Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer
+import re
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+
+config = PeftConfig.from_pretrained("mohamedemam/Arabic-meeting-summarization")
+model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-3b")
+model = PeftModel.from_pretrained(model, "mohamedemam/Arabic-meeting-summarization")
+# Load the tokenizer and model
+model_name ="bigscience/bloomz-3b"
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-3b")
+model.eval()
+import wikipediaapi
+
+
+# Create a Wikipedia API instance
+wiki_wiki = wikipediaapi.Wikipedia('MyProjectName (merlin@example.com)', 'en')
+page_py = wiki_wiki.page('Leo messi')
+example_contexts=page_py.text.split(f"\n")
+for i in range(len(example_contexts)):
+    example_contexts[i]=re.sub(f'\n'," ", example_contexts[i])
+# Recommended words for users to choose from
+
+
+# Function to generate questions and answers with configurable parameters
+def generate_qa(context, temperature, top_p,num_seq,l_p, num_b):
+    input_text = context
+    input_ids = tokenizer(input_text, return_tensors='pt')
+    
+    # Generate with configurable parameters
+    output = model.generate(
+        **input_ids,
+        temperature=temperature,
+        top_p=top_p,
+        num_return_sequences=num_seq,
+   
+        max_length=100,
+        num_beams=num_b,
+        length_penalty=l_p,    
+        do_sample=True,
+        
+    )
+    #
+    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+    formatted_output = "\n\n".join(set(generated_text))
+    return formatted_output
+iface = gr.Interface(
+    fn=generate_qa,
+    inputs=[
+        gr.inputs.Dropdown(example_contexts, label="Choose an Example"),
+        gr.inputs.Slider(minimum=0.0, maximum=5, default=2.1, step=0.01, label="Temperature"),
+        gr.inputs.Slider(minimum=0.0, maximum=1, default=0.5, step=0.01, label="Top-p"),     
+        gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="num of sequance"), 
+        gr.inputs.Slider(minimum=0.01, maximum=5, default=3, step=.01, label="l_p")
+     ,
+        gr.inputs.Slider(minimum=1, maximum=20, default=3, step=1, label="num of beams"),
+
+
+    ],
+    outputs=gr.outputs.Textbox(label="Generated Output"),
+    title="Question Generation and Answering",
+    description="Select an example context, choose a recommended word, adjust temperature and top-p. The model will generate questions and answers.",
+)
+# Launch the interface
+iface.launch()