Update app.py

app.py

@@ -4,7 +4,7 @@ from llama_index.readers.file import PDFReader
 from llama_index.core import VectorStoreIndex
 from llama_index.core.chat_engine.types import BaseChatEngine
 
-# Set your OpenAI API key here (or via Hugging Face Secrets)
+# Set your OpenAI API key from environment (set this in Hugging Face Secrets)
 os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
 
 # Globals
@@ -13,8 +13,10 @@ chat_engine: BaseChatEngine = None
 # Function to process uploaded resume
 def process_resume(file):
     global chat_engine
+    if file is None:
+        return "⚠️ Please upload a PDF file."
     reader = PDFReader()
-    documents = reader.load_data(file=file.name)
+    documents = reader.load_data(file=file)
     index = VectorStoreIndex.from_documents(documents)
     chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=False)
     return "✅ Resume uploaded and indexed! You can now ask questions."
@@ -25,7 +27,8 @@ def chat_with_resume(message, chat_history):
     if not chat_engine:
         return "⚠️ Please upload a resume first.", chat_history
     response = chat_engine.chat(message)
-    chat_history.append((message, response.response))
+    chat_history.append({"role": "user", "content": message})
+    chat_history.append({"role": "assistant", "content": response.response})
     return "", chat_history
 
 # Gradio UI
@@ -40,7 +43,7 @@ with gr.Blocks() as demo:
 
     upload_button.click(fn=process_resume, inputs=file_input, outputs=upload_output)
 
-    chatbot = gr.Chatbot(label="Chat with Resume")
+    chatbot = gr.Chatbot(label="Chat with Resume", type="messages")
     message = gr.Textbox(placeholder="Ask something like: What are my key skills?", label="Your Question")
     send = gr.Button("Send")