Chatbot.py

Chatbot.py

@@ -1,29 +1,169 @@
-from openai import OpenAI
 import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import HuggingFaceHub
+from langchain.prompts import PromptTemplate
+import os
+import asyncio
 
-with st.sidebar:
-    openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
+# API tokens
+HUGGINGFACE_API_TOKEN = "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"
 
-st.title("💬 Chatbot")
-st.caption("🚀 A Streamlit chatbot powered by OpenAI")
+if not HUGGINGFACE_API_TOKEN:
+    st.error("HUGGINGFACE_API_TOKEN is not set.")
+    st.stop()
+
+# Initialize session states
 if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
-
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
-
-if prompt := st.chat_input():
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
-
-    client = OpenAI(api_key=openai_api_key)
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
-    msg = response.choices[0].message.content
-    st.session_state.messages.append({"role": "assistant", "content": msg})
-    st.chat_message("assistant").write(msg)
+    st.session_state.messages = []
+
+def get_pdf_text(pdf_docs):
+    """Extract text from uploaded PDF documents."""
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text() or ""
+    return text.strip()
+
+def get_text_chunks(text):
+    """Split the text into manageable chunks."""
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=800,
+        chunk_overlap=100
+    )
+    return text_splitter.split_text(text)
+
+def get_vector_store(text_chunks):
+    """Create and cache a FAISS vector store."""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    """Create a conversational chain with custom prompts."""
+    prompt_template = """
+    Use the provided context to answer the user's question. If no relevant context is found, respond:
+    "Sorry, I couldn't find sufficient information in the document."
+
+    Context: {context}
+    Question: {question}
+
+    Answer:
+    """
+    model = HuggingFaceHub(
+        repo_id="google/flan-t5-base", 
+        model_kwargs={"temperature": 0.5, "max_length": 768},  
+        huggingfacehub_api_token=HUGGINGFACE_API_TOKEN
+    )
+    prompt = PromptTemplate(
+        template=prompt_template,
+        input_variables=["context", "question"]
+    )
+    return load_qa_chain(llm=model, chain_type="stuff", prompt=prompt)
+
+async def process_user_input(user_question):
+    """Process user questions asynchronously."""
+    try:
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+        docs = new_db.similarity_search(user_question, k=3)
+
+        if not docs:
+            return "Sorry, I couldn't find relevant information in the uploaded documents."
+
+        chain = get_conversational_chain()
+        response = chain.run(input_documents=docs, question=user_question)
+
+        st.session_state.messages.append({"role": "user", "content": user_question})
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+        return response
+    except Exception as e:
+        return f"Error processing your query: {str(e)}"
+
+def display_chat():
+    """Display the chat history."""
+    for message in st.session_state.messages:
+        if message["role"] == "user":
+            st.chat_message("user").markdown(message["content"])
+        elif message["role"] == "assistant":
+            st.chat_message("assistant").markdown(message["content"])
+
+def extract_text(uploaded_files):
+    """
+    Extract text content from uploaded files (PDFs and .txt).
+    Supports PDF files and plain text files.
+    """
+    text = ""
+    for file in uploaded_files:
+        file_type = file.name.split(".")[-1].lower()
+
+        if file_type == "pdf":
+            pdf_reader = PdfReader(file)
+            for page in pdf_reader.pages:
+                text += page.extract_text() or ""
+        elif file_type == "txt":
+            text += file.read().decode("utf-8")  # Assuming UTF-8 encoding for text files
+        else:
+            st.warning(f"Unsupported file type: {file.name}. Only PDFs and .txt files are supported.")
+
+    if not text.strip():
+        st.error("No text could be extracted from the uploaded files.")
+    return text.strip()
+
+# Update the main function to use the new extraction method
+def main():
+    """Main application function."""
+    st.set_page_config("Multi File Chatbot", page_icon=":robot:", layout="wide")
+    st.title("PDF Reader Chatbot 🤖")
+
+    with st.sidebar:
+        st.header("📁 File Upload")
+        uploaded_files = st.file_uploader(
+            "Upload your PDF or Text files",
+            accept_multiple_files=True
+        )
+
+        if st.button("Process Files"):
+            if not uploaded_files:
+                st.warning("Please upload files first!")
+                return
+
+            with st.spinner("Processing..."):
+                raw_text = extract_text(uploaded_files)
+                if not raw_text:
+                    st.error("No text could be extracted from the uploaded files.")
+                    return
+
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Processing complete! Vector store created.")
+
+        if st.button("Clear Chat"):
+            st.session_state.messages = []
+            st.experimental_rerun()
+
+    # Display the chat history
+    display_chat()
+
+    # Chat Input
+    if prompt := st.chat_input("Ask a question about your uploaded files..."):
+        if not os.path.exists("faiss_index"):
+            st.warning("Please process files first!")
+            return
+
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.chat_message("user").markdown(prompt)
+
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = asyncio.run(process_user_input(prompt))
+                st.markdown(response)
+
+# Run the main application
+if __name__ == "__main__":
+    main()