Update app.py

app.py

@@ -105,24 +105,28 @@ def load_sample_pdf():
 
     return "PDF(s) indexed successfully!"
 
+last_relevant_info_state = gr.State("")
 
 def format_docs(docs):
     return "\n\n".join(doc.page_content for doc in docs)
-    
 
 def generate_response(query, history, model, temperature, max_tokens, top_p, seed):
+    global last_relevant_info_state # Declare global to modify the state value
+    
     if vector_store is None:
         return "Please upload and index a PDF at the Indexing tab."
 
     if seed == 0:
         seed = random.randint(1, 100000)
 
+    last_relevant_info_state.value = "" 
     retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
     llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
     custom_rag_prompt = PromptTemplate.from_template(template)
 
     docs = retriever.invoke(query)
-    relevant_info = format_docs(docs)    
+    relevant_info = format_docs(docs)
+    last_relevant_info_state.value = relevant_info
     
     rag_chain = (
         {"context": retriever | format_docs, "question": RunnablePassthrough()}
@@ -135,9 +139,8 @@ def generate_response(query, history, model, temperature, max_tokens, top_p, see
    
     return relevant_info
 
-
-
-
+def get_relevant_info(state):
+    return state # The state's value is what we want to display
 
 additional_inputs = [
     gr.Dropdown(choices=["llama-3.3-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it"], value="gemma2-9b-it", label="Model"),
@@ -148,7 +151,8 @@ additional_inputs = [
 ]
 
 # Create the Gradio interface
-with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
+with gr.Blocks(theme=gr.themes.Default()) as demo:
+    gr_last_relevant_info_state = gr.State("")
     with gr.Tab("Indexing"):
         gr.Markdown(desc)
         # pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
@@ -171,12 +175,17 @@ with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
                     # additional_outputs=[relevant_info],
                     cache_examples=False,
                 )
-            # with gr.Column():
-            #     relevant_info = gr.Textbox(
-            #         label="Retrieved Information",
-            #         interactive=False,
-            #         lines=20, 
-            #     )
-
+            with gr.Column():
+                retrieve_button = gr.Button("Retrieve Relevant Info")
+                relevant_info = gr.Textbox(
+                    label="Retrieved Information",
+                    interactive=False,
+                    lines=20, 
+                )
+        retrieve_button.click(
+            fn=get_relevant_info,
+            inputs=[gr_last_relevant_info_state], # Input is the state component
+            outputs=[relevant_info] # Output updates the relevant_info textbox
+            
 # Launch the Gradio app
 demo.launch(share=True)