on1onmangoes commited on
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f1ef731
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1 Parent(s): b951b08

Update app.py

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  1. app.py +1 -809
app.py CHANGED
@@ -6,7 +6,7 @@ import os
6
  HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using an environment variable
7
 
8
  # Initialize the Gradio Client for the specified API
9
- client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
10
 
11
 
12
  # Function to handle chat API call
@@ -40,58 +40,6 @@ def stream_chat_with_rag(
40
  # Return the assistant's reply
41
  return response
42
 
43
-
44
-
45
-
46
- # # OG code in V9
47
- # def stream_chat_with_rag(
48
- # message: str,
49
- # history: list,
50
- # client_name: str,
51
- # system_prompt: str,
52
- # num_retrieved_docs: int = 10,
53
- # num_docs_final: int = 9,
54
- # temperature: float = 0,
55
- # max_new_tokens: int = 1024,
56
- # top_p: float = 1.0,
57
- # top_k: int = 20,
58
- # penalty: float = 1.2,
59
- # ):
60
-
61
- # # Function to handle chat API call
62
- # # def stream_chat_with_rag(message, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
63
- # # response = client.predict(
64
- # # message=message,
65
- # # client_name="rosariarossi", # Hardcoded client name
66
- # # system_prompt=system_prompt,
67
- # # num_retrieved_docs=num_retrieved_docs,
68
- # # num_docs_final=num_docs_final,
69
- # # temperature=temperature,
70
- # # max_new_tokens=max_new_tokens,
71
- # # top_p=top_p,
72
- # # top_k=top_k,
73
- # # penalty=penalty,
74
- # # api_name="/chat"
75
- # # )
76
- # # return response
77
-
78
- # result = client.predict(
79
- # message=message,
80
- # client_name="rosariarossi",
81
- # system_prompt="You are an expert assistant",
82
- # num_retrieved_docs=10,
83
- # num_docs_final=9,
84
- # temperature=0,
85
- # max_new_tokens=1024,
86
- # top_p=1,
87
- # top_k=20,
88
- # penalty=1.2,
89
- # api_name="/chat"
90
- # )
91
- # return result
92
-
93
-
94
-
95
  # Function to handle PDF processing API call
96
  def process_pdf(pdf_file):
97
  return client.predict(
@@ -121,84 +69,6 @@ TITLE = "<h1 style='text-align:center;'>My Gradio Chat App</h1>"
121
  # Create the Gradio Blocks interface
122
  with gr.Blocks(css=CSS) as demo:
123
  gr.HTML(TITLE)
124
- ## OG v9 comments
125
- # gr.ChatInterface(
126
- # fn=stream_chat_with_rag,
127
- # chatbot=chatbot,
128
- # fill_height=True,
129
- # #gr.dropdown(['rosariarossi','bianchifiordaliso','lorenzoverdi'],label="Select Client"),
130
- # additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
131
- # additional_inputs=[
132
- # gr.Dropdown(['rosariarossi','bianchifiordaliso','lorenzoverdi'],value="rosariarossi",label="Select Client", render=False,),
133
- # gr.Textbox(
134
- # # value="""Using the information contained in the context,
135
- # # give a comprehensive answer to the question.
136
- # # Respond only to the question asked, response should be concise and relevant to the question.
137
- # # Provide the number of the source document when relevant.
138
- # # If the answer cannot be deduced from the context, do not give an answer""",
139
- # value ="""You are an expert assistant""",
140
- # label="System Prompt",
141
- # render=False,
142
- # ),
143
- # gr.Slider(
144
- # minimum=1,
145
- # maximum=10,
146
- # step=1,
147
- # value=10,
148
- # label="Number of Initial Documents to Retrieve",
149
- # render=False,
150
- # ),
151
- # gr.Slider(
152
- # minimum=1,
153
- # maximum=10,
154
- # step=1,
155
- # value=9,
156
- # label="Number of Final Documents to Retrieve",
157
- # render=False,
158
- # ),
159
- # gr.Slider(
160
- # minimum=0.2,
161
- # maximum=1,
162
- # step=0.1,
163
- # value=0,
164
- # label="Temperature",
165
- # render=False,
166
- # ),
167
- # gr.Slider(
168
- # minimum=128,
169
- # maximum=8192,
170
- # step=1,
171
- # value=1024,
172
- # label="Max new tokens",
173
- # render=False,
174
- # ),
175
- # gr.Slider(
176
- # minimum=0.0,
177
- # maximum=1.0,
178
- # step=0.1,
179
- # value=1.0,
180
- # label="top_p",
181
- # render=False,
182
- # ),
183
- # gr.Slider(
184
- # minimum=1,
185
- # maximum=20,
186
- # step=1,
187
- # value=20,
188
- # label="top_k",
189
- # render=False,
190
- # ),
191
- # gr.Slider(
192
- # minimum=0.0,
193
- # maximum=2.0,
194
- # step=0.1,
195
- # value=1.2,
196
- # label="Repetition penalty",
197
- # render=False,
198
- # ),
199
- # ],
200
-
201
- # )
202
  with gr.Tab("Chat"):
203
  chatbot = gr.Chatbot() # Create a chatbot interface
204
 
@@ -310,681 +180,3 @@ if __name__ == "__main__":
310
  demo.launch()
311
 
312
 
313
-
314
-
315
-
316
-
317
-
318
-
319
-
320
- # import gradio as gr
321
- # from gradio_client import Client, handle_file
322
- # import os
323
-
324
- # # Define your Hugging Face token (make sure to set it as an environment variable)
325
- # HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using an environment variable
326
-
327
- # # Initialize the Gradio Client for the specified API
328
- # client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
329
-
330
- # # Function to handle chat API call
331
- # def stream_chat_with_rag(message, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
332
- # response = client.predict(
333
- # message=message,
334
- # client_name="rosariarossi", # Hardcoded client name
335
- # system_prompt=system_prompt,
336
- # num_retrieved_docs=num_retrieved_docs,
337
- # num_docs_final=num_docs_final,
338
- # temperature=temperature,
339
- # max_new_tokens=max_new_tokens,
340
- # top_p=top_p,
341
- # top_k=top_k,
342
- # penalty=penalty,
343
- # api_name="/chat"
344
- # )
345
- # return response
346
-
347
- # # Function to handle PDF processing API call
348
- # def process_pdf(pdf_file):
349
- # return client.predict(
350
- # pdf_file=handle_file(pdf_file),
351
- # client_name="rosariarossi", # Hardcoded client name
352
- # api_name="/process_pdf2"
353
- # )[1] # Return only the result string
354
-
355
- # # Function to handle search API call
356
- # def search_api(query):
357
- # return client.predict(query=query, api_name="/search_with_confidence")
358
-
359
- # # Function to handle RAG API call
360
- # def rag_api(question):
361
- # return client.predict(question=question, api_name="/answer_with_rag")
362
-
363
- # # CSS for custom styling
364
- # CSS = """
365
- # # chat-container {
366
- # height: 100vh;
367
- # }
368
- # """
369
-
370
- # # Title for the application
371
- # TITLE = "<h1 style='text-align:center;'>My Gradio Chat App</h1>"
372
-
373
- # # Create the Gradio Blocks interface
374
- # with gr.Blocks(css=CSS) as demo:
375
- # gr.HTML(TITLE)
376
-
377
- # with gr.Tab("Chat"):
378
- # chatbot = gr.Chatbot() # Create a chatbot interface
379
-
380
- # chat_interface = gr.ChatInterface(
381
- # fn=stream_chat_with_rag,
382
- # chatbot=chatbot,
383
- # additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
384
- # additional_inputs=[
385
- # gr.Dropdown(
386
- # ['rosariarossi', 'bianchifiordaliso', 'lorenzoverdi'],
387
- # value="rosariarossi",
388
- # label="Select Client",
389
- # render=False,
390
- # ),
391
- # gr.Textbox(
392
- # value="You are an expert assistant",
393
- # label="System Prompt",
394
- # render=False,
395
- # ),
396
- # gr.Slider(
397
- # minimum=1,
398
- # maximum=10,
399
- # step=1,
400
- # value=10,
401
- # label="Number of Initial Documents to Retrieve",
402
- # render=False,
403
- # ),
404
- # gr.Slider(
405
- # minimum=1,
406
- # maximum=10,
407
- # step=1,
408
- # value=9,
409
- # label="Number of Final Documents to Retrieve",
410
- # render=False,
411
- # ),
412
- # gr.Slider(
413
- # minimum=0.2,
414
- # maximum=1,
415
- # step=0.1,
416
- # value=0,
417
- # label="Temperature",
418
- # render=False,
419
- # ),
420
- # gr.Slider(
421
- # minimum=128,
422
- # maximum=8192,
423
- # step=1,
424
- # value=1024,
425
- # label="Max new tokens",
426
- # render=False,
427
- # ),
428
- # gr.Slider(
429
- # minimum=0.0,
430
- # maximum=1.0,
431
- # step=0.1,
432
- # value=1.0,
433
