Update app.py

app.py

@@ -10,4 +10,62 @@ fileobj.close()
 from sentence_transformers import SentenceTransformer, util
 import torch
 
-embedder = SentenceTransformer("ramdane/jurimodel")
+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(share=True,debug=True)