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Update app.py
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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,
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@@ -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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-
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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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# 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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# 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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# 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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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:
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