- # label="Top P",
434
- # render=False,
435
- # ),
436
- # gr.Slider(
437
- # minimum=1,
438
- # maximum=20,
439
- # step=1,
440
- # value=20,
441
- # label="Top K",
442
- # render=False,
443
- # ),
444
- # gr.Slider(
445
- # minimum=0.0,
446
- # maximum=2.0,
447
- # step=0.1,
448
- # value=1.2,
449
- # label="Repetition Penalty",
450
- # render=False,
451
- # ),
452
- # ],
453
- # )
454
-
455
- # with gr.Tab("Process PDF"):
456
- # pdf_input = gr.File(label="Upload PDF File")
457
- # pdf_output = gr.Textbox(label="PDF Result", interactive=False)
458
-
459
- # pdf_button = gr.Button("Process PDF")
460
- # pdf_button.click(
461
- # process_pdf,
462
- # inputs=[pdf_input],
463
- # outputs=pdf_output
464
- # )
465
-
466
- # with gr.Tab("Search"):
467
- # query_input = gr.Textbox(label="Enter Search Query")
468
- # search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
469
-
470
- # search_button = gr.Button("Search")
471
- # search_button.click(
472
- # search_api,
473
- # inputs=query_input,
474
- # outputs=search_output
475
- # )
476
-
477
- # with gr.Tab("Answer with RAG"):
478
- # question_input = gr.Textbox(label="Enter Question for RAG")
479
- # rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)
480
-
481
- # rag_button = gr.Button("Get Answer")
482
- # rag_button.click(
483
- # rag_api,
484
- # inputs=question_input,
485
- # outputs=rag_output
486
- # )
487
-
488
- # # Launch the app
489
- # if __name__ == "__main__":
490
- # demo.launch()
491
-
492
-
493
-
494
-
495
-
496
-
497
-
498
-
499
-
500
-
501
-
502
-
503
- # import gradio as gr
504
- # from gradio_client import Client, handle_file
505
- # import os
506
-
507
- # # Define your Hugging Face token (make sure to set it as an environment variable)
508
- # HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using an environment variable
509
-
510
- # # Initialize the Gradio Client for the specified API
511
- # client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
512
-
513
- # # Function to handle chat API call
514
- # def stream_chat_with_rag(message, client_name, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
515
- # response = client.predict(
516
- # message=message,
517
- # client_name=client_name,
518
- # system_prompt=system_prompt,
519
- # num_retrieved_docs=num_retrieved_docs,
520
- # num_docs_final=num_docs_final,
521
- # temperature=temperature,
522
- # max_new_tokens=max_new_tokens,
523
- # top_p=top_p,
524
- # top_k=top_k,
525
- # penalty=penalty,
526
- # api_name="/chat"
527
- # )
528
- # return response
529
-
530
- # # Function to handle PDF processing API call
531
- # def process_pdf(pdf_file, client_name):
532
- # return client.predict(
533
- # pdf_file=handle_file(pdf_file),
534
- # client_name=client_name,
535
- # api_name="/process_pdf2"
536
- # )[1] # Return only the result string
537
-
538
- # # Function to handle search API call
539
- # def search_api(query):
540
- # return client.predict(query=query, api_name="/search_with_confidence")
541
-
542
- # # Function to handle RAG API call
543
- # def rag_api(question):
544
- # return client.predict(question=question, api_name="/answer_with_rag")
545
-
546
- # # Create the Gradio Blocks interface
547
- # with gr.Blocks() as app:
548
- # gr.Markdown("### Login")
549
-
550
- # with gr.Row():
551
- # username_input = gr.Textbox(label="Username", placeholder="Enter your username")
552
- # password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
553
-
554
- # with gr.Tab("Chat"):
555
