add palm methods

app.py

@@ -6,6 +6,7 @@ Module for detecting fallacies in text.
 import os
 import gradio as gr
 from utils.core import HamburgerStyle
+from context.palm import rebuttal_generator
 
 rebuttal = HamburgerStyle()
 
@@ -29,7 +30,7 @@ gpt4 = gr.Interface(
     description="Single, comprehensive prompt which assigns GPT-4 the role of a climate change analyst as an expert persona to debunk misinformation",
 )
 palm = gr.Interface(
-    fn=textgen,
+    fn=rebuttal_generator,
     inputs=gr.Textbox(
         label="input myth", lines=4, placeholder="climate change misinformation"
     ),

context/palm.py

@@ -0,0 +1,149 @@
+"""
+Module for detecting fallacies in text.
+
+Functions:
+- rebuttal_generator: Detects fallacies in a text input by utilizing models for fallacy 
+detection and semantic textual similarity and generates a rebuttal for the fallacious claim.
+- query: Sends a query to a specified API endpoint with the provided payload and returns
+ the response.
+- demo: Launches a Gradio interface for interactively detecting fallacies in text.
+
+Dependencies:
+- os: Provides a portable way of using operating system dependent functionality.
+- json: Provides functions for encoding and decoding JSON data.
+- requests: Allows sending HTTP requests easily.
+- gradio: Facilitates the creation of customizable UI components for machine learning models.
+- langchain_google_genai: Wrapper for Google Generative AI language models.
+- auxiliar: Contains auxiliary data used in the fallacy detection process.
+
+Environment Variables:
+- HF_API_KEY: API key for accessing Hugging Face model APIs.
+- GOOGLE_API_KEY: API key for accessing Google APIs.
+
+Constants:
+- FLICC_MODEL: API endpoint for the FLICC model used for fallacy detection.
+- CARDS_MODEL: API endpoint for the CARDS model used for fallacy detection.
+- SEMANTIC_TEXTUAL_SIMILARITY: API endpoint for the model used for semantic textual similarity.
+
+Global Variables:
+- hf_api_key: API key for accessing Hugging Face model APIs.
+- google_key: API key for accessing Google APIs.
+- safety_settings: Settings for safety measures in the Google Generative AI model.
+- llm: Instance of the GoogleGenerativeAI class for text generation.
+- similarity_template: Template for generating prompts for similarity comparison.
+- FALLACY_CLAIMS: Dictionary containing fallacy labels and corresponding claims.
+- DEBUNKINGS: Dictionary containing debunkings for fallacy claims.
+- DEFINITIONS: Dictionary containing definitions for fallacy labels.
+"""
+
+import os
+import json
+import requests
+from langchain_google_genai import GoogleGenerativeAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from auxiliar import (
+    FALLACY_CLAIMS,
+    DEBUNKINGS,
+    DEFINITIONS,
+    SIMILARITY_TEMPLATE,
+)
+
+hf_api_key = os.environ["HF_API_KEY"]
+google_key = os.environ["GOOGLE_API_KEY"]
+
+llm = GoogleGenerativeAI(
+    model="models/text-bison-001",
+    google_api_key=google_key,
+    temperature=0,
+    # safety_settings=safety_settings,
+)
+
+similarity_template = PromptTemplate.from_template(SIMILARITY_TEMPLATE)
+
+
+def query(payload, api_url, api_token=hf_api_key):
+    """
+    Sends a query to the specified API endpoint with the provided payload.
+
+    Args:
+        payload (dict): The payload to be sent to the API.
+        api_url (str): The URL of the API endpoint.
+        api_token (str, optional): The API token used for authentication. Defaults to hf_api_key.
+
+    Returns:
+        dict: The JSON response from the API.
+
+    Raises:
+        ValueError: If the response content cannot be decoded as UTF-8.
+
+    Example:
+        >>> query({"text": "example text"}, "https://api.example.com")
+        {'status': 'success', 'result': 'example result'}
+    """
+    headers = {"Authorization": f"Bearer {api_token}"}
+    options = {"use_cache": False, "wait_for_model": True}
+    payload = {"inputs": payload, "options": options}
+    response = requests.post(api_url, headers=headers, json=payload)
+    return json.loads(response.content.decode("utf-8"))
+
+
+FLICC_MODEL = "https://api-inference.huggingface.co/models/fzanartu/flicc"
+CARDS_MODEL = (
+    "https://api-inference.huggingface.co/models/crarojasca/BinaryAugmentedCARDS"
+)
+SEMANTIC_TEXTUAL_SIMILARITY = (
+    "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
+)
+
+
+def rebuttal_generator(text):
+    """
+    Generates a rebuttal for a text containing a detected fallacy.
+
+    This function detects fallacies in the input text and generates a rebuttal
+    for the fallacious claim.
+
+    Args:
+        text (str): The input text containing a potentially fallacious claim.
+
+    Returns:
+        str: A rebuttal for the fallacious claim in the input text.
+
+    Raises:
+        ValueError: If no similar sentence is found.
+
+    Example:
+        >>> rebuttal_generator("This is a text containing a fallacy.")
+        'A rebuttal to the fallacy of [fallacy label]: [rebuttal]'
+    """
+
+    response = query(text, api_url=CARDS_MODEL)
+    if response[0][0].get("label") == "Contrarian":
+        response = query(text, api_url=FLICC_MODEL)
+        label = response[0][0].get("label")
+        claims = FALLACY_CLAIMS.get(label, None)
+
+        if claims:
+            data = query(
+                {"source_sentence": text, "sentences": claims},
+                api_url=SEMANTIC_TEXTUAL_SIMILARITY,
+            )
+            max_similarity = data.index(max(data))
+            chain = LLMChain(llm=llm, prompt=similarity_template, verbose=True)
+            result = chain.run(
+                {
+                    "claim": claims[max_similarity],
+                    "fallacy": label,
+                    "definition": DEFINITIONS.get(label),
+                    "example": DEBUNKINGS.get(claims[max_similarity]),
+                    "text": text,
+                }
+            )
+
+        else:
+            raise ValueError("No similar sentence found")
+    else:
+        result = "No fallacy has been detected in your text."
+
+    return result