Linting with Black

chill.py

@@ -29,17 +29,24 @@ suggestions = []
 
 start_time = time.time()
 
+
 class ImprovedText(BaseModel):
     text: str = Field(str, description="The improved text.")
 
+
 class SpicyScore(BaseModel):
     spicy_score: float = Field(float, description="The spiciness score of the text.")
 
+
 class Critique(BaseModel):
     critique: str = Field(str, description="The critique of the text.")
 
+
 class FaithfulnessScore(BaseModel):
-    faithfulness_score: float = Field(float, description="The faithfulness score of the text.")
+    faithfulness_score: float = Field(
+        float, description="The faithfulness score of the text."
+    )
+
 
 improve_prompt = """
 Your task is to rephrase inflammatory text, so it is more calm and constructive, without changing the intended meaning.
@@ -135,7 +142,6 @@ Please score the text.
 """
 
 
-
 def improve_text():
     global suggestions
     replacements = {
@@ -143,7 +149,7 @@ def improve_text():
         "previous_suggestions": json.dumps(suggestions, indent=2),
     }
     resp_json = query_ai_prompt(improve_prompt, replacements, ImprovedText)
-    #print('resp_json', resp_json)
+    # print('resp_json', resp_json)
     return resp_json["text"]
 
 
@@ -152,15 +158,11 @@ def critique_text(last_edit):
 
     # Query the AI for each of the new prompts separately
 
-    critique_resp = query_ai_prompt(
-        critique_prompt, replacements, Critique 
-    )
+    critique_resp = query_ai_prompt(critique_prompt, replacements, Critique)
     faithfulness_resp = query_ai_prompt(
         faith_scorer_prompt, replacements, FaithfulnessScore
     )
-    spiciness_resp = query_ai_prompt(
-        spicy_scorer_prompt, replacements, SpicyScore
-    )
+    spiciness_resp = query_ai_prompt(spicy_scorer_prompt, replacements, SpicyScore)
 
     # Combine the results from the three queries into a single dictionary
     combined_resp = {

utils.py

@@ -2,17 +2,22 @@ import json
 from typing import Any, Dict, Union
 import requests
 
-from llama_cpp import json_schema_to_gbnf  # Only used directly to convert the JSON schema to GBNF,
+from llama_cpp import (
+    json_schema_to_gbnf,
+)  # Only used directly to convert the JSON schema to GBNF,
+
 # The main interface is the HTTP server, not the library directly.
 
 
-def llm_streaming(prompt:str, pydantic_model_class, return_pydantic_object=False) -> Union[str, Dict[str, Any]]:
+def llm_streaming(
+    prompt: str, pydantic_model_class, return_pydantic_object=False
+) -> Union[str, Dict[str, Any]]:
     schema = pydantic_model_class.model_json_schema()
-    
+
     # Optional example field from schema, is not needed for the grammar generation
     if "example" in schema:
         del schema["example"]
-    
+
     json_schema = json.dumps(schema)
     grammar = json_schema_to_gbnf(json_schema)
 
@@ -21,19 +26,18 @@ def llm_streaming(prompt:str, pydantic_model_class, return_pydantic_object=False
         "max_tokens": 1000,
         "grammar": grammar,
         "temperature": 1.0,
-        "messages": [
-            {
-                "role": "user",
-                "content": prompt
-            }
-        ],
+        "messages": [{"role": "user", "content": prompt}],
     }
     headers = {
         "Content-Type": "application/json",
     }
 
-    response = requests.post("http://localhost:5834/v1/chat/completions"
-                             , headers=headers, json=payload, stream=True)
+    response = requests.post(
+        "http://localhost:5834/v1/chat/completions",
+        headers=headers,
+        json=payload,
+        stream=True,
+    )
     output_text = ""
     for chunk in response.iter_lines():
         if chunk:
@@ -43,10 +47,10 @@ def llm_streaming(prompt:str, pydantic_model_class, return_pydantic_object=False
                 if chunk.strip() == "[DONE]":
                     break
                 chunk = json.loads(chunk)
-                new_token = chunk.get('choices')[0].get('delta').get('content')
+                new_token = chunk.get("choices")[0].get("delta").get("content")
                 if new_token:
                     output_text = output_text + new_token
-                    print(new_token,sep='',end='',flush=True)
+                    print(new_token, sep="", end="", flush=True)
 
     if return_pydantic_object:
         model_object = pydantic_model_class.model_validate_json(output_text)
@@ -55,14 +59,15 @@ def llm_streaming(prompt:str, pydantic_model_class, return_pydantic_object=False
         json_output = json.loads(output_text)
         return json_output
 
+
 def replace_text(template: str, replacements: dict) -> str:
     for key, value in replacements.items():
         template = template.replace(f"{{{key}}}", value)
     return template
 
+
 def query_ai_prompt(prompt, replacements, model_class):
     prompt = replace_text(prompt, replacements)
-    #print('prompt')
-    #print(prompt)
+    # print('prompt')
+    # print(prompt)
     return llm_streaming(prompt, model_class)
-