app.py update with NLI model and skeleton code for remaining models

app.py

@@ -5,22 +5,63 @@ from peft import AutoPeftModelForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA")
 
+tokenizer1 = AutoTokenizer.from_pretrained("albert-base-v2")
+
+
+
 #pretrained models
-#Textclass_pipe = pipeline()
 #STSmodel_pipe = pipeline()
 #NLImodel_pipe = pipeline()
 
-# Handle calls to DistilBERT no LORA
+# Handle calls to DistilBERT 
+distilBERTUntrained_pipe = pipeline("sentiment-analysis", model="bert-base-uncased")
 distilBERTnoLORA_pipe = pipeline(model="Intradiction/text_classification_NoLORA")
 distilBERTwithLORA_pipe = pipeline("sentiment-analysis", model=loraModel, tokenizer=tokenizer)
 
-
+#text class models 
 def distilBERTnoLORA_fn(text):
     return distilBERTnoLORA_pipe(text)
 
 def distilBERTwithLORA_fn(text):
     return distilBERTwithLORA_pipe(text)
 
+def distilBERTUntrained_fn(text):
+    return distilBERTUntrained_pipe(text)
+
+
+# Handle calls to ALBERT
+ALbertUntrained_pipe = pipeline("text-classification", model="albert-base-v2")
+AlbertnoLORA_pipe = pipeline(model="Intradiction/NLI-Conventional-Fine-Tuning")
+#AlbertwithLORA_pipe = pipeline()
+
+#NLI models 
+def AlbertnoLORA_fn(text1, text2):
+    return AlbertnoLORA_pipe(text1, text2)
+
+def AlbertwithLORA_fn(text1, text2):
+    return ("working2")
+
+def AlbertUntrained_fn(text1, text2):
+    return ALbertUntrained_pipe(text1,text2)
+
+
+# Handle calls to Deberta
+#DebertaUntrained_pipe = pipeline()
+#DebertanoLORA_pipe = pipeline()
+#DebertawithLORA_pipe = pipeline()
+
+#STS models
+def DebertanoLORA_fn(text1, text2):
+    return ("working3")
+
+def DebertawithLORA_fn(text1, text2):
+    return ("working2")
+
+def DebertaUntrained_fn(text1, text2):
+    return ("working3")
+
+
+#placeholder
 def chat1(message,history):
     history = history or []
     message = message.lower()
@@ -32,9 +73,6 @@ def chat1(message,history):
     history.append((message, response))
     return history, history
 
-chatbot = gr.Chatbot()
-chatbot1 = gr.Chatbot()
-chatbot2 = gr.Chatbot()
 
 with gr.Blocks(
     title="",
@@ -76,29 +114,30 @@ with gr.Blocks(
 
             with gr.Column():
                 with gr.Row(variant="panel"):
-                    out =  gr.Textbox(label= " DistilBERT no LoRA")
+                    TextClassOut =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out1 = gr.Textbox(label= " DistilBERT with LoRA")
+                    TextClassOut1 = gr.Textbox(label= "Conventionaly Trained Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
+                    TextClassOut2 = gr.Textbox(label= "LoRA Fine Tuned Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
-        btn.click(fn=distilBERTnoLORA_fn, inputs=inp, outputs=out)
-        btn.click(fn=distilBERTwithLORA_fn, inputs=inp, outputs=out1)
-        btn.click(fn=chat1, inputs=inp, outputs=out2)
+        btn.click(fn=distilBERTUntrained_fn, inputs=inp, outputs=TextClassOut)
+        btn.click(fn=distilBERTnoLORA_fn, inputs=inp, outputs=TextClassOut1)
+        btn.click(fn=distilBERTwithLORA_fn, inputs=inp, outputs=TextClassOut2)
+        
 
