Update app.py

app.py

@@ -1,32 +1,51 @@
 import os
 import gradio as gr
-from llama_index.core import VectorStoreIndex
+import openai
 from llama_index.readers.file import PDFReader
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+def process_pdf(file, question):
+    try:
+        reader = PDFReader()
+        documents = reader.load_data(file=file.name)
 
-# Set your OpenAI API key (use HF secrets in prod!)
-openai,api.key = os.environ.get("OPENAI_API_KEY")
-def process_resume(file, question):
-    if file is None or question.strip() == "":
-        return "Please upload a resume and enter a question."
+        embed_model = OpenAIEmbedding()
+        llm = OpenAI()
 
-    reader = PDFReader()
-    documents = reader.load_data(file=file.name)
+        index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
+        query_engine = index.as_query_engine(llm=llm)
+        response = query_engine.query(question)
 
-    # Use OpenAI-based vector index
-    index = VectorStoreIndex.from_documents(documents)
-    query_engine = index.as_query_engine()  # Defaults to OpenAI LLM if key is set
-    response = query_engine.query(question)
+        return str(response)
 
-    return str(response)
+    except Exception as e:
+        return f"❌ Error: {e}"
+
+# Gradio Blocks UI
+with gr.Blocks(title="Resume Analyzer by Advaith") as demo:
+    gr.Markdown("""
+    # 📄 Resume Analyzer
+    Upload a resume and ask any question about the candidate!  
+    Powered by **LlamaIndex** + **OpenAI**
+    """)
 
-with gr.Blocks() as demo:
-    gr.Markdown("## 📄 Resume Analysis Chatbot (OpenAI Powered)")
     with gr.Row():
-        file = gr.File(label="Upload Resume (PDF)", file_types=[".pdf"])
-        question = gr.Textbox(label="Ask a question about your resume", placeholder="e.g. What are my strengths?")
-    answer = gr.Textbox(label="Bot's Answer", lines=5)
-    btn = gr.Button("Ask")
+        pdf_file = gr.File(label="📁 Upload your resume (PDF)", file_types=[".pdf"])
+        question = gr.Textbox(lines=2, label="💬 Ask something", placeholder="e.g., What are the candidate's technical strengths?")
+
+    analyze_button = gr.Button("🔍 Analyze")
+
+    result = gr.Textbox(label="🧠 Answer", lines=10)
+
+    def run_analysis(file, question):
+        return process_pdf(file, question)
 
-    btn.click(fn=process_resume, inputs=[file, question], outputs=answer)
+    analyze_button.click(run_analysis, inputs=[pdf_file, question], outputs=result)
 
-demo.launch()
+# Launch app
+if __name__ == "__main__":
+    demo.launch()