acecalisto3 commited on
Commit
0dc1093
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1 Parent(s): 2bc4405

Update app.py

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Files changed (1) hide show
  1. app.py +22 -1
app.py CHANGED
@@ -2,16 +2,32 @@ import gradio as gr
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  from huggingface_hub import InferenceClient
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  import os
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  import requests
 
 
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  # Hugging Face Inference Client
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  client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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  # Function to analyze issues and provide solutions
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  def analyze_issues(issue_text, model_name):
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  nlp = pipeline("text-generation", model=model_name)
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  result = nlp(issue_text)
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  return result[0]['generated_text']
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  # Function to handle chat responses
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  def respond(
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  message,
@@ -71,7 +87,12 @@ def respond(
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  issue = issues[issue_number]
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  issue_text = issue['title'] + "\n\n" + issue['body']
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  resolution = analyze_issues(issue_text, "gpt2") # Default to gpt2 for now
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- yield f"Resolution for Issue '{issue['title']}':\n{resolution}"
 
 
 
 
 
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  else:
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  yield f"Error fetching GitHub issues: {response.status_code}"
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  except Exception as e:
 
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  from huggingface_hub import InferenceClient
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  import os
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  import requests
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+ from transformers import pipeline
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+ from sentence_transformers import SentenceTransformer, util
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  # Hugging Face Inference Client
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  client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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+ # Load a pre-trained model for sentence similarity
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+ similarity_model = SentenceTransformer('all-mpnet-base-v2')
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+
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  # Function to analyze issues and provide solutions
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  def analyze_issues(issue_text, model_name):
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  nlp = pipeline("text-generation", model=model_name)
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  result = nlp(issue_text)
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  return result[0]['generated_text']
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+ # Function to find related issues
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+ def find_related_issues(issue_text, issues):
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+ issue_embedding = similarity_model.encode(issue_text)
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+ related_issues = []
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+ for issue in issues:
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+ title_embedding = similarity_model.encode(issue['title'])
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+ similarity = util.cos_sim(issue_embedding, title_embedding)[0][0]
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+ related_issues.append((issue, similarity))
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+ related_issues = sorted(related_issues, key=lambda x: x[1], reverse=True)
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+ return related_issues[:3] # Return top 3 most similar issues
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+
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  # Function to handle chat responses
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  def respond(
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  message,
 
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  issue = issues[issue_number]
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  issue_text = issue['title'] + "\n\n" + issue['body']
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  resolution = analyze_issues(issue_text, "gpt2") # Default to gpt2 for now
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+
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+ # Find and display related issues
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+ related_issues = find_related_issues(issue_text, issues)
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+ related_issue_text = "\n".join([f"- {issue['title']} (Similarity: {similarity:.2f})" for issue, similarity in related_issues])
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+
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+ yield f"Resolution for Issue '{issue['title']}':\n{resolution}\n\nRelated Issues:\n{related_issue_text}"
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  else:
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  yield f"Error fetching GitHub issues: {response.status_code}"
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  except Exception as e: