Create app.py

app.py

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+import gradio as gr
+import os
+
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model_name = "google/gemma-7b"
+#adapter_model_name = "samidh/cope-g2b-2c-hs-skr-s1.5.9-sx-sk-s5.d25"
+#adapter_model_name = "samidh/cope-g2b-2c-hs-skr-s1.5.9-sx-sk-s1.5.l1e4-e10-d25"
+#adapter_model_name = "samidh/cope-g2b-2c-hs-s1.f5.9.l5e5-e10-d25-r8"
+#adapter_model_name = "samidh/cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8"
+#adapter_model_name = "samidh/cope-ap-g2b-2c-hs.s1.5.9-sx.s1.5.9o-vl.s1.5.9-hr.s5-sh.s5.l1e4-e5-d25-r8"
+#adapter_model_name = "samidh/cope-g2b-2c-hs.s1.5pcf.9.l5e5-e10-d25-r8"
+#adapter_model_name = "samidh/cope-g7bq-2c-hs.s1.5fpc.9-sx.s1.5.9o-VL.s1.5.9-HR.s5-SH.s5-l1e4-e5-d25-r8"
+adapter_model_name = "samidh/cope-g7bq-2c-hs.s1.5fpc.9-sx.s1.5.9o-VL.s1.5.9-HR.s5-SH.s5-l5e5-e3-d25-r8"
+
+model = AutoModelForCausalLM.from_pretrained(base_model_name, token=os.environ['HF_TOKEN'])
+model = PeftModel.from_pretrained(model, adapter_model_name, token=os.environ['HF_TOKEN'])
+model.merge_and_unload()
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+#inputs = tokenizer.encode("This movie was really", return_tensors="pt")
+
+#outputs = model.generate(inputs, max_new_tokens=1)
+#print(tokenizer.decode(outputs[0], skip_special_tokens=True)[-1])
+
+#gr.load("models/samidh/cope-gemma2b-hs-2c-skr-s1.5.9.d25", hf_token=os.environ['HF_TOKEN']).launch()
+
+PROMPT = """
+INSTRUCTIONS
+============
+Examine the given POLICY and determine if the given CONTENT meets the criteria for ANY of the LABELS. Answer "1" if yes, and "0" if no.
+
+POLICY
+======
+
+{policy}
+
+
+CONTENT
+=======
+
+{content}
+
+
+ANSWER
+======
+
+"""
+
+DEFAULT_POLICY = """# Policy
+## Overview
+This policy guides the classification of content related to expressions that may be sensitive or controversial, aiming to foster a community where open dialogue is encouraged while maintaining respect for all individuals.
+## Definition of Terms
+- Protected Classes: Individuals or groups of individuals defined by sharing a race, ethnicity, religion, disability, sex, gender identity, or sexual orientation. The scope is narrowed to focus on characteristics commonly recognized in anti-discrimination laws. Religions themselves, as concepts ARE NOT protected - only their members are.
+- Non-Protected Classes: Groups defined by other characteristics, such as age, socioeconomic status, and education, which are typically excluded partially or entirely from anti-discrimination laws.
+## Interpretation of Language
+## Definition of Labels
+### (HS): Hate Speech
+#### Includes
+- Explicit Violence Advocacy: Direct and clear threats of or praise for violence or harm against individuals or groups based on their membership in a particular protected class. Stating an intention to defend against aggression DOES NOT qualify
+- Intentional Dehumanization: Statements that clearly depict individuals or groups as inherently ""other"", alien, animalistic, unintelligent, immoral, unclean, or less-than-fully-human based on their membership in a particular protected class in a way that justifies harm or discrimination.
+- Targeted Use of Derogatory Slurs: Targeting another person or group of people using a one-word name for a particular protected class that has an inherent negative connotation (e.g. Nigger, Kike, Cunt, Retard). Multi-word terms are never slurs.
+- Explicit Discrimination Advocacy: Direct and clear calls for exclusion, segregation, or discrimination against individuals or groups based on their membership in a particular protected class, with a clear intent to promote inequality.
+- Direct Hateful Insults: Content that directly addresses another person or group of people the second person (e.g. ""You over there"") and insults them based on their membership in a particular protected class
+#### Excludes
+- Artistic and Educational Content: Expressions intended for artistic, educational, or documentary purposes that discuss sensitive topics but do not advocate for violence or discrimination against individuals or groups based on their membership in a particular protected class.
+- Political and Social Commentary: Commentary on political issues, social issues, and political ideologies that does not directly incite violence or discrimination against individuals or groups based on their membership in a particular protected class.
+- Rebutting Hateful Language: Content that rebuts, condemns, questions, criticizes, or mocks a different person's hateful language or ideas OR that insults the person advocating those hateful
+- Quoting Hateful Language: Content in which the author quotes someone else's hateful language or ideas while discussing, explaining, or neutrally factually presenting those ideas.
+- Describing Sectarian Violence: Content that describes, but does not endorse or praise, violent physical injury against a specifically named race, ethnicity, nationality, sexual orientation, or religious community by another specifically named race, ethnicity, nationality, sexual orientation, or religious community
+"""
+
+DEFAULT_CONTENT = "LLMs steal our jobs."
+
+# Function to make predictions
+def predict(content, policy):
+    input_text = PROMPT.format(policy=policy, content=content)
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_new_tokens=1)
+    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if int(decoded_output[-1]) == 0:
+        return f'NON-Violating ({decoded_output[-1]})'
+    else:
+        return f'VIOLATING ({decoded_output[-1]})'
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[gr.Textbox(label="Content", lines=2, value=DEFAULT_CONTENT), 
+            gr.Textbox(label="Policy", lines=10, value=DEFAULT_POLICY)], 
+    outputs="label",
+    title="CoPE Alpha Preview",
+    description="See if the given content violates your given policy."
+)
+
+# Launch the app
+iface.launch()