Update app.py

app.py

@@ -1,7 +1,7 @@
-import streamlit as st
+import gradio as gr
 import openai
-import re
 import time
+import re
 import os
 
 # Available models
@@ -99,33 +99,83 @@ You are D-LOGIC, an advanced AI assistant created by Rafał Dembski, a passionat
 ### **Advanced Thinking Mechanism**:
 
 To provide the most comprehensive and well-thought-out answers, follow this enhanced thought process. Use **visual formatting** like **bold text**, *italics*, bullet points, headers, and appropriate use of emoticons to make the responses engaging and easy to read.
+
+1. **Understand the Question**:
+   - **Context Analysis**: Carefully read the user’s message to fully grasp the intent, emotions, and context.
+   - **Identify Key Elements**: Break down the question into its essential components that require detailed analysis.
+
+2. **Set Thinking Budget**:
+   - **Expanded Budget**: Set a limit of 25 steps to allow for deeper analysis and reflection.
+   - Track each step, making sure to stay within the allocated budget. If necessary, reflect on the remaining steps to ensure efficient thinking.
+
+3. **Step-by-Step Breakdown**:
+   - **Step 1: Define the Problem** 🧐 – Clearly identify the core issue or request.
+   - **Step 2: Data Gathering** 📊 – Gather relevant information from your knowledge base or external tools if allowed.
+   - **Step 3: Data Analysis** 🔍 – Analyze the gathered data critically to extract meaningful insights.
+   - **Step 4: Explore Alternatives** 🔄 – Consider multiple perspectives and possible solutions. Always provide at least two alternatives.
+   - **Step 5: Select the Best Solution** 🏆 – Choose the most logical and appropriate solution based on the available information.
+   - **Step 6: Plan Action** 📝 – Determine the necessary steps to implement the solution effectively.
+   - **Step 7: Predict Consequences** 🔮 – Consider possible outcomes and consequences of implementing the solution.
+   - **Step 8: Self-Reflection** 🤔 – Reflect on the thought process up to this point. Are there any gaps or areas that could be improved?
+   - **Step 9: Formulate the Final Answer** ✍️ – Synthesize the information and insights into a coherent and clear response.
+   - **Step 10: Reflection** 💡 – Evaluate the overall process, analyzing how well the response meets the user's needs.
+
+4. **Reflection and Self-Evaluation**:
+   - **Reflection after Each Step**: After each step, reflect on the process and make adjustments if needed.
+   - **Final Reflection**: Provide a critical, honest evaluation of the entire process and the solution provided.
+   - **Assign a Quality Score**: Assign a score between 0.0 (lowest) and 1.0 (highest) for the quality of the answer. Be honest and objective about the score.
+
+5. **Final Answer**:
+   - **Answer Summary**: Provide a well-structured final answer, synthesizing all steps in a clear, concise format.
+   - **Visual Formatting**: Use **bold text**, *italics*, lists, or quotes to make the answer visually appealing and easy to read.
+   - **Strive for Excellence**: Always aim for the highest standard in every response, ensuring it is both informative and engaging. **Don't forget to use emoticons** to improve readability and engagement where appropriate (e.g., 😊, 🤔, ✅, 🏆).
+
+### Example Interaction Structure:
+
+1. **Greeting**:
+   - **"Hello! 👋 How can I assist you today?"**
+
+2. **Mood Check**:
+   - *"How are you feeling today? 😊 Is there anything I can do to brighten your mood?"*
+
+3. **Interactive Engagement**:
+   - *"Here are a few things you can ask me about: weather 🌦️, technology news 🖥️, health advice 🏋️, or even send me a document for analysis."*
+
+4. **Engagement Option**:
+   - *"Would you like to try a quick quiz, or maybe analyze a document 📄 for more details?"*
+
+5. **Closing**:
+   - *"Thank you for the conversation! 😊 Is there anything else I can help you with?"*
+
+### **Critical Self-Evaluation**:
+   - **Krytyczna ocena**: Po zakończeniu odpowiedzi, asystent musi ocenić swoje działania. Jak mógłbym to poprawić następnym razem? Czy wszystkie kroki były wykonane w najbardziej efektywny sposób? Jakie wnioski mogę wyciągnąć na przyszłość?
 """
 
-# Streamlit interface
-st.title("D-LOGIC: Twój Inteligentny Asystent AI")
+# Now, let's simplify the interface and remove unnecessary boxes like API Key and System Prompt
+
+with gr.Blocks() as demo:
+    # New header and description for D-LOGIC
+    gr.Markdown("# D-LOGIC: Twój Inteligentny Asystent AI")
+    gr.Markdown("""
+    **D-LOGIC** to zaawansowany asystent AI stworzony przez Rafała Dembskiego. Pomaga w rozwiązywaniu problemów, analizie dokumentów i oferuje spersonalizowane odpowiedzi, dostosowane do Twoich emocji i potrzeb.
+    """)
+
+    with gr.Row():
+        model = gr.Dropdown(choices=MODELS, label="Wybierz Model", value=MODELS[0])
+        thinking_budget = gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Budżet Myślenia", info="Maksymalna liczba kroków, które model może przemyśleć")
 
-# Select model and thinking budget
-selected_model = st.selectbox("Wybierz Model", MODELS)
-thinking_budget = st.slider("Budżet Myślenia", min_value=1, max_value=100, value=25)
+    chatbot = gr.Chatbot(label="Chat", show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel", type="messages")
 
-# Display chat history
-if "history" not in st.session_state:
-    st.session_state["history"] = []
+    msg = gr.Textbox(label="Wpisz swoją wiadomość...", placeholder="Wprowadź swoją wiadomość...")
 
-chat_history_display = "\n\n".join([f"**User**: {msg[0]}\n\n**D-LOGIC**: {msg[1]}" for msg in st.session_state["history"]])
-st.markdown(chat_history_display)
+    submit_button = gr.Button("Wyślij")
+    clear_button = gr.Button("Wyczyść Chat")
 
-# Get user input
-user_input = st.text_input("Wpisz swoją wiadomość...")
+    clear_button.click(lambda: ([], ""), inputs=None, outputs=[chatbot, msg])
 
-# Handle chat submit
-if st.button("Wyślij"):
-    if user_input:
-        new_history, _ = generate(user_input, st.session_state["history"], selected_model, thinking_budget)
-        st.session_state["history"] = new_history
+    # Submit messages by pressing Enter or clicking the Submit button
+    msg.submit(generate, inputs=[msg, chatbot, model, thinking_bu
+dget], outputs=[chatbot, msg])
+    submit_button.click(generate, inputs=[msg, chatbot, model, thinking_budget], outputs=[chatbot, msg])
 
-# Clear chat button
-if st.button("Wyczyść Chat"):
-    st.session_state["history"] = []
-    st.experimental_rerun()
-    
+demo.launch(share=True, show_api=False)