     with gr.Tab("Natrual Language Infrencing"):
          with gr.Row():
@@ -115,21 +154,21 @@ with gr.Blocks(
             with gr.Column(scale=0.3,variant="panel"):
                 nli_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
                 nli_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
-                btn = gr.Button("Run")
+                nli_btn = gr.Button("Run")
                 gr.Examples(
                     [
-                        "placeholder text",
-                        "placeholder text",
-                        "placeholder text",
+                        "I am with my friends",
+                        "People like apples",
+                        "Dogs like bones",
                     ],
                     nli_p1,
                     label="Try asking",
                 ) 
                 gr.Examples(
                     [
-                        "placeholder text",
-                        "placeholder text",
-                        "placeholder text",
+                        "I am happy",
+                        "Apples are good",
+                        "Bones like dogs",
                     ],
                     nli_p2,
                     label="Try asking",
@@ -137,25 +176,29 @@ with gr.Blocks(
 
             with gr.Column():
                 with gr.Row(variant="panel"):
-                    out =  gr.Textbox(label= " DistilBERT no LoRA")
+                    NLIOut =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out1 = gr.Textbox(label= " DistilBERT with LoRA")
+                    NLIOut1 = gr.Textbox(label= "Conventionaly Trained Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
+                    NLIOut2 = gr.Textbox(label= "LoRA Fine Tuned Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
+        
+         nli_btn.click(fn=AlbertUntrained_fn, inputs=[nli_p1,nli_p2], outputs=NLIOut)
+         nli_btn.click(fn=AlbertnoLORA_fn, inputs=[nli_p1,nli_p2], outputs=NLIOut1)
+         nli_btn.click(fn=AlbertwithLORA_fn, inputs=[nli_p1,nli_p2], outputs=NLIOut2)
 
     with gr.Tab("Sematic Text Similarity"):
          with gr.Row():
@@ -172,21 +215,21 @@ with gr.Blocks(
             with gr.Column(scale=0.3,variant="panel"):
                 sts_p1 = gr.Textbox(placeholder="Prompt One",label= "Enter Query")
                 sts_p2 = gr.Textbox(placeholder="Prompt Two",label= "Enter Query")
-                btn = gr.Button("Run")
+                sts_btn = gr.Button("Run")
                 gr.Examples(
                     [
-                        "placeholder text",
-                        "placeholder text",
-                        "placeholder text",
+                        "the ball is green",
+                        "i dont like apples",
+                        "our air is clean becase of trees",
                     ],
                     sts_p1,
                     label="Try asking",
                 )
                 gr.Examples(
                     [
-                        "placeholder text",
-                        "placeholder text",
-                        "placeholder text",
+                        "the green ball",
+                        "apples are great",
+                        "trees produce oxygen",
                     ],
                     sts_p2,
                     label="Try asking",
@@ -194,25 +237,29 @@ with gr.Blocks(
 
             with gr.Column():
                 with gr.Row(variant="panel"):
-                    out =  gr.Textbox(label= " DistilBERT no LoRA")
+                    sts_out =  gr.Textbox(label= "Untrained Base Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out1 = gr.Textbox(label= " DistilBERT with LoRA")
+                    sts_out1 = gr.Textbox(label= "Conventionaly Trained Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
 
                 with gr.Row(variant="panel"):
-                    out2 = gr.Textbox(label= " LoRA Fine Tuned Model")
+                    sts_out2 = gr.Textbox(label= "LoRA Fine Tuned Model")
                     gr.Markdown("""<div>
                                 <span><center><B>Training Information</B><center></span>
                                 <span><br><br><br><br><br></span>
                                 </div>""")
+                    
+         sts_btn.click(fn=DebertaUntrained_fn, inputs=[sts_p1,sts_p2], outputs=sts_out)
+         sts_btn.click(fn=DebertanoLORA_fn, inputs=[sts_p1,sts_p2], outputs=sts_out1)
+         sts_btn.click(fn=DebertawithLORA_fn, inputs=[sts_p1,sts_p2], outputs=sts_out2)
 
     with gr.Tab("More information"):
         gr.Markdown("stuff to add")