Create app.py

app.py

@@ -0,0 +1,118 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+import requests
+
+
+
+#changement du logo et du titre de mon application en anglais
+st.set_page_config(page_title="TSC", page_icon="books", layout="centered", menu_items=None)
+
+
+
+col1, col2, col3 = st.columns(3)
+xx = col1
+ar = col2.image("keyce.jpg", width=180)
+yy = col3
+
+
+
+# menu horizontal
+selected = option_menu(None, ["Home", "Translate", "Summarization", "Chatbot"], 
+    icons=['house', 'globe', "pencil", "chat"], 
+    menu_icon="cast", default_index=0, orientation="horizontal",
+    styles={
+            "container": {"padding":"5!important", "background-color":"#000E38", "color":"white"},
+            "icon": {"color":"#fff", "font-size":"16px"}, 
+            "nav-link": {"font-size":"16px", "text-align":"left", "margin":"0px", "--hover-color":"#f9d1ac", "color":"white"},
+            "nav-link-selected": {"background-color": "#FF9633", "color": "white"},
+    })
+
+
+
+if selected == "Home":
+    st.subheader("Welcome to our app !!")
+    st.info(":blue[We are the present of tomorrow! Please like it after use!]")
+    #Insertion d'une image 
+    st.image("nlp1.png", width=None)  
+    st.write("Hello to everybody! In this Artificial Intelligence app, we offer you a THREE-IN-ONE package. First of all, we've got the (Translate) menu, which lets you do a good job of translating into a few foreign languages, then we've got the (Summary) option, which lets you enter your text in order to summarize it, and finally, as a bonus, we've integrated a chatbot model in the (Chatbot) option, which lets you try out our chatbot, which we're currently perfecting.") 
+
+elif selected == 'Translate':
+    languages = st.selectbox('Select input and translate languages', ('', 'english - chinese', 'english - french', 'english - german', 'english - spanish'))
+    col3, col4 = st.columns(2)
+    text_in = col3.text_area("", height=200, placeholder='type or paste the text you wish to translate.')
+    #Boutton pour effectuer la traduction
+    btn_translate = st.button("🌐 Translate")
+    if languages == "english - spanish" and text_in:
+        API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-es"
+        headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+        if btn_translate:
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json() 
+            output = query({"inputs": text_in})
+            text_out = col4.markdown('''<br/> <p style='text-align:left;font-size:16px;'>'''+ output[0]["translation_text"] +'''</p>''',  unsafe_allow_html=True)
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")
+    elif languages == "english - french" and text_in:
+        API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-fr"
+        headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+        if btn_translate:
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json() 
+            output = query({"inputs": text_in})
+            text_out = col4.markdown('''<br/> <p style='text-align:left;font-size:16px;'>'''+ output[0]["translation_text"] +'''</p>''',  unsafe_allow_html=True)
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")   
+    elif languages == 'english - chinese' and text_in:
+        API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-zh"
+        headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}       
+        if btn_translate:
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json() 
+            output = query({"inputs": text_in})
+            text_out = col4.markdown('''<br/> <p style='text-align:left;font-size:16px;'>'''+ output[0]["translation_text"] +'''</p>''',  unsafe_allow_html=True)
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")
+    elif languages == "english - german" and text_in:
+        API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-de"
+        headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+        if btn_translate:
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json() 
+            output = query({"inputs": text_in})
+            text_out = col4.markdown('''<br/> <p style='text-align:left;font-size:16px;'>'''+ output[0]["translation_text"] +'''</p>''',  unsafe_allow_html=True)
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")
+
+elif selected == 'Summarization':
+    text_sum = st.text_area("", height=250, placeholder='Please type or paste the text you wish to summarise here.')
+    #Boutton pour effectuer la prédiction
+    btn_sum = st.button("📝 Summary")
+    if text_sum:
+        if btn_sum:
+            API_URL = "https://api-inference.huggingface.co/models/Falconsai/text_summarization"
+            headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+            def query_sum(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json()
+            st.markdown('''<h4 style='text-align:left;color:black;'> Answer </h4>''',  unsafe_allow_html=True) 
+            output_sum = query_sum({"inputs": text_sum})
+            text_out_sum = st.markdown('''<p style='text-align:left;font-size:16px;color:orange;font-weight:bold'>'''+ output_sum[0]["summary_text"] +'''</p>''',  unsafe_allow_html=True)
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")
+
+elif selected == 'Chatbot':
+    texte_chat = st.text_input("Entrez un texte")
+    #Boutton pour effectuer le chatbot
+    btn_chat = st.button("💬 Submit")
+    API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
+    headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+
+    if texte_chat:
+        if btn_chat:
+            def chatbot(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json()
+            output_chat = chatbot({"inputs": texte_chat})
+            text_out_chat = st.info(':green['+ output_chat[0] ["generated_text"] +']')
+            st.info(":blue[Please feel fit to like it after use! 😉🙏]")
+
+st.markdown('''<br/> <h6 style='text-align:center;color:grey;font-size:11px;'> © 2024 - TOUNDE - All rights reserved </h6>''',  unsafe_allow_html=True)