Upload 4 files

app.py

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+import os
+import streamlit as st
+import google.generativeai as gen_ai
+import pyttsx3
+import threading
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure Streamlit page settings
+st.set_page_config(
+    page_title="Gemini-Pro ChatBot",
+    page_icon="🤖",  # Favicon emoji
+    layout="centered",  # Page layout option
+)
+
+# Retrieve Google API Key
+Google_API_Key = os.getenv("Google_API_Key")
+
+# Set up Google Gemini-Pro AI Model
+gen_ai.configure(api_key=Google_API_Key)
+model = gen_ai.GenerativeModel('gemini-pro')
+
+# Function to translate roles between Gemini-Pro and Streamlit terminology
+def translate_role_for_streamlit(user_role):
+    return "assistant" if user_role == "model" else user_role
+
+# Function to handle text-to-speech (TTS) in a separate thread
+def speak_text(text):
+    engine = pyttsx3.init()
+    engine.say(text)
+    engine.runAndWait()
+
+# Initialize chat session in Streamlit if not already present
+if "chat_session" not in st.session_state:
+    st.session_state.chat_session = model.start_chat(history=[])
+
+# Display chatbot title and description
+st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+
+# Display chat history
+for message in st.session_state.chat_session.history:
+    with st.chat_message(translate_role_for_streamlit(message.role)):
+        st.markdown(message.parts[0].text)
+
+# User input field
+user_prompt = st.chat_input("Ask Gemini Pro...")
+
+# If user enters a prompt
+if user_prompt:
+    # Display user's message
+    st.chat_message("user").markdown(user_prompt)
+
+    # Show a loading indicator while waiting for a response
+    with st.spinner("Thinking..."):
+        gemini_response = st.session_state.chat_session.send_message(user_prompt)
+
+    # Display Gemini-Pro's response
+    with st.chat_message("assistant"):
+        st.markdown(gemini_response.text)
+
+    # Run text-to-speech in the background
+    threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+
+
+
+

chat_history.json

@@ -0,0 +1 @@
+[{"role": "user", "content": "hii"}, {"role": "assistant", "content": "Hello! How can I assist you today?"}, {"role": "user", "content": "what is machine learning"}, {"role": "assistant", "content": "**Machine Learning**\n\nMachine learning (ML) is a subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. It enables systems to identify patterns, make predictions, and solve problems by using data, rather than following predefined rules.\n\n**Key Concepts:**\n\n* **Data:** ML algorithms are trained on large datasets that contain relevant information to the task at hand.\n* **Features:** Data is transformed into numerical features that represent important characteristics.\n* **Model:** An ML model is a mathematical function that maps inputs (features) to outputs (predictions).\n* **Training:** The model is iteratively adjusted by minimizing a loss function that measures the difference between predicted and actual outputs.\n\n**Types of Machine Learning:**\n\n* **Supervised Learning:** Learns from labeled data, where each data point has a known output.\n* **Unsupervised Learning:** Learns from unlabeled data, discovering patterns and structures without guidance.\n* **Reinforcement Learning:** Learns through trial and error, receiving rewards or penalties based on its actions.\n\n**Applications:**\n\nMachine learning is widely used in various industries and applications, including:\n\n* **Natural Language Processing:** Text analysis, machine translation, chatbots\n* **Image Recognition:** Object detection, facial recognition, medical imaging\n* **Predictive Analytics:** Forecasting demand, stock market predictions, fraud detection\n* **Healthcare:** Disease diagnosis, drug discovery, personalized medicine\n* **Finance:** Risk assessment, trading algorithms, portfolio optimization\n\n**Advantages:**\n\n* **Automation:** Automates tasks that are time-consuming or complex for humans.\n* **Accuracy:** Models can achieve high accuracy and precision in predictions.\n* **Adaptability:** Models can be updated and improved over time as new data becomes available.\n* **Customization:** Models can be tailored to specific domains and applications.\n\n**Challenges:**\n\n* **Data Quality:** Poor quality or biased data can lead to inaccurate models.\n* **Overfitting:** Models that perform well on training data but poorly on new data.\n* **Interpretability:** Understanding the decision-making process of ML models can be difficult."}, {"role": "user", "content": "hii"}, {"role": "assistant", "content": "Hello there! How can I assist you today?"}, {"role": "user", "content": "Hii"}, {"role": "assistant", "content": "Hello! How can I help you today?"}, {"role": "user", "content": "Hii"}, {"role": "assistant", "content": "Hello there! How can I assist you today?"}]