- # chatbot = gr.Chatbot() # Create a chatbot interface
556
-
557
- # chat_interface = gr.ChatInterface(
558
- # fn=stream_chat_with_rag,
559
- # chatbot=chatbot,
560
- # fill_height=True,
561
- # additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
562
- # additional_inputs=[
563
- # gr.Dropdown(
564
- # ['rosariarossi', 'bianchifiordaliso', 'lorenzoverdi'],
565
- # value="rosariarossi",
566
- # label="Select Client",
567
- # render=False,
568
- # ),
569
- # gr.Textbox(
570
- # value="You are an expert assistant",
571
- # label="System Prompt",
572
- # render=False,
573
- # ),
574
- # gr.Slider(
575
- # minimum=1,
576
- # maximum=10,
577
- # step=1,
578
- # value=10,
579
- # label="Number of Initial Documents to Retrieve",
580
- # render=False,
581
- # ),
582
- # gr.Slider(
583
- # minimum=1,
584
- # maximum=10,
585
- # step=1,
586
- # value=9,
587
- # label="Number of Final Documents to Retrieve",
588
- # render=False,
589
- # ),
590
- # gr.Slider(
591
- # minimum=0.2,
592
- # maximum=1,
593
- # step=0.1,
594
- # value=0,
595
- # label="Temperature",
596
- # render=False,
597
- # ),
598
- # gr.Slider(
599
- # minimum=128,
600
- # maximum=8192,
601
- # step=1,
602
- # value=1024,
603
- # label="Max new tokens",
604
- # render=False,
605
- # ),
606
- # gr.Slider(
607
- # minimum=0.0,
608
- # maximum=1.0,
609
- # step=0.1,
610
- # value=1.0,
611
- # label="Top P",
612
- # render=False,
613
- # ),
614
- # gr.Slider(
615
- # minimum=1,
616
- # maximum=20,
617
- # step=1,
618
- # value=20,
619
- # label="Top K",
620
- # render=False,
621
- # ),
622
- # gr.Slider(
623
- # minimum=0.0,
624
- # maximum=2.0,
625
- # step=0.1,
626
- # value=1.2,
627
- # label="Repetition Penalty",
628
- # render=False,
629
- # ),
630
- # ],
631
- # )
632
-
633
- # with gr.Tab("Process PDF"):
634
- # pdf_input = gr.File(label="Upload PDF File")
635
- # pdf_output = gr.Textbox(label="PDF Result", interactive=False)
636
-
637
- # pdf_button = gr.Button("Process PDF")
638
- # pdf_button.click(
639
- # process_pdf,
640
- # inputs=[pdf_input, client_name_dropdown],
641
- # outputs=pdf_output
642
- # )
643
-
644
- # with gr.Tab("Search"):
645
- # query_input = gr.Textbox(label="Enter Search Query")
646
- # search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
647
-
648
- # search_button = gr.Button("Search")
649
- # search_button.click(
650
- # search_api,
651
- # inputs=query_input,
652
- # outputs=search_output
653
- # )
654
-
655
- # with gr.Tab("Answer with RAG"):
656
- # question_input = gr.Textbox(label="Enter Question for RAG")
657
- # rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)
658
-
659
- # rag_button = gr.Button("Get Answer")
660
- # rag_button.click(
661
- # rag_api,
662
- # inputs=question_input,
663
- # outputs=rag_output
664
- # )
665
-
666
- # # Launch the app
667
- # app.launch()
668
-
669
-
670
-
671
-
672
-
673
-
674
-
675
-
676
-
677
-
678
- # import gradio as gr
679
- # from gradio_client import Client, handle_file
680
- # import os
681
-
682
- # # Define your Hugging Face token (make sure to set it as an environment variable)
683
- # HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using env variable
684
-
685
- # # Initialize the Gradio Client for the specified API
686
- # client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
687
-
688
- # # Authentication function
689
- # def login(username, password):
690
- # if username == "your_username" and password == "your_password": # Update with actual credentials
691
- # return True
