Create app.py

app.py

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+#Advanced User Interface
+import gradio as gr
+import openai
+
+# Set OpenAI API Key
+openai.api_key = "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"
+openai.api_base = "https://api.groq.com/openai/v1"
+
+# Dictionary to store categorized chats
+saved_chats = {
+    "Stress Management": [],
+    "Career Advice": [],
+    "General": []
+}
+
+# Function to get response from GROQ API
+def get_groq_response(message):
+    try:
+        response = openai.ChatCompletion.create(
+            model="llama-3.1-70b-versatile",
+            messages=[
+                {"role": "user", "content": message},
+                {"role": "system", "content": "You will talk like a Motivational Speaker to help people come out of stress."}
+            ]
+        )
+        return response.choices[0].message["content"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Function to classify messages based on the topic
+def classify_message(user_message, bot_response):
+    if "stress" in user_message.lower():
+        saved_chats["Stress Management"].append((user_message, bot_response))
+        return "Stress Management"
+    elif "career" in user_message.lower():
+        saved_chats["Career Advice"].append((user_message, bot_response))
+        return "Career Advice"
+    else:
+        saved_chats["General"].append((user_message, bot_response))
+        return "General"
+
+# Chatbot function
+def chatbot(user_input, history=[]):
+    bot_response = get_groq_response(user_input)  # Get bot response
+    topic = classify_message(user_input, bot_response)  # Classify the message
+    history.append((f"({topic}) You: {user_input}", f"Motivator Bot: {bot_response}"))  # Update conversation history
+    return history, saved_chats  # Return history and saved chats
+
+# Gradio Interface setup
+def display_saved_chats():
+    # Format saved chats for display
+    display = ""
+    for category, chats in saved_chats.items():
+        display += f"### {category} Chats\n"
+        for user_message, bot_response in chats:
+            display += f"- **You**: {user_message}\n  - **Motivator Bot**: {bot_response}\n\n"
+    return display.strip()
+
+# Customized Gradio Interface
+chat_interface = gr.Blocks(theme="dark")
+
+with chat_interface:
+    gr.Markdown("## 🚀 **Motivational Chatbot**\n\n**Feeling Stressed? Let's Talk!**\n\nThis chatbot is designed to help you relieve stress and find motivation. Ask anything, share your worries, or simply seek some words of encouragement!")
+    chatbot_output = gr.Chatbot(label="Motivator Bot")
+    with gr.Row():
+        user_input = gr.Textbox(label="Your Message", placeholder="Type your message...")
+        send_button = gr.Button("Send")
+    saved_chats_display = gr.Markdown(label="Saved Chats")
+    refresh_button = gr.Button("Refresh Saved Chats")
+
+    # Chat interaction
+    def handle_interaction(user_input, history):
+        if not user_input.strip():
+            return history, display_saved_chats()
+        updated_history, _ = chatbot(user_input, history)
+        return updated_history, display_saved_chats()
+
+    send_button.click(handle_interaction, inputs=[user_input, chatbot_output], outputs=[chatbot_output, saved_chats_display])
+    refresh_button.click(display_saved_chats, inputs=[], outputs=saved_chats_display)
+
+# Launch the Interface
+if __name__ == "__main__":
+    chat_interface.launch(debug=True)