main.py

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+# import os
+# import streamlit as st
+# from dotenv import load_dotenv
+# import google.generativeai as gen_ai
+#
+#
+# # Load environvent variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Chat with Gemini-Pro!",
+#     page_icon=":brain:", # Favicon emoji
+#     layout="centered", # Page layout option
+# )
+#
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemni-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Function to translate roles between Gemini-Pro and streamlit terminology
+#
+# def translate_role_for_streamlit(user_role):
+#     if user_role == 'model':
+#         return 'assistant'
+#     else: return user_role
+#
+# # Initialize chat session in streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display the chatbot's title on the page
+# st.title("🤖 Gemini Pro - ChatBot")
+#
+# # Display the chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+#
+# # Input field for user's message
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# if user_prompt:
+#     # Add user's message to chat and display it
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Send user's message to chat and display it
+#     gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message('assistant'):
+#         st.markdown(gemini_response.text)
+
+
+
+
+
+
+#
+# import os
+# import json
+# import streamlit as st
+# import google.generativeai as gen_ai
+# from dotenv import load_dotenv
+# import speech_recognition as sr
+# import pyttsx3
+#
+# # Load environment variables
+# load_dotenv()
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Chat with Gemini-Pro!",
+#     page_icon="🤖",
+#     layout="wide",
+# )
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Initialize chatbot memory
+# if "chat_history" not in st.session_state:
+#     try:
+#         with open("chat_history.json", "r") as f:
+#             st.session_state.chat_history = json.load(f)
+#     except FileNotFoundError:
+#         st.session_state.chat_history = []
+#
+#
+# # Save chat history
+# def save_chat_history():
+#     with open("chat_history.json", "w") as f:
+#         json.dump(st.session_state.chat_history, f)
+#
+#
+# # Sidebar settings
+# with st.sidebar:
+#     st.subheader("⚙️ Settings")
+#
+#     # Theme selection
+#     theme = st.radio("Select Theme", ["🌞 Light", "🌙 Dark"])
+#
+#     # Clear chat history button
+#     if st.button("🗑️ Clear Chat History"):
+#         st.session_state.chat_history = []
+#         save_chat_history()
+#         st.experimental_rerun()
+#
+# # Apply dark mode styling
+# if theme == "🌙 Dark":
+#     st.markdown(
+#         """
+#         <style>
+#         body {background-color: #333; color: white;}
+#         .stChatMessage {background-color: #444; color: white;}
+#         </style>
+#         """,
+#         unsafe_allow_html=True
+#     )
+#
+# # Display chatbot title
+# st.title("🤖 Gemini Pro - ChatBot")
+#
+# # Display chat history
+# for message in st.session_state.chat_history:
+#     with st.chat_message(message["role"]):
+#         st.markdown(message["content"])
+#
+# # Speech recognition & text-to-speech setup
+# recognizer = sr.Recognizer()
+# engine = pyttsx3.init()
+#
+# # Voice input button
+# if st.button("🎤 Speak"):
+#     with sr.Microphone() as source:
+#         st.write("Listening...")
+#         audio = recognizer.listen(source)
+#         try:
+#             user_prompt = recognizer.recognize_google(audio)
+#             st.chat_message("user").markdown(user_prompt)
+#         except:
+#             st.error("Could not understand. Try again.")
