Upload 5 files

main_app.py

@@ -0,0 +1,184 @@
+import streamlit as st
+import requests
+
+images = {
+    "ChatBot": "https://recfaces.com/wp-content/uploads/2021/06/voice-recognition-830x571.jpg",
+    "Text2Speech": "https://img.freepik.com/vecteurs-premium/traducteur-vocal-ligne-isometrique-concept-langues-apprentissage-e-learning-traduction-langues-guide-audio-traducteur-chatbot-intelligence-artificielle_589019-3704.jpg?w=900",
+    "Translation": "https://as2.ftcdn.net/v2/jpg/00/49/36/65/1000_F_49366575_CRDRRXsM7DrL2AHc06Fa4uoPFTOSh4oj.jpg"
+}
+
+# Fonction pour afficher la barre de menu horizontale interactive
+def afficher_barre_menu():
+    
+    # Modifier l'arrière-plan de la barre latérale en fonction du choix      
+    st.markdown(
+        """
+        <style>
+        
+        [data-testid="stSidebarContent"]{
+        background-color: #e5e5f7;
+        opacity: 0.8;
+        background-image:  repeating-radial-gradient( circle at 0 0, transparent 0, #e5e5f7 10px ), repeating-linear-gradient( #f7724555, #f77245 );
+        font-family: cursive;
+        justify-content: center;
+        
+        }
+        .nav-item {
+            margin-right: 20px;
+            color: white;
+            font-size: 18px;
+            font-weight: bold;
+            text-decoration: none;
+            transition: all 0.3s ease-in-out; /* Animation de transition */
+        }
+        .nav-item:hover {
+            transform: translateY(-3px); /* Animation de légère élévation au survol */
+            cursor: pointer; /* Curseur pointeur au survol */
+        }
+        
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+    st.markdown(
+        """
+        <script>
+        function change_contenu(option) {
+            const choix = option.toLowerCase();
+            const elements = document.getElementsByClassName('nav-item');
+            for (let i = 0; i < elements.length; i++) {
+                elements[i].classList.remove('selected');
+            }
+            document.getElementById(option).classList.add('selected');
+            const contenu = document.getElementById('contenu');
+            fetch_data(choix);
+        }
+
+        function fetch_data(choix) {
+            const descriptions = {
+                ChatBot: "Description de l'onglet ChatBot",
+                Text2Speech: "Description de l'onglet Text2Speech",
+                Translation: "Description de l'onglet Translation"
+            };
+            const description = descriptions[choix];
+            document.getElementById('description').innerText = description;
+        }
+        
+        </script>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Fonction pour afficher la barre de description verticale
+def afficher_barre_description(description):
+    st.sidebar.title("Description")
+    st.sidebar.write(description)
+
+# Fonction principale pour afficher l'application
+def main():
+    afficher_barre_menu()
+    st.sidebar.header("Principal")
+    choix = st.sidebar.selectbox("Choose your option ",["ChatBot", "Text2Speech", "Translation"])
+
+    if choix == "ChatBot":
+        afficher_barre_description("This section features a chatbot inspired by Mistral-7B-Instruct-v0.2. Which is able to respond with a high level of expertise NB: for more precision put your text between quote.")
+        afficher_ChatBot()
+        
+    elif choix == "Text2Speech":
+        afficher_barre_description("Enter the text you wish to convert to speech in the text box below, then click the button to listen to the generated speech.")
+        afficher_Text2Speech()
+       
+    elif choix == "Translation":
+        afficher_barre_description("The Hellsinki model is used to translate Korean and Spanish texts into French.")
+        afficher_Translation()
+        
+# Fonctions pour afficher le contenu
+def afficher_ChatBot():
+    st.title("Interactive Chatbot ")
+    st.write("Welcome all of you.")
+    
+    # Définir le modèle et le tokenizer
+    
+    API_URL =  "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+    headers = {"Authorization":"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"}
+    
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+    
+    # Envoyer le message lorsque l'utilisateur appuie sur Entrée
+    messages = st.container(height = 300)
+    if user_input := st.chat_input("Say something"):
+        output = query({
+	"inputs": user_input
+	
+})
+        messages.chat_message('user').write(user_input)
+        
+        with st.spinner('Loading....'):
+            messages.chat_message("assistant")
+            messages.write(output[0]['generated_text'])
+
+
+
+    
+def afficher_Text2Speech():
+    st.title("Text2Speech")
+    API_URL = "https://api-inference.huggingface.co/models/facebook/mms-tts-eng"
+    headers = {"Authorization": "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"}
+
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.content
+
+    texte_input = st.text_input("Enter your speech")
+    
+    if st.button('Conversion'):
+        with st.spinner("Converting..."):
+            audio_bytes = query({"inputs": texte_input, })
+           
+            md = f"""
+            <audio autoplay="true">
+            <source src="data:audio/wav;base64" type="audio/mp3">
+            </audio>
+            """
+            st.markdown(
+            md,
+            unsafe_allow_html=True,
+        )
+            st.audio(audio_bytes, format="audio/mpeg", loop=True)
+            
+def afficher_Translation():
+  langues = {
+    "Korean - French": "Helsinki-NLP/opus-mt-ko-fr",
+    "Espagnol - French": "Helsinki-NLP/opus-mt-es-fr"
+}
+  st.title("Translator")
+  with st.container(height=500):
+  
+    langue_source = st.selectbox("Source :", list(langues.keys()))
+    phrase = st.text_input("Enter the sentence to be translated :") 
+    if st.button("Translate"):
+            # Récupérer les modèles correspondants aux langues sélectionnées
+        modele_source = langues[langue_source]
+    
+            # Appeler l'API pour traduire la phrase
+        resultat = traduire(phrase, modele_source)
+
+            # Afficher le résultat de la traduction
+        st.markdown("** The translation :**")
+        st.write(resultat[0]['translation_text'])
+
+# Fonction pour appeler l'API de traduction
+def traduire(phrase, modele_source):
+    API_URL = f"https://api-inference.huggingface.co/models/{modele_source}"
+    headers = {"Authorization": "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"}
+    payload = {"inputs": phrase}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    
+    return response.json()
+
+# Appel de la fonction principale
+if __name__ == "__main__":
+    main()

package-lock.json

@@ -0,0 +1,395 @@
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+}

package.json

@@ -0,0 +1,5 @@
+{
+  "dependencies": {
+    "streamlit-component-lib": "^2.0.0"
+  }
+}