Added new demo

gemini_multimodal_chatbot/README.md

@@ -0,0 +1,28 @@
+## ⚡️ Multimodal Chatbot with Gemini Flash 
+This repository contains a Streamlit application that demonstrates a multimodal chatbot using Google's Gemini Flash model. The chatbot allows users to interact with the model using both image and text inputs, providing lightning-fast results.
+
+## Features
+- Multimodal input: Users can upload images and enter text queries to interact with the chatbot.
+- Gemini Flash model: The chatbot leverages Google's powerful Gemini Flash model for generating responses.
+- Chat history: The application maintains a chat history, displaying the conversation between the user and the chatbot.
+
+### How to get Started?
+
+1. Clone the GitHub repository
+
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+3. Get your Google Studio API Key
+
+- Sign up for an [Google AI Studio](https://aistudio.google.com/app/apikey) and obtain your API key.
+
+4. Run the Streamlit App
+```bash
+streamlit run gemini_multimodal_chatbot.py
+```

gemini_multimodal_chatbot/gemini_multimodal_chatbot.py

@@ -0,0 +1,67 @@
+import os
+import streamlit as st
+import google.generativeai as genai
+from PIL import Image
+
+# Set up the Streamlit App
+st.set_page_config(page_title="Multimodal Chatbot with Gemini Flash", layout="wide")
+st.title("Multimodal Chatbot with Gemini Flash ⚡️")
+st.caption("Chat with Google's Gemini Flash model using image and text input to get lightning fast results. 🌟")
+
+# Get OpenAI API key from user
+api_key = st.text_input("Enter Google API Key", type="password")
+
+# Set up the Gemini model
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest")
+
+if api_key:
+    # Initialize the chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Sidebar for image upload
+    with st.sidebar:
+        st.title("Chat with Images")
+        uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Main layout
+    chat_placeholder = st.container()
+
+    with chat_placeholder:
+        # Display the chat history
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+    # User input area at the bottom
+    prompt = st.chat_input("What do you want to know?")
+
+    if prompt:
+        inputs = [prompt]
+        
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        # Display user message in chat message container
+        with chat_placeholder:
+            with st.chat_message("user"):
+                st.markdown(prompt)
+        
+        if uploaded_file:
+            inputs.append(image)
+
+        with st.spinner('Generating response...'):
+            # Generate response
+            response = model.generate_content(inputs)
+    
+        # Display assistant response in chat message container
+        with chat_placeholder:
+            with st.chat_message("assistant"):
+                st.markdown(response.text)
+
+    if uploaded_file and not prompt:
+        st.warning("Please enter a text query to accompany the image.")

gemini_multimodal_chatbot/requirements.txt

@@ -0,0 +1,3 @@
+streamlit 
+pillow
+google-generativeai

multi_agent_researcher/research_agent_llama3.py

@@ -8,7 +8,6 @@ from phi.llm.ollama import Ollama
 st.title("Multi-Agent AI Researcher using Llama-3 🔍🤖")
 st.caption("This app allows you to research top stories and users on HackerNews and write blogs, reports and social posts.")
 
-
 # Create instances of the Assistant
 story_researcher = Assistant(
     name="HackerNews Story Researcher",