+#
+# # Text input
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# if user_prompt:
+#     # Add user message to chat history
+#     st.session_state.chat_history.append({"role": "user", "content": user_prompt})
+#
+#     # Display user message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Get response from Gemini-Pro
+#     gemini_response = model.generate_content(user_prompt)
+#
+#     # Add chatbot response to history
+#     st.session_state.chat_history.append({"role": "assistant", "content": gemini_response.text})
+#
+#     # Display chatbot response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Save chat history
+#     save_chat_history()
+#
+#     # Text-to-Speech response
+#     engine.say(gemini_response.text)
+#     engine.runAndWait()
+
+
+
+
+
+# import os
+# import json
+# import streamlit as st
+# import google.generativeai as gen_ai
+# from dotenv import load_dotenv
+# import speech_recognition as sr
+# import pyttsx3
+#
+# # Load environment variables
+# load_dotenv()
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Chat with Gemini-Pro!",
+#     page_icon="🤖",
+#     layout="wide",
+# )
+#
+# # Apply custom CSS for UI/UX improvements
+# st.markdown(
+#     """
+#     <style>
+#     .stChatMessage { padding: 10px; border-radius: 10px; margin: 5px 0; }
+#     .user { background-color: #DCF8C6; }
+#     .assistant { background-color: #E3E3E3; }
+#     .sidebar .sidebar-content { background-color: #f0f2f6; }
+#     .chat-container { max-width: 700px; margin: auto; }
+#     </style>
+#     """,
+#     unsafe_allow_html=True
+# )
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Initialize chatbot memory
+# if "chat_history" not in st.session_state:
+#     try:
+#         with open("chat_history.json", "r") as f:
+#             st.session_state.chat_history = json.load(f)
+#     except (FileNotFoundError, json.JSONDecodeError):
+#         st.session_state.chat_history = []
+#
+#
+# # Save chat history
+# def save_chat_history():
+#     with open("chat_history.json", "w") as f:
+#         json.dump(st.session_state.chat_history, f)
+#
+#
+# # Sidebar settings
+# with st.sidebar:
+#     st.subheader("⚙️ Settings")
+#
+#     # Theme selection
+#     theme = st.radio("Select Theme", ["🌞 Light", "🌙 Dark"])
+#
+#     # Clear chat history button
+#     if st.button("🗑️ Clear Chat History"):
+#         st.session_state.chat_history = []
+#         save_chat_history()
+#         st.experimental_rerun()
+#
+# # Apply dark mode styling
+# if theme == "🌙 Dark":
+#     st.markdown(
+#         """
+#         <style>
+#         body {background-color: #1e1e1e; color: white;}
+#         .stChatMessage {background-color: #444; color: white;}
+#         .sidebar .sidebar-content {background-color: #333;}
+#         .stTextInput, .stButton, .stRadio {background-color: #333; color: white;}
+#         </style>
+#         """,
+#         unsafe_allow_html=True
+#     )
+#
+# # Display chatbot title
+# st.title("🤖 Gemini Pro - AI ChatBot")
+#
+# # Chat container
+# st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_history:
+#     role = message["role"]
+#     avatar = "👤" if role == "user" else "🤖"
+#     bg_color = "user" if role == "user" else "assistant"
+#
+#     with st.chat_message(role):
+#         st.markdown(f'<div class="{bg_color} stChatMessage">{avatar} {message["content"]}</div>',
+#                     unsafe_allow_html=True)
+#
+# # Speech recognition & text-to-speech setup
+# recognizer = sr.Recognizer()
+# # engine = pyttsx3.init()
+# engine = pyttsx3.init()
+# engine.setProperty('rate', 150)  # Adjust speech rate
+# engine.setProperty('voice', engine.getProperty('voices')[0].id)  # Set a specific voice
+#
+# # Voice input button
+# if st.button("🎤 Speak"):
+#     with sr.Microphone() as source:
+#         st.write("Listening...")
+#         audio = recognizer.listen(source)
+#         try:
+#             user_prompt = recognizer.recognize_google(audio)
+#             st.session_state.chat_history.append({"role": "user", "content": user_prompt})
+#             st.chat_message("user").markdown(user_prompt)
+#             # Trigger chatbot response
+#             gemini_response = model.generate_content(user_prompt)
+#             st.session_state.chat_history.append({"role": "assistant", "content": gemini_response.text})
+#             st.chat_message("assistant").markdown(gemini_response.text)
+#             save_chat_history()
+#             engine.say(gemini_response.text)
+#             engine.runAndWait()
+#         except sr.UnknownValueError:
+#             st.error("Sorry, I could not understand the audio.")
+#         except sr.RequestError:
+#             st.error("Could not request results from the speech recognition service.")
