Final commit

app.py

@@ -1,13 +1,9 @@
 import numpy as np
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
- 
-def flip_text(x):
-    return x[::-1]
- 
-def flip_image(x):
-    return np.fliplr(x)
 
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, T5Tokenizer, T5Model, BertTokenizer, BertModel, T5ForConditionalGeneration,  AutoTokenizer, AutoModelForSeq2SeqLM
+
+# 1. GENERATE SUMMARY
 tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
 model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
 
@@ -17,23 +13,108 @@ def generate_summary(text):
     summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
+
+
+
+# 2. TRANSLATE FUNCTION
+t5_tokenizer = T5Tokenizer.from_pretrained('t5-small')
+t5_model = T5ForConditionalGeneration.from_pretrained('t5-small')
+
+def translate_text(text_to_translate, original_language, destination_language):
+    input_text = "translate "+original_language+" to "+destination_language+": "+text_to_translate
+
+    input_ids = t5_tokenizer.encode(input_text, return_tensors='pt')
+
+    outputs = t5_model.generate(input_ids)
+    output_text = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return(output_text)
+
+
+
+# 4. QUESTION ANSWERING FUNCTION
+def question_answering(question,context):
+    qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
+    question = question
+    context = context
+    solution = qa_model(question = question, context = context)
+    return solution['answer']
+
+
+
+# 5. PARAPHRASING FUNCTION
+paraphrasing_tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing", model_input_names=['input_ids', 'attention_mask'])
+paraphrasing_model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing")
+
+def paraphrasing(text):
+    input_text= text
+
+    token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda')
+    outputs = model.generate(**token_input)
+    return(tokenizer.decode(outputs[0]))
+
+
+
+
  
-with gr.Blocks() as main:
+with gr.Blocks() as demo:
     gr.Markdown("My AI interface")
     with gr.Tab("Single models"):
-        text_to_summarize = gr.Textbox(label="Text to summarize")
-        summary_output = gr.Textbox(label="Summary")
-        summarize_btn = gr.Button("Summarize")
+        # 1. GENERATE SUMMARY
+        with gr.Accordion("Text summarization"):
+            gr.Markdown("Single model summarization using BART model")
+            text_to_summarize = gr.Textbox(label="Text to summarize")
+            summary_output = gr.Textbox(label="Summary")
+            summarize_btn = gr.Button("Summarize")
+
+
+        # 2. TRANSLATE FUNCTION
+        with gr.Accordion("Text translation", open=False):
+            gr.Markdown("Single model translation using GOOGLE T5 Base model")
+            text_to_translate = gr.Textbox(label="Text to translate")
+            original_language = gr.Textbox(label="Original language (Write in full form e.g. english)")
+            destination_language = gr.Textbox(label="Destination language (Write in full form e.g. deutsch)")
+            translate_output = gr.Textbox(label="Translation")
+            translate_btn = gr.Button("Translate")
+
+
+        # 3. ..
+        with gr.Accordion("Scentence fill mask", open=False):
+            gr.Markdown("Single model translation using GOOGLE T5 Base model")
+            scentence_To_fill = gr.Textbox(label="Text to translate")
+            filled_scentence = gr.Textbox(label="Translation")
+            fill_button = gr.Button("Fill scentence")
+
+
+        # 4. QUESTION ANSWERING 
+        with gr.Accordion("Question answering", open=False):
+            gr.Markdown("Single model question answering using GOOGLE mdeberta model")
+            question = gr.Textbox(label="Question")
+            context = gr.Textbox(label="Context for question")
+            answer = gr.Textbox(label="Answer to question")
+            ask_question_button = gr.Button("Ask question")
+
+
+        # 5. PARAPHRASING
+        with gr.Accordion("Paraphrasing", open=False):
+            gr.Markdown("Single model paraphrasing using the VBART model")
+            scentence_to_rephrase = gr.Textbox(label="Text to rephrase")
+            rephrased_scentence = gr.Textbox(label="Rephrased scentence")
+            paraphrase_button = gr.Button("Rephrase scentence")
 
 
     with gr.Tab("Multi models"):
         with gr.Row():
-            image_input = gr.Image()
-            image_output = gr.Image()
-        image_button = gr.Button("Flip")
+            print("No multi models yet..")
  
-    # text_button.click(flip_text, inputs=text_input, outputs=text_output)
-    image_button.click(flip_image, inputs=image_input, outputs=image_output)
-    summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output)
+
+    # Button listeners
+    summarize_btn.click(generate_summary, inputs=text_to_summarize, outputs=summary_output) # 1. GENERATE SUMMARY
+    translate_btn.click(translate_text, inputs=[text_to_translate, original_language, destination_language], outputs=translate_output) # 2. TRANSLATE FUNCTION
+
+    ask_question_button.click(question_answering, inputs=[question,context], outputs=answer) # 4. QUESTION ANSWERING
+    paraphrase_button.click(paraphrasing, inputs=scentence_to_rephrase, outputs=rephrased_scentence) # 5. PARAPHRASING
+
+
  
-main.launch()
+demo.launch()

requirements.txt

@@ -1,2 +1,3 @@
 torchvision==0.17.2
-transformers==4.40.0 
+transformers==4.40.0 
+sentencepiece==0.2.0