Update app.py

app.py

@@ -1,33 +1,33 @@
 import gradio as gr
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# Load pre-trained models
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
-flashcard_generator = pipeline("text-generation", model="gpt-2")
+# Initialize the Inference Client
+client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
 
 # Function for text summarization
 def summarize(text):
     try:
-        summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-        return summary[0]['summary_text']
+        prompt = f"Summarize the following text:\n\n{text}\n\nSummary:"
+        response = client.text_generation(prompt, max_new_tokens=100)
+        return response
     except Exception as e:
         return f"Error in summarization: {str(e)}"
 
 # Function for generating flashcards
 def generate_flashcards(text):
     try:
-        prompt = f"Generate flashcards for the following text: {text}"
-        flashcards = flashcard_generator(prompt, max_length=200, num_return_sequences=1)
-        return flashcards[0]['generated_text']
+        prompt = f"Generate flashcards for the following text:\n\n{text}\n\nFlashcards:"
+        response = client.text_generation(prompt, max_new_tokens=200)
+        return response
     except Exception as e:
         return f"Error in flashcard generation: {str(e)}"
 
 # Function for question answering
 def answer_question(text, question):
     try:
-        result = qa_pipeline(question=question, context=text)
-        return result['answer']
+        prompt = f"Answer the following question based on the text:\n\nText: {text}\n\nQuestion: {question}\n\nAnswer:"
+        response = client.text_generation(prompt, max_new_tokens=100)
+        return response
     except Exception as e:
         return f"Error in question answering: {str(e)}"