+# # Text input
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# if user_prompt:
+#     # Add user message to chat history
+#     st.session_state.chat_history.append({"role": "user", "content": user_prompt})
+#
+#     # Display user message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Get response from Gemini-Pro
+#     # gemini_response = model.generate_content(user_prompt)
+#     try:
+#         gemini_response = model.generate_content(user_prompt)
+#     except Exception as e:
+#         st.error(f"An error occurred: {e}")
+#         gemini_response = type('Object', (), {'text': 'Sorry, I could not generate a response.'})
+#
+#     # Add chatbot response to history
+#     st.session_state.chat_history.append({"role": "assistant", "content": gemini_response.text})
+#
+#     # Display chatbot response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Save chat history
+#     save_chat_history()
+#
+#     # Text-to-Speech response
+#     engine.say(gemini_response.text)
+#     engine.runAndWait()
+#
+# st.markdown('</div>', unsafe_allow_html=True)
+#
+
+
+
+
+
+
+
+
+
+
+# import os
+# import streamlit as st
+# import google.generativeai as gen_ai
+# import pyttsx3
+# import threading
+# from dotenv import load_dotenv
+#
+# # Load environment variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Gemini-Pro ChatBot",
+#     page_icon="🤖",  # Favicon emoji
+#     layout="centered",  # Page layout option
+# )
+#
+# # Retrieve Google API Key
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Function to translate roles between Gemini-Pro and Streamlit terminology
+# def translate_role_for_streamlit(user_role):
+#     return "assistant" if user_role == "model" else user_role
+#
+# # Function to handle text-to-speech (TTS) in a separate thread
+# def speak_text(text):
+#     engine = pyttsx3.init()
+#     engine.say(text)
+#     engine.runAndWait()
+#
+# # Initialize chat session in Streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display chatbot title and description
+# st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+# # User input field
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# # If user enters a prompt
+# if user_prompt:
+#     # Display user's message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Show a loading indicator while waiting for a response
+#     with st.spinner("Thinking..."):
+#         gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Run text-to-speech in the background
+#     threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+
+
+
+
+
+
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+# import os
+# import streamlit as st
+# import google.generativeai as gen_ai
+# import pyttsx3
+# import threading
+# from dotenv import load_dotenv
+#
+# # Load environment variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Gemini-Pro ChatBot",
+#     page_icon="🤖",
+#     layout="centered",
+# )
+#
+# # Retrieve Google API Key
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Function to translate roles between Gemini-Pro and Streamlit terminology
+# def translate_role_for_streamlit(user_role):
+#     return "assistant" if user_role == "model" else user_role
+#
+# # Initialize text-to-speech engine
+# if "tts_engine" not in st.session_state:
+#     st.session_state.tts_engine = pyttsx3.init()
+#
+# def stop_speech():
+#     """Stop the current speech if running."""
+#     st.session_state.tts_engine.stop()
+#
+# def speak_text(text):
+#     """Stop previous speech and start speaking new text."""
+#     stop_speech()  # Stop any ongoing speech
+#     st.session_state.tts_engine.say(text)
+#     st.session_state.tts_engine.runAndWait()
+#
+# # Initialize chat session in Streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display chatbot title and description
+# st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+# # User input field
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# # If user enters a prompt
+# if user_prompt:
+#     # Display user's message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Show a loading indicator while waiting for a response
+#     with st.spinner("Thinking..."):
+#         gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Run text-to-speech in the background (stopping previous speech first)
+#     threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+
+
+# import os
+# import streamlit as st
+# import google.generativeai as gen_ai
+# import pyttsx3
+# import threading
+# from dotenv import load_dotenv
+#
+# # Load environment variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Gemini-Pro ChatBot",
+#     page_icon="🤖",
+#     layout="centered",
+# )
+#
+# # Retrieve Google API Key
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Function to translate roles between Gemini-Pro and Streamlit terminology
+# def translate_role_for_streamlit(user_role):
+#     return "assistant" if user_role == "model" else user_role
+#
+# # Initialize text-to-speech engine
+# if "tts_engine" not in st.session_state:
+#     st.session_state.tts_engine = pyttsx3.init()
+#
+# # Initialize threading event for speech control
+# if "speech_event" not in st.session_state:
+#     st.session_state.speech_event = threading.Event()
+#
+# def stop_speech():
+#     """Stop the current speech if running."""
