app.py

import pickle
import os



print(os.getcwd())
fileobj=open("/home/user/app/embmmn7.obj","rb")
corpus_embeddings,corpus=pickle.load(fileobj)
fileobj.close()
from sentence_transformers import SentenceTransformer, util
import torch

embedder = SentenceTransformer("ramdane/jurimodel")
import google.generativeai as genai
genai.configure(api_key="AIzaSyCcxB0xY2C1IGDqxlLRmLBH6AX_wbBORX4")

# Set up the model
generation_config = {
  "temperature": 0,
  "top_p": 1,
  "top_k": 1,
  "max_output_tokens": 2048,
}

safety_settings = [
  {
    "category": "HARM_CATEGORY_HARASSMENT",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_HATE_SPEECH",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
    "threshold": "BLOCK_NONE"
  },
]

model = genai.GenerativeModel(model_name="gemini-1.0-pro-001",
                              generation_config=generation_config,
                              safety_settings=safety_settings)
def show(queries):
  query_embedding = embedder.encode(queries, convert_to_tensor=True)
  hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=10)

  hits = hits[0]
  history=[]

    #Get the hits for the first query

  for i in range(0,10):
    history.append({"role": "user", "parts": [corpus[hits[i]['corpus_id']]]})
    history.append({"role": "model", "parts": ["حسنا"]})
  convo = model.start_chat(history=history)
  convo.send_message("    اجب من خلال ما سبق من اجتهادات على السؤال التالي مع دكر الاجتهاد الدي اعتمدت عليه"+queries)

  return convo.last.text
import gradio as gr
app = gr.Interface(
  fn=show,
  inputs=gr.Textbox(label="إسئل وسيتم الاجابة عن طريق الاجتهادات القضائية"),
  outputs=gr.TextArea(label="استنتاج النموذج"),
  # Prevents caching conversation history
)

app.launch()