Upload 3 files

appp.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+from page1 import text
+from page2 import image
+def main():
+
+    st.title("Chat With Gemini")
+
+    with st.sidebar:
+        selection = option_menu(
+        menu_title="Main Menu",
+        options=["Text Model", "Image Model"],
+        icons=["pencil", "image"],
+        menu_icon="cast",
+        default_index=0
+        )
+
+    if selection == "Text Model":
+        text()
+
+    elif selection == "Image Model":
+        image()
+
+
+if __name__ == '__main__':
+    main()

page1.py

@@ -0,0 +1,56 @@
+import streamlit as st
+from langchain_core.messages import HumanMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
+
+def text():
+
+    apiKey = "AIzaSyAXkkcrrUBjPEgj93tZ9azy7zcS1wI1jUA"
+    msgs = StreamlitChatMessageHistory(key="special_app_key")
+
+    memory = ConversationBufferMemory(memory_key="history", chat_memory=msgs)
+    if len(msgs.messages) == 0:
+        msgs.add_ai_message("How can I help you?")
+    template = """You are an AI chatbot having a conversation with a human.
+
+    {history}
+    Human: {human_input}
+    AI: """
+    prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
+    llm_chain = LLMChain( llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=apiKey), prompt=prompt, memory = memory)
+
+    if 'messages' not in st.session_state:
+        st.session_state['messages'] = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    prompt = st.chat_input("Say something")
+
+    if prompt:
+        with st.chat_message("user").markdown(prompt):
+            st.session_state.messages.append(
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            )
+        for chunk in llm_chain.stream(prompt):
+            text_output = chunk.get("text", "")
+
+        with st.chat_message("assistant").markdown(text_output):
+            st.session_state.messages.append(
+                {
+                    "role": "assistant",
+                    "content": text_output
+                }
+            )
+
+
+
+
+

page2.py

@@ -0,0 +1,82 @@
+import streamlit as st
+from langchain_core.messages import HumanMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from streamlit_chat import message
+from PIL import Image
+import base64
+import io
+from IPython.display import display
+from IPython.display import Markdown
+
+import pathlib
+import textwrap
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
+
+# Streamlit app
+def image():
+
+
+    def process_image(uploaded_file):
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        # Process the image and return the URL or other information
+        # For demonstration purposes, convert the image to base64 and return a data URL
+        buffered = io.BytesIO()
+        image.save(buffered, format="JPEG")
+        image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        image_url = f"data:image/jpeg;base64,{image_base64}"
+
+        return image_url
+    apiKey = "AIzaSyAXkkcrrUBjPEgj93tZ9azy7zcS1wI1jUA"
+
+    llm = ChatGoogleGenerativeAI(model="gemini-pro-vision", google_api_key=apiKey)
+
+    image_url = None  # Initialize image_url outside the if statement
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image_url = process_image(uploaded_file)
+
+
+    if 'messages' not in st.session_state:
+        st.session_state['messages'] = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    prompt = st.chat_input("Say something")
+    message = HumanMessage(
+        content=[
+            {
+                "type": "text",
+                "text": prompt,
+            },  # You can optionally provide text parts
+            {"type": "image_url", "image_url": image_url},
+        ]
+    )
+
+    if prompt:
+        with st.chat_message("user").markdown(prompt):
+            st.session_state.messages.append(
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            )
+        response = llm.invoke([message])
+        text_output = response.content
+
+        with st.chat_message("assistant").markdown(text_output):
+            st.session_state.messages.append(
+                {
+                    "role": "assistant",
+                    "content": text_output
+                }
+            )
+
+