Update app.py

app.py

@@ -1,145 +1,42 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
+import json
+from langchain.chains import SimpleChatChain
 from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
+from langchain.llms import OpenAI
 
-load_dotenv()
-os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+# Define your API key for OpenAI or any other LLM provider
+openai_api_key = 'sk-nAqoChT9cmkAxALwMLdWT3BIbkFJcNHsH5Z5LN2ixPcDAopT'
 
-def get_pdf_text(pdf_docs):
-    text=""
-    for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
+# Initialize the LLM
+llm = OpenAI(api_key=openai_api_key)
 
+# Define a simple chat chain with a prompt template
+chat_chain = SimpleChatChain(
+    llm=llm,
+    prompt_template=PromptTemplate(
+        input_variables=["user_input"],
+        template="User: {user_input}\nBot:"
+    )
+)
 
+def chat_with_json(input_json):
+    # Parse the input JSON
+    input_data = json.loads(input_json)
+    user_input = input_data.get('message', '')
 
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-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")
-
-
-def get_conversational_chain():
-
-    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
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    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
-
-
-
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
-    new_db = FAISS.load_local("faiss_index", embeddings)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    print(response)
-    st.write("Reply: ", response["output_text"])
-
-
-
-
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Gemini-Powered-MultiPDF-Chatbot")
-
-    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")
+    # Generate a response using the chat chain
+    response = chat_chain.run(user_input)
 
+    # Create the response JSON
+    response_json = json.dumps({'response': response})
+    return response_json
 
+# Example usage
 if __name__ == "__main__":
-    main()
-
-def set_bg_from_url(url, opacity=1):
+    # Simulate a JSON input from the user
+    user_input_json = json.dumps({'message': 'Hello, how are you?'})
     
-    footer = """
-    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-gH2yIJqKdNHPEq0n4Mqa/HGKIhSkIHeL5AyhkYV8i59U5AR6csBvApHHNl/vI1Bx" crossorigin="anonymous">
-    <footer>
-        <div style='visibility: visible;margin-top:7rem;justify-content:center;display:flex;'>
-            <p style="font-size:1.1rem;">
-                Made by Mohamed Shaad
-                &nbsp;
-                <a href="https://www.linkedin.com/in/mohamedshaad">
-                    <svg xmlns="http://www.w3.org/2000/svg" width="23" height="23" fill="white" class="bi bi-linkedin" viewBox="0 0 16 16">
-                        <path d="M0 1.146C0 .513.526 0 1.175 0h13.65C15.474 0 16 .513 16 1.146v13.708c0 .633-.526 1.146-1.175 1.146H1.175C.526 16 0 15.487 0 14.854V1.146zm4.943 12.248V6.169H2.542v7.225h2.401zm-1.2-8.212c.837 0 1.358-.554 1.358-1.248-.015-.709-.52-1.248-1.342-1.248-.822 0-1.359.54-1.359 1.248 0 .694.521 1.248 1.327 1.248h.016zm4.908 8.212V9.359c0-.216.016-.432.08-.586.173-.431.568-.878 1.232-.878.869 0 1.216.662 1.216 1.634v3.865h2.401V9.25c0-2.22-1.184-3.252-2.764-3.252-1.274 0-1.845.7-2.165 1.193v.025h-.016a5.54 5.54 0 0 1 .016-.025V6.169h-2.4c.03.678 0 7.225 0 7.225h2.4z"/>
-                    </svg>          
-                </a>
-                &nbsp;
-                <a href="https://github.com/shaadclt">
-                    <svg xmlns="http://www.w3.org/2000/svg" width="23" height="23" fill="white" class="bi bi-github" viewBox="0 0 16 16">
-                        <path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.012 8.012 0 0 0 16 8c0-4.42-3.58-8-8-8z"/>
-                    </svg>
-                </a>
-            </p>
-        </div>
-    </footer>
-"""
-    st.markdown(footer, unsafe_allow_html=True)
+    # Get the response from the chatbot
+    response_json = chat_with_json(user_input_json)
     
-    
-    # Set background image using HTML and CSS
-    st.markdown(
-        f"""
-        <style>
-            body {{
-                background: url('{url}') no-repeat center center fixed;
-                background-size: cover;
-                opacity: {opacity};
-            }}
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-# Set background image from URL
-set_bg_from_url("https://images.crunchbase.com/image/upload/c_pad,f_auto,q_auto:eco,dpr_1/awj1xai1s7tvk7zprgvh", opacity=0.875)
-   
+    # Print the response JSON
+    print(response_json)