Update app.py

app.py

@@ -10,21 +10,6 @@ import gdown
 import os
 import pandas as pd
 
-# # Download the file
-# file_id = '1P3Nz6f3KG0m0kO_2pEfnVIhgP8Bvkl4v'
-# url = f'https://drive.google.com/uc?id={file_id}'
-# excel_file_path = os.path.join(os.path.expanduser("~"), 'medical_data.csv')
-
-# gdown.download(url, excel_file_path, quiet=False)
-
-# # Read the CSV file into a DataFrame using 'latin1' encoding
-# try:
-#     medical_df = pd.read_csv(excel_file_path, encoding='utf-8')
-# except UnicodeDecodeError:
-#     medical_df = pd.read_csv(excel_file_path, encoding='latin1')
-
-# Read the CSV file into a DataFrame using 'latin1' encoding
-
 excel_file_path = 'medical_data.csv'
 
 try:
@@ -71,11 +56,51 @@ def get_medical_response(question, vectorizer, X_tfidf, model, tokenizer, sbert_
     else:
         return lm_generated_response
 
-# Streamlit app
-st.title("DiBot")
-
-user_input = st.text_input("You:")
+# Custom CSS to enhance the UI
+st.markdown("""
+    <style>
+        .main {
+            background-color: #f5f5f5;
+            padding: 2rem;
+            border-radius: 10px;
+        }
+        .stTextInput > div > div > input {
+            border: 2px solid #ccc;
+            border-radius: 10px;
+            padding: 10px;
+            width: 100%;
+        }
+        .stButton > button {
+            background-color: #4CAF50;
+            color: white;
+            border: none;
+            border-radius: 10px;
+            padding: 10px 20px;
+            cursor: pointer;
+        }
+        .stButton > button:hover {
+            background-color: #45a049;
+        }
+        .stTextArea > div > div > textarea {
+            border: 2px solid #ccc;
+            border-radius: 10px;
+            padding: 10px;
+            width: 100%;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Streamlit app layout
+st.title("🤖 DiBot - Your Medical Query Assistant")
+st.write("Ask me any medical question, and I'll do my best to provide an accurate response.")
+
+st.subheader("Enter your question below:")
+
+user_input = st.text_input("Your question:", "")
 if user_input.lower() == "exit":
     st.stop()
-response = get_medical_response(user_input, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df)
-st.text_area("Bot's Response:", response)
+
+if user_input:
+    response = get_medical_response(user_input, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df)
+    st.subheader("Bot's Response:")
+    st.text_area("", response, height=200)