added integration for the hallucination model

app.py

@@ -15,7 +15,18 @@ import time
 dotenv.load_dotenv()
 
 seamless_client = Client("facebook/seamless_m4t")
+HuggingFace_Token = os.getenv("HuggingFace_Token")
 
+def check_hallucination(assertion,citation):
+    API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
+    headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
+    payload = {"inputs" : f"{assertion} [SEP] {citation}"}
+
+    response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
+    output = response.json()
+    output = output[0][0]["score"]
+
+    return f"**hullicination score:** {output}"
 
 
 
@@ -425,9 +436,16 @@ with gr.Blocks(theme='ParityError/Anime') as iface :
         audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
         audio_output = gr.Markdown(label="output text")
         audio_button = gr.Button("process audio")
+    with gr.Tab("hallucination check"):
+        assertion = gr.Textbox(label="assertion")
+        citation =  gr.Textbox(label="citation text")
+        hullucination_output = gr.Markdown(label="output text")
+        audio_button = gr.Button("check hallucination")
     text_button.click(process_and_query, inputs=text_input, outputs=text_output)
     image_button.click(process_image, inputs=image_input, outputs=image_output)
     audio_button.click(process_speech, inputs=[audio_input,input_language], outputs=audio_output)
+    audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
+
 
 
 iface.queue().launch(show_error=True,debug=True)