Update app.py

app.py

@@ -1,85 +1,85 @@
-import streamlit as st
-import pandas as pd
-from PIL import Image
-import json
-from streamlit_lottie import st_lottie
-
-##### BUET Logo ###########
-image = Image.open("buet.png")
-new_image = image.resize((100, 100))
-#st.image(new_image)
-st.title("Automated LLM and Web Scrapping based Road Accident Dataset creation from Newspapers")
-
-
-######### Animation ##########
-def load_lottiefile(filepath:str):
-    with open(filepath,"r") as f:
-        return json.load(f)
-lottie_coding=load_lottiefile("animate.json")
-st_lottie(
-        lottie_coding,
-        height=200,
-        
-    )
-
-
-radio_btn1=st.radio("**Choose the newspaper you want to collect news from**",options=("Prothom Alo","Dhaka Tribune","The Daily Star"))
-radio_btn2=st.radio("Choose an LLM model",options=("GPT-3.5 (Medium Cost)","GPT-4 (High Cost)","Llama3 (Free)"))
-
-number = st.number_input("**Enter the number of accident news you want the LLM to go through**",min_value=0,max_value=5000)
-
-if st.button("Generate Dataset"):
-    st.write("**Please wait until the datasest is finished generating. It takes almost 8 sec to process each entry for GPT-4 and 30 sec for GPT-3.5 and Llama3. So, for example, if you entered 15 as input, it will take almost 2 minutes for GPT-4 and 7.5 min for GPT-3.5 and Llama3. The dataset will appear below.**")
-    
-    if radio_btn1=="Prothom Alo":
-        import Prothom_alo_fully_scraped
-        df=Prothom_alo_fully_scraped.get_data(number)
-    elif radio_btn1=="Dhaka Tribune":
-        import Dhaka_Tribune_Fully_Scraped
-        df=Dhaka_Tribune_Fully_Scraped.get_data(number)
-    elif radio_btn1== "The Daily Star":
-        import Daily_Star_fully_scraped
-        df=Daily_Star_fully_scraped.get_data(number)
-    if radio_btn2=="GPT-4 (High Cost)":
-        import LLM_automation_GPT
-        df2=LLM_automation_GPT.create_data(df)
-    elif radio_btn2=="Llama3 (Free)":
-        import LLM_automation_Groq
-        df2=LLM_automation_Groq.create_data(df)
-    elif radio_btn2=="GPT-3.5 (Medium Cost)":
-        import LLM_automation_GPT35
-        df2=LLM_automation_GPT35.create_data(df)
-    st.dataframe(df2)
-    print(len(df))
-    
-    
-#st.write("""
-#                    **Developed by:**\n
-        
-#                    *MD Thamed Bin Zaman Chowdhury, Student ID: 1904184,*\n
-#                    *Department of Civil Engineering, BUET*\n
-#                    *E-mail: [email protected]*
-#         """)
-
-
-st.write("--------")
-st.write("**Modules and packages used to develop the program:**")
-
-######## Other Logos ################
-p=125
-image2 = Image.open("pandas.png")
-new_image2 = image2.resize((p, p))
-image3 = Image.open("numpy.png")
-new_image3 = image3.resize((p, p))
-image4 = Image.open("selenium_webdriver.jpeg")
-new_image4 = image4.resize((p, p))
-image5 = Image.open("streamlit.png")
-new_image5 = image5.resize((p, p))
-image6 = Image.open("openai.png")
-new_image6 = image6.resize((p, p))
-image7 = Image.open("llama3.jpeg")
-new_image7 = image7.resize((p, p))
-image8 = Image.open("langchain.png")
-new_image8 = image8.resize((p, p))
-
+import streamlit as st
+import pandas as pd
+from PIL import Image
+import json
+from streamlit_lottie import st_lottie
+
+##### BUET Logo ###########
+image = Image.open("buet.png")
+new_image = image.resize((100, 100))
+#st.image(new_image)
+st.title("Automated LLM and Web Scrapping based Road Accident Dataset creation from Newspapers")
+
+
+######### Animation ##########
+def load_lottiefile(filepath:str):
+    with open(filepath,"r") as f:
+        return json.load(f)
+lottie_coding=load_lottiefile("animate.json")
+st_lottie(
+        lottie_coding,
+        height=200,
+        
+    )
+
+
+radio_btn1=st.radio("**Choose the newspaper you want to collect news from**",options=("Prothom Alo","The Daily Star"))
+radio_btn2=st.radio("Choose an LLM model",options=("GPT-3.5 (Medium Cost)","GPT-4 (High Cost)","Llama3 (Free)"))
+
+number = st.number_input("**Enter the number of accident news you want the LLM to go through (Maximum 50)**",min_value=0,max_value=50)
+
+if st.button("Generate Dataset"):
+    st.write("**Please wait until the datasest is finished generating. It takes almost 8 sec to process each entry for GPT-4 and 30 sec for GPT-3.5 and Llama3. So, for example, if you entered 15 as input, it will take almost 2 minutes for GPT-4 and 7.5 min for GPT-3.5 and Llama3. The dataset will appear below.**")
+    
+    if radio_btn1=="Prothom Alo":
+        import Prothom_alo_fully_scraped
+        df=Prothom_alo_fully_scraped.get_data(number)
+    elif radio_btn1=="Dhaka Tribune":
+        import Dhaka_Tribune_Fully_Scraped
+        df=Dhaka_Tribune_Fully_Scraped.get_data(number)
+    elif radio_btn1== "The Daily Star":
+        import Daily_Star_fully_scraped
+        df=Daily_Star_fully_scraped.get_data(number)
+    if radio_btn2=="GPT-4 (High Cost)":
+        import LLM_automation_GPT
+        df2=LLM_automation_GPT.create_data(df)
+    elif radio_btn2=="Llama3 (Free)":
+        import LLM_automation_Groq
+        df2=LLM_automation_Groq.create_data(df)
+    elif radio_btn2=="GPT-3.5 (Medium Cost)":
+        import LLM_automation_GPT35
+        df2=LLM_automation_GPT35.create_data(df)
+    st.dataframe(df2)
+    print(len(df))
+    
+    
+#st.write("""
+#                    **Developed by:**\n
+        
+#                    *MD Thamed Bin Zaman Chowdhury, Student ID: 1904184,*\n
+#                    *Department of Civil Engineering, BUET*\n
+#                    *E-mail: [email protected]*
+#         """)
+
+
+st.write("--------")
+st.write("**Modules and packages used to develop the program:**")
+
+######## Other Logos ################
+p=125
+image2 = Image.open("pandas.png")
+new_image2 = image2.resize((p, p))
+image3 = Image.open("numpy.png")
+new_image3 = image3.resize((p, p))
+image4 = Image.open("selenium_webdriver.jpeg")
+new_image4 = image4.resize((p, p))
+image5 = Image.open("streamlit.png")
+new_image5 = image5.resize((p, p))
+image6 = Image.open("openai.png")
+new_image6 = image6.resize((p, p))
+image7 = Image.open("llama3.jpeg")
+new_image7 = image7.resize((p, p))
+image8 = Image.open("langchain.png")
+new_image8 = image8.resize((p, p))
+
 st.image([new_image2, new_image3,new_image4,new_image5,new_image6,new_image7,new_image8])