Update app.py

app.py

@@ -12,13 +12,12 @@ from langchain.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI
 
 
-
+# ✅ OpenRouter API setup (use Hugging Face Secret)
 os.environ["OPENAI_API_KEY"] = os.environ["OPENROUTER_API_KEY"]
 os.environ["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
 os.environ["OPENAI_API_HEADERS"] = '{"HTTP-Referer":"https://huggingface.co", "X-Title":"PDF-RAG"}'
 
-
-#Section-aware PDF extractor
+# ✅ Load and clean the policy PDF
 def extract_clean_sections(file_path):
     with pdfplumber.open(file_path) as pdf:
         full_text = ""
@@ -40,8 +39,7 @@ def extract_clean_sections(file_path):
             docs.append(Document(page_content=f"{title}:\n{content}", metadata={"section": title}))
     return docs
 
-
-#TF-IDF Embedding for RAG
+# ✅ TF-IDF Embeddings
 class TfidfEmbedding(Embeddings):
     def __init__(self):
         self.vectorizer = TfidfVectorizer()
@@ -55,8 +53,7 @@ class TfidfEmbedding(Embeddings):
     def embed_query(self, text):
         return self.vectorizer.transform([text]).toarray()[0]
 
-
-#  prompt
+# ✅ Prompt Template (no emojis, no markdown)
 TEMPLATE = """
 You are a strict healthcare policy checker for Systems Ltd.
 
@@ -72,17 +69,12 @@ Use the following policy information to support your answer.
 Question: {question}
 Answer:
 """
-
 custom_prompt = PromptTemplate(template=TEMPLATE, input_variables=["context", "question"])
 
-# Global state
-retriever = None
-qa_chain = None
-
 
-# ✅ Process the PDF once when button is clicked
-def load_policy():
-    global retriever, qa_chain
+# ✅ Load the policy at startup
+def initialize_policy():
+    global qa_chain
     docs = extract_clean_sections("healthcare_policy.pdf")
     texts = [doc.page_content for doc in docs]
     embedder = TfidfEmbedding()
@@ -101,7 +93,7 @@ def load_policy():
         temperature=0.0
     )
 
-    qa_chain_local = RetrievalQA.from_chain_type(
+    qa_chain = RetrievalQA.from_chain_type(
         llm=llm,
         chain_type="stuff",
         retriever=retriever,
@@ -109,27 +101,23 @@ def load_policy():
         chain_type_kwargs={"prompt": custom_prompt}
     )
 
-    qa_chain = qa_chain_local
-    return "Policy loaded. You may now ask questions."
-
-
-# ✅ Answer a claim question
+# ✅ Run QA on user question
 def ask_policy_question(question):
     if qa_chain is None:
-        return "Please click 'Ask about claim' to load the policy first."
+        return "The policy is still loading. Please wait."
     try:
         return qa_chain.run(question)
     except Exception as e:
         return f"Error: {str(e)}"
 
 
-# ✅ Gradio UI
+# ✅ Gradio Interface
+qa_chain = None
+status_text = "Loading..."  # Initial status
+
 with gr.Blocks() as demo:
     gr.Markdown("## SL HealthCare Claim Checker (RAG)")
-
-    load_btn = gr.Button("📥 Ask about claim (Load Policy)")
-    load_status = gr.Textbox(label="Status")
-    load_btn.click(fn=load_policy, outputs=load_status)
+    status_box = gr.Textbox(label="Status", value=status_text, interactive=False)
 
     with gr.Row():
         question = gr.Textbox(label="Enter your claim question")
@@ -138,4 +126,13 @@ with gr.Blocks() as demo:
     answer = gr.Textbox(label="Answer", lines=6)
     ask_btn.click(fn=ask_policy_question, inputs=question, outputs=answer)
 
+    # Load the policy on startup
+    def startup():
+        global status_text
+        initialize_policy()
+        status_text = "Policy loaded. You may now ask questions."
+        return status_text
+
+    demo.load(fn=startup, outputs=status_box)
+
 demo.launch()