Update app.py

app.py

@@ -1,42 +1,32 @@
 # import the main classes
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import requests
 
 
-# Load the model.
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
-model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
-
 # input examples list
 ex_list = [["Women are not as capable as men in leadership roles."],["Women are capable as men in leadership roles."]]
 
-# generate_text method
-def generate_text(prompt):
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-    output = model.generate(
-        input_ids,
-        max_length=512,
-        temperature=0.7,
-        do_sample=True,
-        top_p=0.9,
-        top_k=0,
-        num_return_sequences=1,
-    )
-    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+API_URL_Switch = "https://api-inference.huggingface.co/models/google/flan-t5-base"
+headers_Switch = {"Authorization": "Bearer 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"}
+
+
+def query_Switch(payload):
+    response = requests.post(API_URL_Switch, headers=headers_Switch, json=payload)
+    return response.json()
 
 def classify_gender_equality(input_sentence):
     # Here goes your code to classify gender equality from input_sentence
     # Return the result as a string
 
-
     # sub_text = 'Gender equality is important for the progress of society.'
     sub_text = input_sentence
-    prompt = f"Please classify the this sentence {sub_text} as promoting or not promoting gender equality:"
-    generated_text = generate_text(prompt)
-    # print("This sentence is",generated_text)
-  
-    return "This sentence is " + generated_text + " for gender equality"
+    prompt = f"Please classify the this sentence ( {sub_text} ) as promoting or not promoting gender equality"
+
+    output_temp = query_Switch({
+    "inputs": prompt,
+    })
+   
+    return "This sentence is " + output_temp[0]['generated_text'] + " for gender equality"