NewWithAPI

app.py

@@ -1,16 +1,32 @@
 import gradio as gr
-from transformers import pipeline
+import requests
+import os
+from dotenv import load_dotenv
 
-# Load the question-answering model
-qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2", tokenizer="deepset/roberta-base-squad2")
+# Load environment variables from .env file
+load_dotenv()
+
+API_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
+headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACE_API_KEY']}"}
 
 # Initialize an empty list to store conversation history
 conversation_history = []
 
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
 def answer_question(question, context):
-    # Use the model to get the answer
-    result = qa_pipeline(question=question, context=context)
-    answer = result['answer']
+    # Use the Hugging Face Inference API to get the answer
+    output = query({
+        "inputs": {
+            "question": question,
+            "context": context
+        },
+    })
+    
+    # Extract the answer from the API response
+    answer = output.get('answer', 'Sorry, I couldn\'t find an answer.')
     
     # Add the Q&A to the conversation history
     conversation_history.append(f"Human: {question}")

requirements.txt

@@ -1 +1,4 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+gradio
+requests
+dotenv