692
- # else:
693
- # return False
694
-
695
- # # Function to handle different API calls based on user input
696
- # def handle_api_call(username, password, message=None, client_name="rosariarossi",
697
- # system_prompt="You are an expert assistant", num_retrieved_docs=10,
698
- # num_docs_final=9, temperature=0, max_new_tokens=1024,
699
- # top_p=1, top_k=20, penalty=1.2,
700
- # pdf_file=None, query=None, question=None):
701
-
702
- # if not login(username, password):
703
- # return "Invalid credentials! Please try again."
704
-
705
- # if message:
706
- # # Handle chat message
707
- # chat_result = client.predict(
708
- # message=message,
709
- # client_name=client_name,
710
- # system_prompt=system_prompt,
711
- # num_retrieved_docs=num_retrieved_docs,
712
- # num_docs_final=num_docs_final,
713
- # temperature=temperature,
714
- # max_new_tokens=max_new_tokens,
715
- # top_p=top_p,
716
- # top_k=top_k,
717
- # penalty=penalty,
718
- # api_name="/chat"
719
- # )
720
- # return chat_result
721
- # elif pdf_file:
722
- # # Handle PDF file
723
- # pdf_result = client.predict(
724
- # pdf_file=handle_file(pdf_file),
725
- # client_name=client_name,
726
- # api_name="/process_pdf2"
727
- # )
728
- # return pdf_result[1] # Returning the string result from the PDF processing
729
- # elif query:
730
- # # Handle search query
731
- # search_result = client.predict(query=query, api_name="/search_with_confidence")
732
- # return search_result
733
- # elif question:
734
- # # Handle question for RAG
735
- # rag_result = client.predict(question=question, api_name="/answer_with_rag")
736
- # return rag_result
737
- # else:
738
- # return "No valid input provided!"
739
-
740
- # # Create the Gradio Blocks interface
741
- # with gr.Blocks() as app:
742
- # gr.Markdown("### Login")
743
-
744
- # with gr.Row():
745
- # username_input = gr.Textbox(label="Username", placeholder="Enter your username")
746
- # password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
747
-
748
- # with gr.Tab("Chat"):
749
- # message_input = gr.Textbox(label="Message", placeholder="Type your message here")
750
-
751
- # gr.Markdown("### Client Options")
752
- # client_name_dropdown = gr.Dropdown(
753
- # label="Select Client",
754
- # choices=["rosariarossi", "bianchifiordaliso", "lorenzoverdi"],
755
- # value="rosariarossi"
756
- # )
757
-
758
- # system_prompt_input = gr.Textbox(
759
- # label="System Prompt",
760
- # placeholder="Enter system prompt here",
761
- # value="You are an expert assistant"
762
- # )
763
-
764
- # num_retrieved_docs_slider = gr.Slider(
765
- # label="Number of Initial Documents to Retrieve",
766
- # minimum=1,
767
- # maximum=100,
768
- # step=1,
769
- # value=10
770
- # )
771
-
772
- # num_docs_final_slider = gr.Slider(
773
- # label="Number of Final Documents to Retrieve",
774
- # minimum=1,
775
- # maximum=100,
776
- # step=1,
777
- # value=9
778
- # )
779
-
780
- # temperature_slider = gr.Slider(
781
- # label="Temperature",
782
- # minimum=0,
783
- # maximum=2,
784
- # step=0.1,
785
- # value=0
786
- # )
787
-
788
- # max_new_tokens_slider = gr.Slider(
789
- # label="Max New Tokens",
790
- # minimum=1,
791
- # maximum=2048,
792
- # step=1,
793
- # value=1024
794
- # )
795
-
796
- # top_p_slider = gr.Slider(
797
- # label="Top P",
798
- # minimum=0,
799
- # maximum=1,
800
- # step=0.01,
801
- # value=1
802
- # )
803
-
804
- # top_k_slider = gr.Slider(
805
- # label="Top K",
806
- # minimum=1,
807
- # maximum=100,
808
- # step=1,
809
