Update app.py

app.py

@@ -3,6 +3,16 @@ import gradio as gr
 import requests
 import os  
 
+hf_token = os.getenv("HuggingFace_Token")
+vapi_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
+headers = {"Authorization": f"Bearer {hf_token}"}
+
+
+# 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}"}
@@ -15,52 +25,15 @@ def check_hallucination(assertion, citation):
     return f"**hallucination score:** {output}"
 
 
-# Define the API parameters
-vapi_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
-
-headers = {"Authorization": f"Bearer {hf_token}"}
-
-
-# Function to query the API
-def query(payload):
-    response = requests.post(vapi_url, headers=headers, json=payload)
-    return response.json()
-
-
-# Function to evaluate hallucination
-def evaluate_hallucination(input1, input2):
-    # Combine the inputs
-    combined_input = f"{input1}. {input2}"
-    
-    # Make the API call
-    output = query({"inputs": combined_input})
-    
-    # Extract the score from the output
-    score = output[0][0]['score']
-    
-    # Generate a label based on the score
-    if score < 0.5:
-        label = f"🔴 High risk. Score: {score:.2f}"
-    else:
-        label = f"🟢 Low risk. Score: {score:.2f}"
-    
-    return label
-
 def query_vectara(text):
     user_message = text
-
-    # Read authentication parameters from the .env file
     customer_id = os.getenv('CUSTOMER_ID')
     corpus_id = os.getenv('CORPUS_ID')
     api_key = os.getenv('API_KEY')
-
-    # Define the headers
     api_key_header = {
         "customer-id": customer_id,
         "x-api-key": api_key
     }
-
-    # Define the request body in the structure provided in the example
     request_body = {
         "query": [
             {
@@ -104,11 +77,9 @@ def query_vectara(text):
             }
         ]
     }
-
-    # Make the API request using Gradio
     response = requests.post(
         "https://api.vectara.io/v1/query",
-        json=request_body,  # Use json to automatically serialize the request body
+        json=request_body,  
         verify=True,
         headers=api_key_header
     )
@@ -156,9 +127,8 @@ iface = gr.Interface(
     inputs=[gr.Textbox(label="Assertion"), gr.Textbox(label="Citation")],
     outputs=[gr.Label(), gr.Textbox(label="Explanation")],
     live=False,
-    title="👋🏻Welcome to 🌟Tonic's 🧠🌈Hallucination Tester 🔴🟢",
-    description="How To Use 🌈Hallucination tester: 🗣️📝add any assertion from an LLM or a human 🗣️😷 add any citation from a RAG retriever or a source 👇🏻📩 Press send 🔴red means a 🌈hallucination, 🟢 green means a 🧠credible assertion. Check out the model [vectara/hallucination_evaluation_model](https://huggingface.co/vectara/hallucination_evaluation_model) You can also use 🥒🍆🫑Vectara - Hallucination Tester 🗣️😷 via API below or way by cloning this space. 🧬🔬🔍 Simply click here: 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) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗",
-    theme='ParityError/Anime',
+    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)",
 )
 
 # Launch the interface