Update app.py

app.py

@@ -5,6 +5,18 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 import numpy as np
 import pandas as pd
+from supabase import create_client, Client
+from google import genai
+import os
+
+
+apikey = os.getenv('gemini_secret')
+client = genai.Client(api_key=apikey)
+
+supabase_url = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
+supabase_anon_key = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
+
+supabase: Client = create_client(supabase_url, supabase_anon_key)
 
 # Load BERT model & tokenizer
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
@@ -29,7 +41,67 @@ category_mappings = {
     "kmeans_labels": pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int32')
 }
 
-def infer(component,title,description):
+
+def combine_document_features(component, description):
+    return f"Component is {component} with Description {description}"
+
+
+def retrieve_similar_documents(component, description,pid):
+    new_doc_text = combine_document_features(component, description)
+    inputs = tokenizer(new_doc_text, return_tensors="pt", truncation=True,max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        new_embedding = outputs.hidden_states[-1][:, 0, :][0].tolist()
+
+    # Get all documents from the "bugs" table
+    response = supabase.rpc("get_similar_bugs", {
+    "query_embedding": new_embedding,
+    "pid": pid
+}).execute()
+
+    if not response.data:
+        return None, new_doc_text
+    outputs=[]
+    for i in response.data:
+        outputs.append(i)
+    return outputs,new_doc_text
+
+def generate_title(soft_prompt, new_document):
+    soft_prompt_examples = '\n\n'.join(
+        f'Component: {value[0]}\n Description: {value[1]}\n title: {key}' for key, value in soft_prompt.items()
+    )
+
+    input_prompt = f'''Generate a Bug report title based on description and Component. Do not generate anything else. Try to keep it in a limit of 30 characters.
+    Look at the below examples to understand the task.
+    {soft_prompt_examples}
+    Now using the examples above, try giving the output for the following input enclosed within the | delimiter.
+    | {new_document} |.
+    Give the title as instructed above. Give the title only'''
+    
+    response = client.models.generate_content(model="gemini-2.0-flash", contents=input_prompt)
+    return response.text
+
+def suggest_title(component, description,pid):
+    similar_docs, new_doc_text = retrieve_similar_documents(component,description,pid)
+    
+    examples={'''Concatenation of string variables slow compared to strings themselves''':['''JavaScript Engine''','''Ill be uploading a test case with various tests of string concatenation.  ; Mozilla (build 2000040308) shows good performance with all the ones that uses ; strings directly; e.g. string1 + string2.  its the last three it has ; problems with; they use string variables (e.g. var1 + var 2) in the ; concatenation.; ; try it out for yourselves.  all numberical values shown in the form fields is ; the execution time in millseconds.  the four tests on the left hand side; and ; the 2 at the top on the right hand side finished in around 1650ms on my P3/450. ;  this is just the same speed as Netscape Comm 4.72.  On the last three tests on ; the right hand side NC4.72 uses 7000ms; 10000ms and around 4500ms respectively; ; while Mozilla suddenly uses 10000ms; 14750ms and 5500ms on the same three tests. ;  Im slightly surprised by this sudden large increase in execution time.; ; the test results are very positive compared to IE5.01 though; except for the ; three tests with variables in them.  the 4 tests on the left hand side; from top ; to bottom; finish in around 5.5s; 9s; 12.5s and 16s in IE5.01.  in other words; ; a nearly linear increase in usage for each string that gets added.  the two top ; tests on the right hand side finish in around 9.3s and 20s; a _huge_ difference ; from both Mozilla and Communicator.  the last three tests; with variables; ; execute at just about the same speed as Communicator though (the last one ; actually about a second faster).'''],'''Linux/Slackware: undefined iostream symbols; app wont start''':['''HTML: Parser''','''johnny:~/mozilla/package# ./mozilla-apprunner.sh; MOZILLA_FIVE_HOME=/root/mozilla/package;   LD_LIBRARY_PATH=/root/mozilla/package:/usr/local/rvplayer5.0;       MOZ_PROGRAM=./apprunner;         moz_debug=0;      moz_debugger=; ./apprunner: error in loading shared libraries; /root/mozilla/package/libraptorhtmlpars.so: undefined symbol:; __vt_8iostream.3ios; ; I am running Slackware 4.0 and never have had any luck running any; of these milestone releases.  This was the M7 attempt.; Just thought you should know.; Thanks; Johnny O''']}
+    
+    soft_prompt = {i["title"]: [i["component"],i["description"]] for i in similar_docs if i["similarity"] > 0} if similar_docs else None
+    if soft_prompt is None:
+        soft_prompt = {}
+    
+    for k, v in examples.items():
+        if len(soft_prompt) >= 3:
+            break
+        if k not in soft_prompt:
+            soft_prompt[k] = v
+    
+    generated_title = generate_title(soft_prompt, new_doc_text)
+    return f"{generated_title}"
+
+def infer(component,title,description,pid=None,mode="priority"):
+    if mode=="title":
+        return suggest_title(component, description,pid)
     # BERT embedding
     combined_text = f"{component} [SEP] {title} [SEP] {description}"
     inputs = tokenizer(combined_text, return_tensors="pt", truncation=True,max_length=512)
@@ -57,8 +129,10 @@ iface = gr.Interface(
     inputs=[
         gr.Textbox(label="Component"),
         gr.Textbox(label="Title"),
-        gr.Textbox(label="Description")
+        gr.Textbox(label="Description"),
+        gr.Textbox(label="PID (optional)", value="default_pid"),  # Or Hidden
+        gr.Radio(["priority", "title"], label="Mode", value="priority")
     ],
     outputs="text"
 )
-iface.launch()
+iface.launch()