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bce1e15
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1 Parent(s): 7539e21

Upload folder using huggingface_hub

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  1. gradio_app.py +19 -20
gradio_app.py CHANGED
@@ -107,24 +107,24 @@ def translate(text):
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- def get_random_sample():
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- filename = "Apr.csv" # Replace 'your_file.csv' with your actual file path
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- with open(filename, 'r', newline='') as csvfile:
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- # Step 3: Read all rows into a list
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- reader = csv.reader(csvfile)
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- rows = [row for row in reader]
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- # Step 4: Generate a random index
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- random_index = random.randint(0, len(rows) - 1)
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- print(ra)
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- # Step 5: Retrieve the row at the random index
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- random_row = rows[random_index]
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- # Step 6: Print or process the random row
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- return random_row
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- example = get_random_sample()
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  def predict_statutes(fir_text,language):
@@ -133,17 +133,16 @@ def predict_statutes(fir_text,language):
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  else:
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  text = fir_text
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- ac_statute= example[-1] if fir_text==example[5] else ''
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  if text:
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  gpt_output = generate(text)
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  statutes_list = extract_statutes(gpt_output)
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  if statutes_list:
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- return ("\n".join(f"- {statute}" for statute in statutes_list),ac_statute)
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  else:
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- return ("No statutes were predicted. Please check the FIR text and try again.",ac_statute)
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  else:
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- return ("Please enter the FIR text to predict statutes.",ac_statute)
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  demo = gr.Interface(
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  title='Statute Prediction',
@@ -153,8 +152,8 @@ demo = gr.Interface(
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  gr.Dropdown(label="Select Language", choices=["English", "Hindi"], value="English"),
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  # gr.Slider(minimum=0.1,maximum=1.0,value=0.5,step=0.1),
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  ],
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- outputs=[gr.Textbox(label="Predicted Statutes"),gr.Textbox(label="Actual Statutes",value=example[-1])],
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- examples=[[example[5], "English"]],
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  )
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  demo.launch()
 
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+ # def get_random_sample():
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+ # filename = "Apr.csv" # Replace 'your_file.csv' with your actual file path
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+ # with open(filename, 'r', newline='') as csvfile:
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+ # # Step 3: Read all rows into a list
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+ # reader = csv.reader(csvfile)
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+ # rows = [row for row in reader]
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+ # # Step 4: Generate a random index
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+ # random_index = random.randint(0, len(rows) - 1)
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+ # print(ra)
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+ # # Step 5: Retrieve the row at the random index
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+ # random_row = rows[random_index]
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+ # # Step 6: Print or process the random row
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+ # return random_row
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+ # example = get_random_sample()
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  def predict_statutes(fir_text,language):
 
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  else:
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  text = fir_text
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  if text:
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  gpt_output = generate(text)
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  statutes_list = extract_statutes(gpt_output)
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  if statutes_list:
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+ return "\n".join(f"- {statute}" for statute in statutes_list)
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  else:
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+ return "No statutes were predicted. Please check the FIR text and try again."
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  else:
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+ return "Please enter the FIR text to predict statutes."
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  demo = gr.Interface(
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  title='Statute Prediction',
 
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  gr.Dropdown(label="Select Language", choices=["English", "Hindi"], value="English"),
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  # gr.Slider(minimum=0.1,maximum=1.0,value=0.5,step=0.1),
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  ],
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+ outputs=[gr.Textbox(label="Predicted Statutes")],
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+ # examples=[[example[5], "English"]],
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  )
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  demo.launch()