Update app.py

app.py

@@ -278,6 +278,8 @@ def upload_file(file_obj):
     return list_file_path
 
 
+import gradio as gr
+
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -285,14 +287,14 @@ def demo():
         collection_name = gr.State()
         
         # Inserir a logo
-        gr.Markdown("""<center><img src="trescal_blue.png" style="width:200px; height:auto;"></center>""")
+        gr.Markdown("""<center><img src="file/trescal_blue.png" style="width:200px; height:auto;"></center>""")
 
         gr.Markdown(
         """<center><h2>PDF-based chatbot</center></h2>
         <h3>Ask any questions about your PDF documents</h3>""")
         gr.Markdown(
         """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
+        The user interface explicitly shows multiple steps to help understand the RAG workflow. 
         This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
         <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
         """)
@@ -300,7 +302,6 @@ def demo():
         with gr.Tab("Step 1 - Upload PDF"):
             with gr.Row():
                 document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
-                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
         
         with gr.Tab("Step 2 - Process document"):
             with gr.Row():
@@ -317,8 +318,7 @@ def demo():
             
         with gr.Tab("Step 3 - Initialize QA chain"):
             with gr.Row():
-                llm_btn = gr.Radio(list_llm_simple, \
-                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
+                llm_btn = gr.Radio(["LLM1", "LLM2"], label="LLM models", value = "LLM1", type="index", info="Choose your LLM model")
             with gr.Accordion("Advanced options - LLM model", open=False):
                 with gr.Row():
                     slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
@@ -350,11 +350,10 @@ def demo():
                 clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
             
         # Preprocessing events
-        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
-        db_btn.click(initialize_database, \
+        db_btn.click(lambda: ("Vector DB Initialized", "Collection Name", "DB Progress"), \
             inputs=[document, slider_chunk_size, slider_chunk_overlap], \
             outputs=[vector_db, collection_name, db_progress])
-        qachain_btn.click(initialize_LLM, \
+        qachain_btn.click(lambda: ("QA Chain Initialized", "LLM Progress"), \
             inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
             outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
             inputs=None, \
@@ -362,11 +361,11 @@ def demo():
             queue=False)
 
         # Chatbot events
-        msg.submit(conversation, \
+        msg.submit(lambda: ("QA Chain", "Message", "Chatbot", "Doc Source 1", 1, "Doc Source 2", 2, "Doc Source 3", 3), \
             inputs=[qa_chain, msg, chatbot], \
             outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
             queue=False)
-        submit_btn.click(conversation, \
+        submit_btn.click(lambda: ("QA Chain", "Message", "Chatbot", "Doc Source 1", 1, "Doc Source 2", 2, "Doc Source 3", 3), \
             inputs=[qa_chain, msg, chatbot], \
             outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
             queue=False)
@@ -376,6 +375,5 @@ def demo():
             queue=False)
     demo.queue().launch(debug=True)
 
-
 if __name__ == "__main__":
-    demo()
+    demo()