add some debug print statements to debug the state,db being unavaiable in chat_with_pdf()

app.py

@@ -22,46 +22,47 @@ class PDFChatbot:
         self.state = SessionState()
 
     def process_pdf(self, pdf_file):
-        try:
-            if self.state.is_db_ready():
-                return
-
-            file_size_mb = os.path.getsize(pdf_file.name) / (1024 * 1024)
-            if file_size_mb >= 75:
-                gr.Error("File size exceeds the 75 MB limit. Please upload a smaller PDF.")
-                return
-
-            self.state = SessionState()
-            doc = fitz.open(pdf_file.name)
-            text = ""
-            for page in doc:
-                text += page.get_text()
-            doc.close()
-
-            text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-            docs = text_splitter.create_documents([text])
-
-            embeddings = GoogleGenerativeAIEmbeddings(model=EMBEDDING_MODEL)
-            self.state.db = Chroma.from_documents(
-                documents=docs,
-                embedding=embeddings,
-                persist_directory=self.state.vector_store_path
-            )
-
-            gr.Info("PDF processed successfully! You can now ask questions about the document.")
-        except Exception as e:
-            if os.path.exists(self.state.vector_store_path):
-                shutil.rmtree(self.state.vector_store_path)
-            gr.Error(f"An error occurred: {str(e)}")
+    try:
+        if self.state.is_db_ready():
+            print("Database is already ready.")
+            return
 
-    def is_db_ready(self):
-        return self.state.db is not None
+        file_size_mb = os.path.getsize(pdf_file.name) / (1024 * 1024)
+        if file_size_mb >= 75:
+            print("File size exceeds the 75 MB limit.")
+            gr.Error("File size exceeds the 75 MB limit. Please upload a smaller PDF.")
+            return
+
+        self.state = SessionState()
+        doc = fitz.open(pdf_file.name)
+        text = ""
+        for page in doc:
+            text += page.get_text()
+        doc.close()
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        docs = text_splitter.create_documents([text])
+
+        embeddings = GoogleGenerativeAIEmbeddings(model=EMBEDDING_MODEL)
+        self.state.db = Chroma.from_documents(
+            documents=docs,
+            embedding=embeddings,
+            persist_directory=self.state.vector_store_path
+        )
+        print("PDF processed successfully! Database is ready.")
+    except Exception as e:
+        if os.path.exists(self.state.vector_store_path):
+            shutil.rmtree(self.state.vector_store_path)
+        print(f"An error occurred: {str(e)}")
 
     def chat_with_pdf(self, message, history):
-        if not self.is_db_ready():
+        print("Chat interface called. Checking if database is ready...")
+        if not self.state.is_db_ready():
+            print("Database is not ready.")
             yield "Error: Database not ready."
             return
 
+        print("Database is ready. Retrieving relevant documents...")
         retriever = self.state.db.as_retriever()
         llm = ChatGoogleGenerativeAI(model=LLM_MODEL, temperature=0.7)
 
@@ -87,6 +88,9 @@ class PDFChatbot:
         response = rag_chain.invoke(message)
         yield response
 
+    def is_db_ready(self):
+        return self.state.db is not None
+
 class SessionState:
     def __init__(self):
         self.session_id = str(uuid.uuid4())