+#     st.session_state.speech_event.set()  # Set the event to stop speech
+#     st.session_state.tts_engine.stop()
+#
+# def speak_text(text):
+#     """Stop previous speech and start speaking new text."""
+#     stop_speech()  # Stop any ongoing speech
+#     st.session_state.speech_event.clear()  # Clear the event for new speech
+#     st.session_state.tts_engine.say(text)
+#     st.session_state.tts_engine.runAndWait()
+#
+# # Initialize chat session in Streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display chatbot title and description
+# st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+# # User input field
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# # If user enters a prompt
+# if user_prompt:
+#     # Display user's message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Show a loading indicator while waiting for a response
+#     with st.spinner("Thinking..."):
+#         gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Run text-to-speech in the background (stopping previous speech first)
+#     threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+
+
+
+
+
+
+#
+# import os
+# import streamlit as st
+# import google.generativeai as gen_ai
+# import pyttsx3
+# import threading
+# from dotenv import load_dotenv
+#
+# # Load environment variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Gemini-Pro ChatBot",
+#     page_icon="🤖",
+#     layout="centered",
+# )
+#
+# # Retrieve Google API Key
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Function to translate roles between Gemini-Pro and Streamlit terminology
+# def translate_role_for_streamlit(user_role):
+#     return "assistant" if user_role == "model" else user_role
+#
+# # Initialize text-to-speech engine
+# if "tts_engine" not in st.session_state:
+#     st.session_state.tts_engine = pyttsx3.init()
+#
+# def stop_speech():
+#     """Stop the current speech if running."""
+#     st.session_state.tts_engine.stop()
+#
+# def speak_text(text):
+#     """Stop previous speech and start speaking new text."""
+#     stop_speech()  # Stop any ongoing speech
+#     st.session_state.tts_engine.say(text)
+#     st.session_state.tts_engine.runAndWait()
+#
+# # Initialize chat session in Streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display chatbot title and description
+# st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+# # User input field
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# # If user enters a prompt
+# if user_prompt:
+#     # Display user's message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Show a loading indicator while waiting for a response
+#     with st.spinner("Thinking..."):
+#         gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Run text-to-speech in the background (stopping previous speech first)
+#     threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+#
+#
+#
+#
+
+
+
+
+
+
+
+
+
+
+
+
+
+#
+#
+# import os
+# import streamlit as st
+# import google.generativeai as gen_ai
+# import pyttsx3
+# import threading
+# from dotenv import load_dotenv
+# import speech_recognition as sr
+#
+# # Load environment variables
+# load_dotenv()
+#
+# # Configure Streamlit page settings
+# st.set_page_config(
+#     page_title="Gemini-Pro ChatBot",
+#     page_icon="🤖",  # Favicon emoji
+#     layout="centered",  # Page layout option
+# )
+#
+# # Retrieve Google API Key
+# Google_API_Key = os.getenv("Google_API_Key")
+#
+# # Set up Google Gemini-Pro AI Model
+# gen_ai.configure(api_key=Google_API_Key)
+# model = gen_ai.GenerativeModel('gemini-pro')
+#
+# # Initialize text-to-speech engine
+# if "tts_engine" not in st.session_state:
+#     st.session_state.tts_engine = pyttsx3.init()
+#
+# # Speech-to-text function
+# def listen_for_input():
+#     recognizer = sr.Recognizer()
+#     with sr.Microphone() as source:
+#         print("Listening...")
+#         audio = recognizer.listen(source)
+#         try:
+#             return recognizer.recognize_google(audio)
+#         except sr.UnknownValueError:
+#             return "Sorry, I did not catch that."
+#         except sr.RequestError:
+#             return "Sorry, there was an error with the speech recognition service."
