GitHub Actions Bot commited on
Commit
64806b3
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1 Parent(s): 590474d

Deploy backend from GitHub Actions

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Files changed (1) hide show
  1. services/gemini_service.py +4 -4
services/gemini_service.py CHANGED
@@ -44,9 +44,9 @@ class GeminiService:
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  prompt = f"Analyze the following Reddit comment/post for toxic behavior, including harassment, hate speech, abusive language, or threats:\n\n{text}"
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- # Run model call (using gemini-1.5-flash as default)
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  response = client.models.generate_content(
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- model='gemini-1.5-flash',
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  contents=prompt,
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  config=types.GenerateContentConfig(
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  response_mime_type="application/json",
@@ -75,7 +75,7 @@ class GeminiService:
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  prompt = f"Analyze the following conversation thread. Determine if it shows signs of a rapidly escalating flame war or hostile back-and-forth personal arguments:\n\n{thread_text}"
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  response = client.models.generate_content(
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- model='gemini-1.5-flash',
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  contents=prompt,
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  config=types.GenerateContentConfig(
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  response_mime_type="application/json",
@@ -99,7 +99,7 @@ class GeminiService:
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  try:
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  # Generate embedding using the standard embedding model
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  response = client.models.embed_content(
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- model='text-embedding-004',
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  contents=text
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  )
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  # Response contains a list of embeddings (usually 768 dimensions)
 
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  prompt = f"Analyze the following Reddit comment/post for toxic behavior, including harassment, hate speech, abusive language, or threats:\n\n{text}"
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+ # Run model call (using gemini-2.0-flash as default)
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  response = client.models.generate_content(
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+ model='gemini-2.0-flash',
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  contents=prompt,
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  config=types.GenerateContentConfig(
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  response_mime_type="application/json",
 
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  prompt = f"Analyze the following conversation thread. Determine if it shows signs of a rapidly escalating flame war or hostile back-and-forth personal arguments:\n\n{thread_text}"
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  response = client.models.generate_content(
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+ model='gemini-2.0-flash',
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  contents=prompt,
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  config=types.GenerateContentConfig(
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  response_mime_type="application/json",
 
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  try:
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  # Generate embedding using the standard embedding model
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  response = client.models.embed_content(
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+ model='gemini-embedding-2',
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  contents=text
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  )
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  # Response contains a list of embeddings (usually 768 dimensions)