Create app.py

app.py

@@ -0,0 +1,163 @@
+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
+import pytz
+
+# DocumentRAG class with environment variable support for API Key
+class DocumentRAG:
+    def __init__(self):
+        self.document_store = None
+        self.qa_chain = None
+        self.document_summary = ""
+        self.chat_history = []
+        self.last_processed_time = None
+        self.api_key = os.getenv("OPENAI_API_KEY")  # Fetch the API key from environment variable
+        self.init_time = datetime.now(pytz.UTC)
+
+        if not self.api_key:
+            raise ValueError("API Key not found. Make sure to set the 'OPENAI_API_KEY' environment variable.")
+
+    def process_documents(self, file_paths):
+        if not self.api_key:
+            return "Please set the OpenAI API key in the environment variables."
+        if not file_paths:
+            return "Please upload documents first."
+
+        try:
+            documents = []
+            for file_path in file_paths:
+                if file_path.name.endswith('.pdf'):
+                    loader = PyPDFLoader(file_path.name)
+                elif file_path.name.endswith('.txt'):
+                    loader = TextLoader(file_path.name)
+                elif file_path.name.endswith('.csv'):
+                    loader = CSVLoader(file_path.name)
+                else:
+                    continue
+
+                try:
+                    documents.extend(loader.load())
+                except Exception as e:
+                    print(f"Error loading {file_path.name}: {str(e)}")
+                    continue
+
+            if not documents:
+                return "No valid documents were processed. Please check your files."
+
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=1000,
+                chunk_overlap=200,
+                length_function=len
+            )
+            documents = text_splitter.split_documents(documents)
+
+            combined_text = " ".join([doc.page_content for doc in documents])
+            self.document_summary = self.generate_summary(combined_text)
+
+            embeddings = OpenAIEmbeddings(api_key=self.api_key)
+            self.document_store = Chroma.from_documents(documents, embeddings)
+            self.qa_chain = ConversationalRetrievalChain.from_llm(
+                ChatOpenAI(temperature=0, model_name='gpt-4', api_key=self.api_key),
+                self.document_store.as_retriever(search_kwargs={'k': 6}),
+                return_source_documents=True,
+                verbose=False
+            )
+
+            self.last_processed_time = datetime.now(pytz.UTC)
+            return "Documents processed successfully!"
+        except Exception as e:
+            return f"Error processing documents: {str(e)}"
+
+    def generate_summary(self, text):
+        """Generate a summary of the uploaded documents."""
+        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):
+        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)}")]
+
+# Streamlit UI
+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.")
+else:
+    st.success("API Key successfully loaded from environment variable.")
+
+# Initialize RAG system
+try:
+    rag_system = DocumentRAG()
+except ValueError as e:
+    st.error(str(e))
+    st.stop()
+
+# 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:
+        file_paths = [tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.name)[1]).name for file in uploaded_files]
+        for file, temp_path in zip(uploaded_files, file_paths):
+            with open(temp_path, 'wb') as temp_file:
+                temp_file.write(file.read())
+        st.success(rag_system.process_documents(file_paths))
+    else:
+        st.warning("No files uploaded.")
+
+# Document Q&A
+st.subheader("Step 2: Ask Questions")
+if rag_system.qa_chain:
+    history = []
+    user_question = st.text_input("Ask a question:")
+    if st.button("Submit Question"):
+        history = 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.")