intial commit

app.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+import re
+
+def cleaning_history_tuple(history):
+    s=sum(history,())
+    s=list(s)
+    s2=""
+    for i in s:
+        i=re.sub("\n", '', i)
+        i=re.sub("<p>", '', i)
+        i=re.sub("</p>", '', i)
+        s2=s2+i+'\n'
+    return s2
+
+def ai_output(string1,string2):
+    a1=len(string1)
+    a2=len(string2)
+    string3=string2[a1:]
+    sub1="A:"
+    sub2="User"
+    #sub3="\n"
+    try:
+        try:
+            idx1=string3.index(sub1)
+            response=string3[:idx1]
+            return response
+            
+        except:
+            idx1=string3.index(sub2)
+            response=string3[:idx1]
+            return response
+    except:
+        return string3
+
+model4 = AutoModelForCausalLM.from_pretrained("bigscience/bloom-3b")
+tokenizer4 = AutoTokenizer.from_pretrained("bigscience/bloom-3b")
+
+def predict(input,initial_prompt, temperature=0.7,top_p=1,top_k=5,max_tokens=64,no_repeat_ngram_size=1,num_beams=6,do_sample=True, history=[]):
+	
+    s = cleaning_history_tuple(history)
+    
+    s = s+ "\n"+ "User: "+ input + "\n" + "Assistant: "
+    s2=initial_prompt+" " + s
+    
+    input_ids = tokenizer4.encode(str(s2), return_tensors="pt")
+    response = model4.generate(input_ids, min_length = 10,
+                         max_new_tokens=int(max_tokens),
+                         top_k=int(top_k),
+                         top_p=float(top_p),
+                         temperature=float(temperature),
+                         no_repeat_ngram_size=int(no_repeat_ngram_size),
+                         num_beams = int(num_beams),
+                         do_sample = bool(do_sample),
+                         )
+    
+  
+    response2 = tokenizer4.decode(response[0])
+    print("Response after decoding tokenizer: ",response2)
+    print("\n\n")
+    response3=ai_output(s2,response2)
+    
+    input="User: "+input
+    response3="Assistant: "+ response3
+    history.append((input, response3))
+
+    return history, history
+
+#gr.Interface(fn=predict,title="BLOOM-3b",
+#             inputs=["text","text","text","text","text","text","text","text","text",'state'],
+#            
+#             outputs=["chatbot",'state']).launch()
+             
+
+gr.Interface(inputs=[gr.Textbox(label="input", lines=1, value=""),
+                     gr.Textbox(label="initial_prompt", lines=1, value=prompt),
+                     gr.Textbox(label="temperature", lines=1, value=0.7),
+                     gr.Textbox(label="top_p", lines=1, value=1),
+                     gr.Textbox(label="top_k", lines=1, value=5),
+                     gr.Textbox(label="max_tokens", lines=1, value=64),
+                     gr.Textbox(label="no_repeat_ngram_size", lines=1, value=1),
+                     gr.Textbox(label="num_beams", lines=1, value=6),
+                     gr.Textbox(label="do_sample", lines=1, value="True"), 'state'],
+             fn=predict, title="OPT-6.7B", outputs=["chatbot",'state']
+
+             #inputs=["text","text","text","text","text","text","text","text","text",'state'],
+
+             ).launch()            
+

requirements.txt

@@ -0,0 +1,3 @@
+torch
+gradio
+transformers