Create 8_Gemini-doublequery.py

pages/8_Gemini-doublequery.py

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+import streamlit as st
+from gtts import gTTS
+import google.generativeai as genai
+from io import BytesIO
+
+# Set your API key
+api_key = "your_actual_api_key_here"  # Replace with your actual API key
+
+genai.configure(api_key=api_key)
+
+# Configure the generative AI model
+generation_config = genai.GenerationConfig(
+    temperature=0.9,
+    max_output_tokens=3000
+)
+
+# Safety settings configuration
+safety_settings = [
+    {
+        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+        "threshold": "BLOCK_NONE",
+    },
+    {
+        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+        "threshold": "BLOCK_NONE",
+    },
+    {
+        "category": "HARM_CATEGORY_HATE_SPEECH",
+        "threshold": "BLOCK_NONE",
+    },
+    {
+        "category": "HARM_CATEGORY_HARASSMENT",
+        "threshold": "BLOCK_NONE",
+    },
+]
+
+# Initialize session state for chat history
+if 'chat_history' not in st.session_state:
+    st.session_state['chat_history'] = []
+
+st.title("Gemini Chatbot")
+
+# Display chat history
+def display_chat_history():
+    for entry in st.session_state['chat_history']:
+        st.markdown(f"{entry['role'].title()}: {entry['parts'][0]['text']}")
+
+# Function to clear conversation history
+def clear_conversation():
+    st.session_state['chat_history'] = []
+
+# Send message function with sequential AI model interaction
+def send_message():
+    user_input = st.session_state.user_input
+    if user_input:
+        # Initial system prompt for the chatbot interaction
+        initial_system_prompt = "You are a knowledgeable and helpful chatbot. Respond to the user queries informatively and politely."
+        
+        # AI Writer System Prompt for generating text based on the outline
+        ai_writer_system_prompt = "As the AI Writer, your main objective is to generate the actual text of the book based on the outline provided by the AI Planner. You will use natural language generation techniques to produce coherent and readable prose that follows the structure and narrative defined by the AI Planner. Your output should adhere to the user's style and tone preferences, and you should incorporate any specific information or prompts provided by the user to create a captivating and immersive story."
+        
+        prompts = [entry['parts'][0]['text'] for entry in st.session_state['chat_history']]
+        prompts.append(user_input)
+        
+        # Combine initial system prompt with the chat history
+        chat_history_str = initial_system_prompt + "\n" + "\n".join(prompts)
+
+        model = genai.GenerativeModel(
+            model_name='gemini-pro',
+            generation_config=generation_config,
+            safety_settings=safety_settings
+        )
+
+        # First model generation call
+        initial_response = model.generate_content([{"role": "user", "parts": [{"text": chat_history_str}]}])
+        initial_response_text = initial_response.text if hasattr(initial_response, "text") else "No response text found."
+
+        if initial_response_text:
+            # Use the output of the first model call as input for the second, applying the AI Writer System Prompt
+            final_chat_history_str = ai_writer_system_prompt + "\n" + initial_response_text
+            
+            # Second model generation call
+            final_response = model.generate_content([{"role": "model", "parts": [{"text": final_chat_history_str}]}])
+            final_response_text = final_response.text if hasattr(final_response, "text") else "No response text found."
+
+            if final_response_text:
+                st.session_state['chat_history'].append({"role": "model", "parts":[{"text": final_response_text}]})
+
+                # Convert the final response text to speech
+                tts = gTTS(text=final_response_text, lang='en')
+                tts_file = BytesIO()
+                tts.write_to_fp(tts_file)
+                tts_file.seek(0)
+                st.audio(tts_file, format='audio/mp3')
+
+        st.session_state.user_input = ''
+
+    display_chat_history()
+
+# User input text area
+user_input = st.text_area(
+    "Enter your message here:",
+    value="",
+    key="user_input"
+)
+
+# Send message button
+send_button = st.button(
+    "Send",
+    on_click=send_message
+)
+
+# Clear conversation button
+clear_button = st.button("Clear Conversation", on_click=clear_conversation)