Update app.py

app.py

@@ -6,41 +6,35 @@ from langchain_community.vectorstores import FAISS
 from langchain_google_genai import ChatGoogleGenerativeAI
 import re
 
+
 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: List[List[str]], d: dict) -> str:
-    if not message.strip():
-        return "Error: Message cannot be empty.", chat_his, d
+  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 chat_his:
-        his.extend([
-            {"role": "user", "parts": i},
-            {"role": "model", "parts": j},
-        ])
-
-    chat = model.start_chat(history=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)
-    
-    # Update chat history
-    chat_his.append((message, response.text))
-    
-    return format_gemini_response(response.text), chat_his, d
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=["text", "list", "json"],
-    outputs="html"  # Change to HTML for proper rendering
-)
-iface.launch(share=True)
+    return format_gemini_response(response.text),chat_his, d
+iface = gr.Interface(fn = predict,inputs = ["text","list","json"],outputs = "text")
+iface.launch()