Update app.py

app.py

@@ -1,29 +1,15 @@
 import streamlit as st
 import os
-from openai import OpenAI
-import tempfile
-from langchain.chains import ConversationalRetrievalChain
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain_community.document_loaders import (
-    PyPDFLoader, 
-    TextLoader, 
-    CSVLoader
-)
 from datetime import datetime
 from pydub import AudioSegment
+import tempfile
 import pytz
-
+from openai import OpenAI
 from langchain.chains import ConversationalRetrievalChain
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain_community.document_loaders import PyPDFLoader, TextLoader, CSVLoader
-import os
-import tempfile
-from datetime import datetime
-import pytz
 
 
 class DocumentRAG:
@@ -53,12 +39,10 @@ class DocumentRAG:
         try:
             documents = []
             for uploaded_file in uploaded_files:
-                # Save uploaded file to a temporary location
                 temp_file_path = tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]).name
                 with open(temp_file_path, "wb") as temp_file:
                     temp_file.write(uploaded_file.read())
 
-                # Determine the loader based on the file type
                 if temp_file_path.endswith('.pdf'):
                     loader = PyPDFLoader(temp_file_path)
                 elif temp_file_path.endswith('.txt'):
@@ -68,7 +52,6 @@ class DocumentRAG:
                 else:
                     return f"Unsupported file type: {uploaded_file.name}"
 
-                # Load the documents
                 try:
                     documents.extend(loader.load())
                 except Exception as e:
@@ -77,7 +60,6 @@ class DocumentRAG:
             if not documents:
                 return "No valid documents were processed. Please check your files."
 
-            # Split text for better processing
             text_splitter = RecursiveCharacterTextSplitter(
                 chunk_size=1000,
                 chunk_overlap=200,
@@ -85,16 +67,14 @@ class DocumentRAG:
             )
             documents = text_splitter.split_documents(documents)
 
-            # Combine text for summary
             combined_text = " ".join([doc.page_content for doc in documents])
             self.document_summary = self.generate_summary(combined_text)
 
-            # Create embeddings and initialize retrieval chain
             embeddings = OpenAIEmbeddings(api_key=self.api_key)
             self.document_store = Chroma.from_documents(
                 documents,
                 embeddings,
-                persist_directory=self.chroma_persist_dir  # Persistent directory for Chroma
+                persist_directory=self.chroma_persist_dir
             )
 
             self.qa_chain = ConversationalRetrievalChain.from_llm(
@@ -137,8 +117,6 @@ class DocumentRAG:
 
         try:
             client = OpenAI(api_key=self.api_key)
-
-            # Generate podcast script
             script_response = client.chat.completions.create(
                 model="gpt-4",
                 messages=[
@@ -157,7 +135,6 @@ class DocumentRAG:
             if not script:
                 return "Error: Failed to generate podcast script.", None
 
-            # Convert script to audio
             final_audio = AudioSegment.empty()
             is_first_speaker = True
 
@@ -201,98 +178,43 @@ class DocumentRAG:
         except Exception as e:
             return f"Error generating podcast: {str(e)}", None
 
-    def generate_summary(self, text):
-        """Generate a summary of the provided text."""
-        if not self.api_key:
-            return "API Key not set. Please set it in the environment variables."
-        try:
-            client = OpenAI(api_key=self.api_key)
-            response = client.chat.completions.create(
-                model="gpt-4",
-                messages=[
-                    {"role": "system", "content": "Summarize the document content concisely and provide 3-5 key points for discussion."},
-                    {"role": "user", "content": text[:4000]}
-                ],
-                temperature=0.3
-            )
-            return response.choices[0].message.content
-        except Exception as e:
-            return f"Error generating summary: {str(e)}"
-
-    def handle_query(self, question, history):
-        """Handle user queries."""
-        if not self.qa_chain:
-            return history + [("System", "Please process the documents first.")]
-        try:
-            preface = """
-            Instruction: Respond in English. Be professional and concise, keeping the response under 300 words.
-            If you cannot provide an answer, say: "I am not sure about this question. Please try asking something else."
-            """
-            query = f"{preface}\nQuery: {question}"
-
-            result = self.qa_chain({
-                "question": query,
-                "chat_history": [(q, a) for q, a in history]
-            })
-
-            if "answer" not in result:
-                return history + [("System", "Sorry, an error occurred.")]
-
-            history.append((question, result["answer"]))
-            return history
-        except Exception as e:
-            return history + [("System", f"Error: {str(e)}")]
 
 # Initialize RAG system in session state
 if "rag_system" not in st.session_state:
     st.session_state.rag_system = DocumentRAG()
 
-# Streamlit UI
+# Sidebar
+with st.sidebar:
+    st.title("About")
+    st.markdown(
+        """
+        This app is inspired by the [RAG_HW HuggingFace Space](https://huggingface.co/spaces/wint543/RAG_HW).  
+        It allows users to upload documents, generate summaries, ask questions, and create podcasts.
+        """
+    )
+    st.markdown("### Steps:")
+    st.markdown("1. Upload documents.")
+    st.markdown("2. Generate summaries.")
+    st.markdown("3. Ask questions.")
+    st.markdown("4. Create podcasts.")
+
+# Main App
 st.title("Document Analyzer and Podcast Generator")
 
-# Fetch the API key status
-if "OPENAI_API_KEY" not in os.environ or not os.getenv("OPENAI_API_KEY"):
-    st.error("The 'OPENAI_API_KEY' environment variable is not set. Please configure it in your hosting environment.")
-
-# File upload
-st.subheader("Step 1: Upload Documents")
 uploaded_files = st.file_uploader("Upload files (PDF, TXT, CSV)", accept_multiple_files=True)
 
 if st.button("Process Documents"):
     if uploaded_files:
-        # Process the uploaded files
         result = st.session_state.rag_system.process_documents(uploaded_files)
-        if "successfully" in result:
-            st.success(result)
-        else:
-            st.error(result)
+        st.success(result) if "successfully" in result else st.error(result)
     else:
         st.warning("No files uploaded.")
 
-# Document Q&A
-st.subheader("Step 2: Ask Questions")
-if st.session_state.rag_system.qa_chain:
-    history = []
-    user_question = st.text_input("Ask a question:")
-    if st.button("Submit Question"):
-        # Handle the user query
-        history = st.session_state.rag_system.handle_query(user_question, history)
-        for question, answer in history:
-            st.chat_message("user").write(question)
-            st.chat_message("assistant").write(answer)
-else:
-    st.info("Please process documents before asking questions.")
-
-# Podcast Generation
-st.subheader("Step 3: Generate Podcast")
 if st.session_state.rag_system.document_summary:
+    st.subheader("Step 2: Generate Podcast")
     if st.button("Generate Podcast"):
         script, audio_path = st.session_state.rag_system.create_podcast()
         if audio_path:
             st.text_area("Generated Podcast Script", script, height=200)
             st.audio(audio_path, format="audio/mp3")
-            st.success("Podcast generated successfully! You can listen to it above.")
-        else:
-            st.error(script)
-else:
-    st.info("Please process documents to generate a podcast.")
+            st.success("Podcast generated successfully!")