- # value=20
810
- # )
811
-
812
- # penalty_slider = gr.Slider(
813
- # label="Repetition Penalty",
814
- # minimum=1,
815
- # maximum=5,
816
- # step=0.1,
817
- # value=1.2
818
- # )
819
-
820
- # chat_output = gr.Textbox(label="Chat Response", interactive=False)
821
-
822
- # with gr.Tab("Process PDF"):
823
- # pdf_input = gr.File(label="Upload PDF File")
824
- # pdf_output = gr.Textbox(label="PDF Result", interactive=False)
825
-
826
- # with gr.Tab("Search"):
827
- # query_input = gr.Textbox(label="Enter Search Query")
828
- # search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
829
-
830
- # with gr.Tab("Answer with RAG"):
831
- # question_input = gr.Textbox(label="Enter Question for RAG")
832
- # rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)
833
-
834
- # api_button = gr.Button("Submit")
835
-
836
- # # Bind the button click to the handle_api_call function
837
- # api_button.click(
838
- # handle_api_call,
839
- # inputs=[
840
- # username_input, password_input,
841
- # message_input, client_name_dropdown,
842
- # system_prompt_input, num_retrieved_docs_slider,
843
- # num_docs_final_slider, temperature_slider,
844
- # max_new_tokens_slider, top_p_slider,
845
- # top_k_slider, penalty_slider,
846
- # pdf_input, query_input, question_input
847
- # ],
848
- # outputs=[
849
- # chat_output, pdf_output, search_output, rag_output
850
- # ]
851
- # )
852
-
853
- # # Launch the app
854
- # app.launch()
855
-
856
-
857
-
858
-
859
-
860
-
861
-
862
- # import gradio as gr
863
- # from gradio_client import Client, handle_file
864
- # import os
865
-
866
- # # Define your Hugging Face token (make sure to set it as an environment variable)
867
- # HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using env variable
868
-
869
- # # Initialize the Gradio Client for the specified API
870
- # client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
871
-
872
- # # Authentication function
873
- # def login(username, password):
874
- # if username == "your_username" and password == "your_password": # Update with actual credentials
875
- # return True
876
- # else:
877
- # return False
878
-
879
- # # Function to handle different API calls based on user input
880
- # def handle_api_call(username, password, audio_file=None, pdf_file=None, message=None, query=None, question=None):
881
- # if not login(username, password):
882
- # return "Invalid credentials! Please try again."
883
-
884
- # if audio_file:
885
- # # Handle audio file using the appropriate API
886
- # result = client.predict(audio=handle_file(audio_file), api_name="/process_audio") # Example endpoint for audio processing
887
- # return result
888
- # elif pdf_file:
889
- # # Handle PDF file
890
- # pdf_result = client.predict(pdf_file=handle_file(pdf_file), client_name="rosariarossi", api_name="/process_pdf2")
891
- # return pdf_result[1] # Returning the string result from the PDF processing
892
- # elif message:
893
- # # Handle chat message
894
- # chat_result = client.predict(
895
- # message=message,
896
- # client_name="rosariarossi",
897
- # system_prompt="You are an expert assistant",
898
- # num_retrieved_docs=10,
899
- # num_docs_final=9,
900
- # temperature=0,
901
- # max_new_tokens=1024,
902
- # top_p=1,
903
- # top_k=20,
904
- # penalty=1.2,
905
- # api_name="/chat"
906
- # )
907
- # return chat_result
908
- # elif query:
909
- # # Handle search query
910
- # search_result = client.predict(query=query, api_name="/search_with_confidence")
911
