app.py

import gradio as gr
import requests
import os  
import json
import time
import transformers
import re
from transformers import AutoTokenizer, AutoModelForCausalLM

hf_token = os.getenv("HF_AUTH_TOKEN")
vapi_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
headers = {"Authorization": f"Bearer {hf_token}"}


model_name = "allenai/OLMo-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

def generate_text(prompt, max_new_tokens=100, do_sample=False, top_k=50, top_p=0.95):
    inputs = tokenizer(prompt, return_tensors='pt', return_token_type_ids=False)
    response = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=do_sample, top_k=top_k, top_p=top_p)
    return tokenizer.batch_decode(response, skip_special_tokens=True)[0]


# Function to query the API
def query(payload):
    response = requests.post(vapi_url, headers=headers, json=payload)
    return response.json()

def check_hallucination(assertion, citation):
    api_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
    header = {"Authorization": f"Bearer {hf_token}"}
    payload = {"inputs": f"{assertion} [SEP] {citation}"}

    attempts = 0
    max_attempts = 3
    wait_time = 180  # 3 minutes

    while attempts < max_attempts:
        try:
            response = requests.post(api_url, headers=header, json=payload, timeout=120)
            response.raise_for_status()  # This will raise an exception for HTTP error codes
            output = response.json()
            output = output[0][0]["score"]
            return f"**hallucination score:** {output}"
        except requests.exceptions.HTTPError as http_err:
            print(f"HTTP error occurred: {http_err}")  # Python 3.6
        except requests.exceptions.RequestException as err:
            print(f"Other error occurred: {err}")  # Python 3.6
        except KeyError:
            print("KeyError: The expected key was not found in the response. The endpoint might be waking up.")
        
        attempts += 1
        if attempts < max_attempts:
            print(f"Attempt {attempts} failed. Waiting for {wait_time} seconds before retrying...")
            time.sleep(wait_time)
        else:
            print("Maximum attempts reached. Please try again later.")
            return "Error: Unable to retrieve hallucination score after multiple attempts."

    return "Error: Unable to process the hallucination check."


def query_vectara(text):
    user_message = text
    customer_id = os.getenv('CUSTOMER_ID')
    corpus_id = os.getenv('CORPUS_ID')
    api_key = os.getenv('API_KEY')
    api_key_header = {
        "customer-id": customer_id,
        "x-api-key": api_key
    }
    request_body = {
        "query": [
            {
                "query": user_message,
                "queryContext": "",
                "start": 1,
                "numResults": 25,
                "contextConfig": {
                    "charsBefore": 0,
                    "charsAfter": 0,
                    "sentencesBefore": 2,
                    "sentencesAfter": 2,
                    "startTag": "%START_SNIPPET%",
                    "endTag": "%END_SNIPPET%",
                },
                "rerankingConfig": {
                    "rerankerId": 272725718,
                    "mmrConfig": {
                        "diversityBias": 0.35
                    }
                },
                "corpusKey": [
                    {
                        "customerId": customer_id,
                        "corpusId": corpus_id,
                        "semantics": 0,
                        "metadataFilter": "",
                        "lexicalInterpolationConfig": {
                            "lambda": 0
                        },
                        "dim": []
                    }
                ],
                "summary": [
                    {
                        "maxSummarizedResults": 5,
                        "responseLang": "auto",
                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
                    }
                ]
            }
        ]
    }
    response = requests.post(
        "https://api.vectara.io/v1/query",
        json=request_body,  
        verify=True,
        headers=api_key_header
    )

    if response.status_code == 200:
        query_data = response.json()
        if query_data:
            sources_info = []

            # Extract the summary.
            summary = query_data['responseSet'][0]['summary'][0]['text']

            # Iterate over all response sets
            for response_set in query_data.get('responseSet', []):
                # Extract sources
                # Limit to top 5 sources.
                for source in response_set.get('response', [])[:5]:
                    source_metadata = source.get('metadata', [])
                    source_info = {}

                    for metadata in source_metadata:
                        metadata_name = metadata.get('name', '')
                        metadata_value = metadata.get('value', '')

                        if metadata_name == 'title':
                            source_info['title'] = metadata_value
                        elif metadata_name == 'author':
                            source_info['author'] = metadata_value
                        elif metadata_name == 'pageNumber':
                            source_info['page number'] = metadata_value

                    if source_info:
                        sources_info.append(source_info)

            result = {"summary": summary, "sources": sources_info}
            return f"{json.dumps(result, indent=2)}"
        else:
            return "No data found in the response."
    else:
        return f"Error: {response.status_code}"

def remove_references(text):
    # Regex pattern to find references like [1], [1][2], etc.
    pattern = r'\[\d+\]+'
    # Replace found patterns with an empty string
    cleaned_text = re.sub(pattern, '', text)
    return cleaned_text

def clean_text(text):
    # Remove special characters, keeping only letters, numbers, and spaces
    cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
    return cleaned_text

def evaluate_content(user_input):
    vectara_response = query_vectara(user_input)
    vectara_response_json = json.loads(vectara_response)
    
    summary = vectara_response_json.get("summary", "")
    sources = vectara_response_json.get("sources", [])
    
    # Remove references from the summary text
    summary_no_refs = remove_references(summary)
    
    # Clean summary text to remove special characters
    summary_clean = clean_text(summary_no_refs)
    
    # Process sources to extract and clean necessary information
    sources_info = ""
    for source in sources:
        title = source.get("title", "No title")
        author = source.get("author", "No author")
        page_number = source.get("page number", "N/A")
        
        # Clean source info
        title_clean = clean_text(title)
        author_clean = clean_text(author)
        
        sources_info += f"Title: {title_clean}, Author: {author_clean}, Page: {page_number}\n"
    
    # Generate text based on the cleaned and reference-removed summary
    olmo_output = generate_text(summary_clean)
    olmo_output_clean = clean_text(olmo_output)
    
    # Check hallucination based on the original output and summary
    hallucination_score = check_hallucination(olmo_output, summary)
    
    return summary_clean, sources_info, olmo_output_clean, hallucination_score

# Adjust the Gradio interface outputs to match the new structure
iface = gr.Interface(
    fn=evaluate_content,
    inputs=[gr.Textbox(label="User Input")],
    outputs=[
        gr.Textbox(label="Vectara Summary", lines=10),
        gr.Textbox(label="Vectara Sources", lines=10),
        gr.Textbox(label="Generated Text", lines=10),
        gr.Textbox(label="Hallucination Score")
    ],
    live=False,
    title="👋🏻Welcome to 🌟Team Tonic's 🧠🌈SureRAG🔴🟢",
    description="Nothing is more important than reputation. However you can create automated content pipelines for public facing content. How can businesses grow their reputation while mitigating risks due to AI? How it works : vectara rag retrieval reranking and summarization is used to return content. then an LLM generates content based on these returns. this content is checked for hallucination before being validated for publishing on twitter. SureRAG is fixed on Tonic-AI's README files as a Demo, provide input to generate a response. This response is checked by Vectara's HHME. Check out the model [vectara/hallucination_evaluation_model](https://huggingface.co/vectara/hallucination_evaluation_model) Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [DataTonic](https://github.com/Tonic-AI/DataTonic)",
)
iface.launch()