Latest App.py

app.py

@@ -1,38 +1,27 @@
 import gradio as gr
 from typing import List
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-import re
+import json
+from Classes.Owiki_Class import OWiki
 
+def predict(query :str , chat_history : List[tuple[str,str]] , invocation_type : str = "OIC" , schemas : dict= {}) -> str:
+    with open("src/config.json",'r') as f:
+        hyperparameters = json.load(f)
+    a = OWiki(**hyperparameters)
+    if invocation_type =="SQL":
+        try:
+            res = a.create_sql_agent(query,schemas)
+        except Exception as e:
+            return e
+    elif invocation_type == "OIC":
+        try:
+            chat = ""
+            for user,bot in chat_history:
+                chat+= f"User: {user} Bot: {bot}\n\n"
+            res = a.search_from_db(query, chat)
+        except Exception as e:
+            return e  
+    return res
 
-genai.configure(api_key="AIzaSyD2o8vjePJb6z8vT_PVe82lVWMD3_cBL0g")
 
-
-def format_gemini_response(text):
-
-  bold_pattern = r"\*\*(.*?)\*\*"
-  italic_pattern = r"\*(.*?)\*"
-  code_pattern = r"(.*?)"
-  text = text.replace('\n', '<br>')
-  formatted_text = re.sub(code_pattern,"<pre><code>\\1</code></pre>",text)
-  formatted_text = re.sub(bold_pattern, "<b>\\1</b>", formatted_text)
-  formatted_text = re.sub(italic_pattern, "<i>\\1</i>", formatted_text)
-
-  return formatted_text
-def predict(message :str , chat_his ,d ) -> str:
-    model = genai.GenerativeModel("gemini-pro")
-    his = []
-    # for i,j in history:
-    #     his.extend([
-    #         {"role": "user", "parts": i},
-    #         {"role": "model", "parts": j},
-    #     ])
-    chat = model.start_chat(
-        history=his
-    )
-    response = chat.send_message(message)
-    return format_gemini_response(response.text),chat_his, d
-iface = gr.Interface(fn = predict,inputs = ["text","list","json"],outputs = "text")
-iface.launch(debug = True)
+iface = gr.Interface(fn = predict,inputs = ["text","list","text","json"],outputs = "text")
+iface.launch(debug=True)