- # return search_result
912
- # elif question:
913
- # # Handle question for RAG
914
- # rag_result = client.predict(question=question, api_name="/answer_with_rag")
915
- # return rag_result
916
- # else:
917
- # return "No valid input provided!"
918
-
919
- # # Create the Gradio Blocks interface
920
- # with gr.Blocks() as app:
921
- # gr.Markdown("### Login")
922
-
923
- # with gr.Row():
924
- # username_input = gr.Textbox(label="Username", placeholder="Enter your username")
925
- # password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
926
-
927
- # audio_input = gr.Audio(label="Upload Audio File", type="filepath")
928
- # pdf_input = gr.File(label="Upload PDF File")
929
-
930
- # message_input = gr.Textbox(label="Enter Message for Chat")
931
- # query_input = gr.Textbox(label="Enter Search Query")
932
- # question_input = gr.Textbox(label="Enter Question for RAG")
933
-
934
- # output_text = gr.Textbox(label="Output", interactive=False)
935
-
936
- # # Bind the button click to the handle_api_call function
937
- # api_button = gr.Button("Submit")
938
- # api_button.click(
939
- # handle_api_call,
940
- # inputs=[username_input, password_input, audio_input, pdf_input, message_input, query_input, question_input],
941
- # outputs=output_text
942
- # )
943
-
944
- # # Launch the app
945
- # app.launch()
946
-
947
-
948
-
949
-
950
-
951
- # import gradio as gr
952
-
953
- # # Define a function for the main application
954
- # def greet(name):
955
- # return f"Hello {name}!"
956
-
957
- # # Define a function for the authentication
958
- # def login(username, password):
959
- # if username == "your_username" and password == "your_password":
960
- # return True
961
- # else:
962
- # return False
963
-
964
- # # Create the Gradio Blocks interface
965
- # with gr.Blocks() as app:
966
- # gr.Markdown("### Login")
967
-
968
- # with gr.Row():
969
- # username_input = gr.Textbox(label="Username", placeholder="Enter your username")
970
- # password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
971
-
972
- # login_button = gr.Button("Login")
973
- # output_text = gr.Textbox(label="Output", interactive=False)
974
-
975
- # # Function to handle login and display greeting
976
- # def handle_login(username, password):
977
- # if login(username, password):
978
- # # Clear the password field and display the greeting
979
- # #password_input.clear()
980
- # return greet(username)
981
- # else:
982
- # return "Invalid credentials! Please try again."
983
-
984
- # # Bind the button click to the handle_login function
985
- # login_button.click(handle_login, inputs=[username_input, password_input], outputs=output_text)
986
-
987
- # # Launch the app
988
- # app.launch()
989
-
990
-
 
6
  HF_TOKEN = os.getenv("HF_TOKEN") # Replace with your actual token if not using an environment variable
7
 
8
  # Initialize the Gradio Client for the specified API
9
+ client = Client("on1onmangoes/CNIHUB10724v10", hf_token=HF_TOKEN)
10
 
11
 
12
  # Function to handle chat API call
 
40
  # Return the assistant's reply
41
  return response
42
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
43
  # Function to handle PDF processing API call
44
  def process_pdf(pdf_file):
45
  return client.predict(
 
69
  # Create the Gradio Blocks interface
70
  with gr.Blocks(css=CSS) as demo:
71
  gr.HTML(TITLE)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
72
  with gr.Tab("Chat"):
73
  chatbot = gr.Chatbot() # Create a chatbot interface
74
 
 
180
  demo.launch()
181
 
182