Update app.py

app.py

@@ -1,31 +1,40 @@
+import os
 import gradio as gr
 
-# ✅ Use langchain_community imports
-from langchain.text_splitter import CharacterTextSplitter   # stays in main langchain
+# ✅ LangChain imports
+from langchain.text_splitter import CharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain    # stays in main langchain
+from langchain.chains import ConversationalRetrievalChain
 from langchain_community.llms import HuggingFaceHub
 from langchain_community.document_loaders import PyPDFLoader
 
-# 1️⃣ Load your PDF
-# Make sure chimera.pdf is uploaded in the Files tab of Hugging Face Space
-loader = PyPDFLoader("chimera.pdf")  
+# --- 1️⃣ Load your PDF ---
+current_dir = os.path.dirname(__file__)
+pdf_path = os.path.join(current_dir, "chimera.pdf")
+loader = PyPDFLoader(pdf_path)
 documents = loader.load()
 
-# 2️⃣ Split into chunks
+# --- 2️⃣ Split into chunks ---
 text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=100)
 texts = text_splitter.split_documents(documents)
 
-# 3️⃣ Create embeddings + vector DB
+# --- 3️⃣ Create embeddings + FAISS vector store ---
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 db = FAISS.from_documents(texts, embeddings)
 
-# 4️⃣ Build retriever-based chatbot
+# --- 4️⃣ Build retriever-based chatbot ---
 retriever = db.as_retriever(search_kwargs={"k": 3})
 
+# --- Hugging Face Hub LLM setup with secret token ---
+llm = HuggingFaceHub(
+    repo_id="google/flan-t5-base",   # smaller for faster response
+    model_kwargs={"temperature":0},
+    huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+)
+
 qa = ConversationalRetrievalChain.from_llm(
-    HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature":0}),
+    llm,
     retriever=retriever
 )
 
@@ -33,13 +42,54 @@ chat_history = []
 
 def respond(message, history):
     global chat_history
+    chat_history = chat_history[-6:]  # keep last 3 exchanges
     result = qa({"question": message, "chat_history": chat_history})
     chat_history.append((message, result["answer"]))
-    return result["answer"]
+    return result["answer"], chat_history
 
-# 5️⃣ Simple Gradio UI
-chatbot = gr.ChatInterface(respond)
+# --- 5️⃣ Gradio Blocks UI with Entry Warning ---
+with gr.Blocks() as demo:
+    with gr.Column():
+        # Warning message
+        warning_text = gr.HTML(
+            """
+            <div style="background-color:black;color:white;padding:20px;font-family:monospace;font-size:18px;">
+            ⚠ WARNING — INVESTIGATIVE SIMULATION ⚠<br><br>
+            You are about to enter <b>The Chimera Case</b>, a high-stakes investigation into Innovate Future Labs (IFL) and Project Chimera.<br>
+            The scenario contains allegations, leaked files, and disputed testimonies. Treat every claim as unverified until verified by evidence.<br>
+            Your decisions and observations will guide your understanding of the case.<br><br>
+            Are you ready to proceed?
+            </div>
+            """
+        )
 
-if __name__ == "__main__":
-    chatbot.launch()
+        # Buttons
+        enter_btn = gr.Button("Enter the Case")
+        exit_btn = gr.Button("Exit")
+
+        # Chatbot (hidden initially)
+        chatbot = gr.Chatbot(visible=False)
+        user_input = gr.Textbox(placeholder="Type your message here...", visible=False)
+        submit_btn = gr.Button("Send", visible=False)
+
+    # Button interactions
+    def enter_case():
+        chatbot.visible = True
+        user_input.visible = True
+        submit_btn.visible = True
+        warning_text.update(value="")  # hide warning
+        enter_btn.visible = False
+        exit_btn.visible = False
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 
+    def exit_case():
+        return gr.update(value="<h2>Session ended. You exited the simulation.</h2>"), None, None
+
+    enter_btn.click(enter_case)
+    exit_btn.click(exit_case)
+
+    submit_btn.click(respond, inputs=[user_input, chatbot], outputs=[chatbot, chatbot])
+
+# --- 6️⃣ Launch ---
+if __name__ == "__main__":
+    demo.launch(share=True, enable_queue=True)