in statutes_list)
        else:  
            return "No statutes were predicted. Please check the FIR text and try again."
    else:  
        return "Please enter the FIR text to predict statutes."

demo = gr.Interface(
    title='Statute Prediction',
    description='Uses AI to analyze the FIR content and intelligently predict applicable statutes',
    fn=predict_statutes,  
    inputs=[gr.Textbox(label="Enter the FIR:", placeholder="Type or paste the FIR here...", lines=10),   
            gr.Dropdown(label="Select Language", choices=["English", "Hindi"], value="English"),
            # gr.Slider(minimum=0.1,maximum=1.0,value=0.5,step=0.1),
            ],
    outputs=[gr.Textbox(label="Predicted Statutes")],  
    # examples=[[example[5], "English"]],  
)  
  
demo.launch()



#  GRADIO_SERVER_NAME=0.0.0.0 GRADIO_SERVER_PORT=7862 gradio gradio_app.py