Update app.py

app.py

@@ -1,104 +1,41 @@
 from dotenv import load_dotenv
+import streamlit as st
+import os
+import google.generativeai as genai
+from PIL import Image
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
 from langchain.vectorstores import FAISS
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
-import streamlit as st
-import os
-import sqlite3
-import google.generativeai as genai
 
-# Load environment variables
-load_dotenv()
-
-# Configure Genai Key
+load_dotenv() # Load all env variables 
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-# Initialize Streamlit app
-st.set_page_config(page_title="Q&A Demo")
-
-st.header("Gemini LLM Application")
-
-# Initialize session state for chat history if it doesn't exist
-if 'chat_history' not in st.session_state:
-    st.session_state['chat_history'] = []
-
-# Load Google Gemini Model
-model = genai.GenerativeModel("gemini-pro") 
-chat = model.start_chat(history=[])
-
-# Function to get response from Gemini model
-def get_gemini_response(question):
-    response = chat.send_message(question)
-    return response
-
-# Function to read SQL query from database
-def read_sql_query(sql, db):
-    conn = sqlite3.connect(db)
-    cur = conn.cursor()
-    cur.execute(sql)
-    rows = cur.fetchall()
-    conn.commit()
-    conn.close()
-    return rows
-
-# Define prompt for Gemini model
-prompt = """
-You are an expert in converting English questions to SQL query!
-The SQL database has the name STUDENT and has the following columns - NAME, CLASS, SECTION \n\nFor example,\nExample 1 - How many entries of records are present?, 
-the SQL command will be something like this SELECT COUNT(*) FROM STUDENT ;
-\nExample 2 - Tell me all the students studying in Data Science class?, 
-the SQL command will be something like this SELECT * FROM STUDENT 
-where CLASS="Data Science"; And show me the data in tabular format if possible.
-also the sql code should not have ``` in beginning or end and sql word in output
-"""
-
-# Streamlit UI
-input_text = st.text_area("Input: ", key="input")
-submit_button = st.button("Ask the question")
-
-if submit_button and input_text:
-    # Get response from Gemini model
-    response = get_gemini_response(input_text)
-    
-    # Add user query and response to session state chat history
-    st.session_state['chat_history'].append(("You", input_text))
-    st.subheader("The Response is")
-    for chunk in response:
-        st.write(chunk.text)
-        st.session_state['chat_history'].append(("Bot", chunk.text))
-
-# Display chat history
-st.subheader("The Chat History is")
-for role, text in st.session_state['chat_history']:
-    st.write(f"{role}: {text}")
 
-# Function to get text from PDF
 def get_pdf_text(pdf_docs):
-    text = ""
+    text=""
     for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
+        pdf_reader= PdfReader(pdf)
         for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
+            text+= page.extract_text()
+    return  text
+
 
-# Function to split text into chunks
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     chunks = text_splitter.split_text(text)
     return chunks
 
-# Function to create vector store
+
 def get_vector_store(text_chunks):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
-# Function to initialize conversational chain
+
 def get_conversational_chain():
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
     prompt_template = """
     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
     provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
@@ -107,11 +44,14 @@ def get_conversational_chain():
 
     Answer:
     """
+
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
     prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
     return chain
 
-# Function to process user input for PDF interaction
+
 def user_input(user_question):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
@@ -120,22 +60,70 @@ def user_input(user_question):
     response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
     st.write("Reply: ", response["output_text"])
 
-# Main function
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Chat with PDF using Gemini💁")
-    user_question = st.text_input("Ask a Question from the PDF Files")
-    if user_question:
-        user_input(user_question)
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-if __name__ == "__main__":
-    main()
+
+## function to load Gemini Pro model and get responses
+model = genai.GenerativeModel("gemini-pro-vision")
+def get_gemini_response(input,image):
+    if input != "":
+        response=model.generate_content([input,image])
+    else: 
+        response=model.generate_content(image)
+    return response.text
+
+
+## Initialize our Streamlit app
+
+st.set_page_config(page_title='Combined Streamlit Application')
+st.header("Streamlit Application")
+
+# PDF Chat Section
+user_question = st.text_input("Ask a Question from the PDF Files")
+if user_question:
+    user_input(user_question)
+
+with st.sidebar:
+    st.title("Menu:")
+    pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+    if st.button("Submit & Process"):
+        with st.spinner("Processing..."):
+            raw_text = get_pdf_text(pdf_docs)
+            text_chunks = get_text_chunks(raw_text)
+            get_vector_store(text_chunks)
+            st.success("Done")
+
+# Image Chat Section
+input_text = st.text_input("Input for Gemini Pro:", key="input_gemini")
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+submit_gemini = st.button("Ask Gemini Pro")
+
+if submit_gemini:
+    response_gemini = get_gemini_response(input_text, image)
+    st.subheader("Gemini Pro Response:")
+    st.write(response_gemini)
+
+# Q&A Chat Section
+st.header("Q&A Chat Section")
+
+# Initialize session state for chat history if it doesn't exist
+if 'chat_history' not in st.session_state:
+    st.session_state['chat_history'] = []
+
+input_qa = st.text_area("Input for Q&A:", key="input_qa")
+submit_qa = st.button("Ask the question")
+
+if submit_qa and input_qa:
+    response_qa = get_gemini_response(input_qa)
+    # Add user query and response to session state chat history
+    st.session_state['chat_history'].append(("You", input_qa))
+    st.subheader("Q&A Response:")
+    for chunk in response_qa:
+        st.write(chunk.text)
+        st.session_state['chat_history'].append(("Gemini Pro", chunk.text))
+
+st.subheader("Q&A Chat History:")
+for role, text in st.session_state['chat_history']:
+    st.write(f"{role}: {text}")