+#
+# # Stop previous speech and speak the new text
+# def speak_text(text):
+#     stop_speech()  # Stop any ongoing speech
+#     st.session_state.tts_engine.say(text)
+#     st.session_state.tts_engine.runAndWait()
+#
+# # Stop ongoing speech
+# def stop_speech():
+#     st.session_state.tts_engine.stop()
+#
+# # Function to translate roles between Gemini-Pro and Streamlit terminology
+# def translate_role_for_streamlit(user_role):
+#     return "assistant" if user_role == "model" else user_role
+#
+# # Initialize chat session in Streamlit if not already present
+# if "chat_session" not in st.session_state:
+#     st.session_state.chat_session = model.start_chat(history=[])
+#
+# # Display chatbot title and description
+# st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+#
+# # Display chat history
+# for message in st.session_state.chat_session.history:
+#     with st.chat_message(translate_role_for_streamlit(message.role)):
+#         st.markdown(message.parts[0].text)
+#
+# # User input field (with optional speech-to-text input)
+# user_prompt = st.chat_input("Ask Gemini Pro...")
+#
+# if st.button("Use Voice Input"):
+#     user_prompt = listen_for_input()
+#
+# # If user enters a prompt
+# if user_prompt:
+#     # Display user's message
+#     st.chat_message("user").markdown(user_prompt)
+#
+#     # Show a loading indicator while waiting for a response
+#     with st.spinner("Thinking..."):
+#         gemini_response = st.session_state.chat_session.send_message(user_prompt)
+#
+#     # Display Gemini-Pro's response
+#     with st.chat_message("assistant"):
+#         st.markdown(gemini_response.text)
+#
+#     # Run text-to-speech in the background
+#     threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()
+
+
+
+
+
+
+
+
+import os
+import streamlit as st
+import google.generativeai as gen_ai
+import pyttsx3
+import threading
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure Streamlit page settings
+st.set_page_config(
+    page_title="Gemini-Pro ChatBot",
+    page_icon="🤖",  # Favicon emoji
+    layout="centered",  # Page layout option
+)
+
+# Retrieve Google API Key
+Google_API_Key = os.getenv("Google_API_Key")
+
+# Set up Google Gemini-Pro AI Model
+gen_ai.configure(api_key=Google_API_Key)
+model = gen_ai.GenerativeModel('gemini-pro')
+
+# Function to translate roles between Gemini-Pro and Streamlit terminology
+def translate_role_for_streamlit(user_role):
+    return "assistant" if user_role == "model" else user_role
+
+# Initialize the TTS engine if not already present in session state
+if "tts_engine" not in st.session_state:
+    st.session_state.tts_engine = pyttsx3.init()
+
+# Function to stop speech if any ongoing speech is happening
+def stop_speech():
+    if hasattr(st.session_state, "tts_engine"):
+        st.session_state.tts_engine.stop()
+
+# Function to handle text-to-speech (TTS) in a separate thread
+def speak_text(text):
+    try:
+        stop_speech()  # Stop any ongoing speech
+        st.session_state.tts_engine.say(text)
+        st.session_state.tts_engine.runAndWait()
+    except Exception as e:
+        st.error(f"Error in TTS: {e}")
+
+# Initialize chat session in Streamlit if not already present
+if "chat_session" not in st.session_state:
+    st.session_state.chat_session = model.start_chat(history=[])
+
+# Display chatbot title and description
+st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🤖 Gemini-Pro ChatBot</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center; font-size: 16px;'>Ask me anything! I'm powered by Gemini-Pro AI.</p>", unsafe_allow_html=True)
+
+# Display chat history
+for message in st.session_state.chat_session.history:
+    with st.chat_message(translate_role_for_streamlit(message.role)):
+        st.markdown(message.parts[0].text)
+
+# User input field
+user_prompt = st.chat_input("Ask Gemini Pro...")
+
+# If user enters a prompt
+if user_prompt:
+    # Display user's message
+    st.chat_message("user").markdown(user_prompt)
+
+    # Show a loading indicator while waiting for a response
+    with st.spinner("Thinking..."):
+        gemini_response = st.session_state.chat_session.send_message(user_prompt)
+
+    # Display Gemini-Pro's response
+    with st.chat_message("assistant"):
+        st.markdown(gemini_response.text)
+
+    # Run text-to-speech in the background
+    threading.Thread(target=speak_text, args=(gemini_response.text,), daemon=True).start()

requirements.txt

@@ -0,0 +1,2 @@
+streamlit~=1.36.0
+python-dotenv~=1.0.1