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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/gemini-chatbot.gif filter=lfs diff=lfs merge=lfs -text

.streamlit/config.toml

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+[theme]
+primaryColor="#BEB09E"
+backgroundColor="#F6F6F3"
+secondaryBackgroundColor="#D6D6D6"
+textColor="#335095"
+font="serif"
+# 335095

LICENSE

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Readme.md

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+# Gemini Chatbot Interface with Streamlit
+
+## Overview
+
+This project is a Streamlit-based chat application that interacts with the Gemini AI model, allowing users to engage in conversations with an artificial intelligence assistant. The application stores chat history, allowing users to revisit and continue previous conversations.
+
+<div align="center"><img src="docs/gemini-chatbot.gif" width="800"></div>
+
+## Getting Started
+
+### Dependencies
+
+This code uses the following libraries:
+
+- `streamlit`: for building the user interface. 
+- `gemini`: for chat  
+- Gemini API key: Get it from [Google AI Studio](https://ai.google.dev/tutorials/setup?hl=tr)
+
+
+### Usage
+
+Follow these steps to set up and run the project:
+
+1. Create a virtual environment:
+```
+python3 -m venv my_env
+source my_env/bin/activate 
+.\my_env\Scripts\activate 
+```
+
+2. Install dependencies:
+```
+pip install -r requirements.txt
+```
+
+3. Run the Streamlit server:
+```
+streamlit run app_chat.py
+```
+
+4. Access the application in your browser at http://localhost:8501.
+
+5. Start chatting with the assistant!
+
+## Repository Structure
+```
+repository/
+├── app_chat.py               # the code and UI integrated together live here
+├── requirements.txt     # the python packages needed to run locally
+├── .streamlit/
+│   └── config.toml      # theme info for the UI
+├── data/                # folder for saved chat messages 
+├── docs/                # preview for github
+
+```
+
+## How it Works
+
+The app as follows:
+
+1. The user enters a question in the input field.
+
+2. User messages are sent to the Gemini model for processing.
+
+3. The user's input, along with the chat history, is used to generate a response.
+
+4. The Gemini model generates a response based on the patterns it learned during training.
+
+5. The application saves chat messages and Gemini AI chat history to files for later retrieval.
+
+6. A new chat is created if the user initiates a conversation that hasn't been stored before, or user can go back to past chats.

app_chat.py

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+import time
+import os
+import joblib
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+load_dotenv()
+GOOGLE_API_KEY=os.environ.get('GOOGLE_API_KEY')
+genai.configure(api_key=GOOGLE_API_KEY)
+
+new_chat_id = f'{time.time()}'
+MODEL_ROLE = 'ai'
+AI_AVATAR_ICON = '✨'
+
+# Create a data/ folder if it doesn't already exist
+try:
+    os.mkdir('data/')
+except:
+    # data/ folder already exists
+    pass
+
+# Load past chats (if available)
+try:
+    past_chats: dict = joblib.load('data/past_chats_list')
+except:
+    past_chats = {}
+
+# Sidebar allows a list of past chats
+with st.sidebar:
+    st.write('# Past Chats')
+    if st.session_state.get('chat_id') is None:
+        st.session_state.chat_id = st.selectbox(
+            label='Pick a past chat',
+            options=[new_chat_id] + list(past_chats.keys()),
+            format_func=lambda x: past_chats.get(x, 'New Chat'),
+            placeholder='_',
+        )
+    else:
+        # This will happen the first time AI response comes in
+        st.session_state.chat_id = st.selectbox(
+            label='Pick a past chat',
+            options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
+            index=1,
+            format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
+            placeholder='_',
+        )
+    # Save new chats after a message has been sent to AI
+    # TODO: Give user a chance to name chat
+    st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
+
+st.write('# Chat with Gemini')
+
+# Chat history (allows to ask multiple questions)
+try:
+    st.session_state.messages = joblib.load(
+        f'data/{st.session_state.chat_id}-st_messages'
+    )
+    st.session_state.gemini_history = joblib.load(
+        f'data/{st.session_state.chat_id}-gemini_messages'
+    )
+    print('old cache')
+except:
+    st.session_state.messages = []
+    st.session_state.gemini_history = []
+    print('new_cache made')
+st.session_state.model = genai.GenerativeModel('gemini-2.0-flash')
+st.session_state.chat = st.session_state.model.start_chat(
+    history=st.session_state.gemini_history,
+)
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(
+        name=message['role'],
+        avatar=message.get('avatar'),
+    ):
+        st.markdown(message['content'])
+
+# React to user input
+if prompt := st.chat_input('Your message here...'):
+    # Save this as a chat for later
+    if st.session_state.chat_id not in past_chats.keys():
+        past_chats[st.session_state.chat_id] = st.session_state.chat_title
+        joblib.dump(past_chats, 'data/past_chats_list')
+    # Display user message in chat message container
+    with st.chat_message('user'):
+        st.markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append(
+        dict(
+            role='user',
+            content=prompt,
+        )
+    )
+    ## Send message to AI
+    response = st.session_state.chat.send_message(
+        prompt,
+        stream=True,
+    )
+    # Display assistant response in chat message container
+    with st.chat_message(
+        name=MODEL_ROLE,
+        avatar=AI_AVATAR_ICON,
+    ):
+        message_placeholder = st.empty()
+        full_response = ''
+        assistant_response = response
+        # Streams in a chunk at a time
+        for chunk in response:
+            # Simulate stream of chunk
+            # TODO: Chunk missing `text` if API stops mid-stream ("safety"?)
+            for ch in chunk.text.split(' '):
+                full_response += ch + ' '
+                time.sleep(0.05)
+                # Rewrites with a cursor at end
+                message_placeholder.write(full_response + '▌')
+        # Write full message with placeholder
+        message_placeholder.write(full_response)
+
+    # Add assistant response to chat history
+    st.session_state.messages.append(
+        dict(
+            role=MODEL_ROLE,
+            content=st.session_state.chat.history[-1].parts[0].text,
+            avatar=AI_AVATAR_ICON,
+        )
+    )
+    st.session_state.gemini_history = st.session_state.chat.history
+    # Save to file
+    joblib.dump(
+        st.session_state.messages,
+        f'data/{st.session_state.chat_id}-st_messages',
+    )
+    joblib.dump(
+        st.session_state.gemini_history,
+        f'data/{st.session_state.chat_id}-gemini_messages',
+    )

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+google-generativeai==0.3.1
+streamlit==1.29.0
+joblib==1.3.2