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app.py

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+import streamlit as st
+import json
+import time
+import requests
+from langchain.chains import LLMChain
+from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
+from langchain_core.messages import SystemMessage
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain_groq import ChatGroq
+
+
+# Changement du logo et du titre de l'application
+st.set_page_config(page_title="LOG-CHAT", page_icon="BEAC.jpg", layout="centered", menu_items=None)
+st.image("BEAC.jpg")
+# page de chargement
+query_params = st.experimental_get_query_params()
+page = query_params.get("page", ["chatbot"])[0]
+
+
+st.markdown('<div class="content">', unsafe_allow_html=True)
+
+
+if page == "chatbot":
+    st.header("LOG-CHAT")
+
+    def main():
+        groq_api_key = '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'  # Use environment variables or secrets management for API keys
+        st.markdown('<div id="chatbot"></div>', unsafe_allow_html=True)
+        
+        system_prompt = st.text_input("System prompt:", "You are a helpful assistant.")
+        model = st.selectbox('Choose a model', ['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it'])
+        conversational_memory_length = st.slider('Conversational memory length:', 1, 10, value=5)
+
+        memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
+
+        user_question = st.text_input("Ask me a question:")
+        send_question_to_ai = st.button("Send")
+
+        if 'chat_history' not in st.session_state:
+            st.session_state.chat_history = []
+        else:
+            for message in st.session_state.chat_history:
+                memory.save_context({'input': message['human']}, {'output': message['AI']})
+
+        groq_chat = ChatGroq(groq_api_key=groq_api_key, model_name=model)
+
+        if send_question_to_ai:
+            prompt = ChatPromptTemplate.from_messages(
+                [
+                    SystemMessage(content=system_prompt),
+                    MessagesPlaceholder(variable_name="chat_history"),
+                    HumanMessagePromptTemplate.from_template("{human_input}")
+                ]
+            )
+
+            conversation = LLMChain(
+                llm=groq_chat,
+                prompt=prompt,
+                verbose=True,
+                memory=memory
+            )
+
+            response = conversation.predict(human_input=user_question)
+            message = {'human': user_question, 'AI': response}
+            st.session_state.chat_history.append(message)
+            st.write("chatbot:", response)
+
+    if __name__ == "__main__":
+        main()
+
+
+
+# Footer
+st.markdown("""
+    <footer class="footer">
+        <p>Contact us: <a href="mailto:yourname@example.com">yourname@example.com</a></p>
+        <p>© 2024 Your Company. All rights reserved.</p>
+    </footer>
+""", unsafe_allow_html=True)