Create app.py

app.py

@@ -0,0 +1,49 @@
+import os
+import gradio as gr
+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)
+os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
+
+# Globals
+chat_engine: BaseChatEngine = None
+
+# Function to process uploaded resume
+def process_resume(file):
+    global chat_engine
+    reader = PDFReader()
+    documents = reader.load_data(file=file.name)
+    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."
+
+# Chat function
+def chat_with_resume(message, chat_history):
+    global chat_engine
+    if not chat_engine:
+        return "⚠️ Please upload a resume first.", chat_history
+    response = chat_engine.chat(message)
+    chat_history.append((message, response.response))
+    return "", chat_history
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📄 Resume Chatbot\nUpload your resume and ask questions about your experience, skills, and more.")
+    
+    with gr.Row():
+        file_input = gr.File(label="Upload Resume (PDF)", file_types=[".pdf"])
+        upload_button = gr.Button("Process Resume")
+
+    upload_output = gr.Textbox(label="Status")
+
+    upload_button.click(fn=process_resume, inputs=file_input, outputs=upload_output)
+
+    chatbot = gr.Chatbot(label="Chat with Resume")
+    message = gr.Textbox(placeholder="Ask something like: What are my key skills?", label="Your Question")
+    send = gr.Button("Send")
+
+    send.click(chat_with_resume, inputs=[message, chatbot], outputs=[message, chatbot])
+
+demo.launch()