Update app.py

app.py

@@ -1,23 +1,49 @@
+# import gradio as gr
+# from transformers import pipeline
+
+# # Load the pre-trained model
+# generator = pipeline("question-answering", model="EleutherAI/gpt-neo-2.7B")
+
+# # Define Gradio interface
+# def generate_response(prompt):
+#     # Generate response based on the prompt
+#     response = generator(prompt, max_length=50, do_sample=True, temperature=0.9)
+#     return response[0]['generated_text']
+
+# # Create Gradio interface
+# iface = gr.Interface(
+#     fn=generate_response,
+#     inputs="text",
+#     outputs="text",
+#     title="OpenAI Text Generation Model",
+#     description="Enter a prompt and get a generated text response.",
+# )
+
+# # Deploy the Gradio interface
+# iface.launch(share=True)
+
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "gpt2"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Load the pre-trained model
-generator = pipeline("question-answering", model="EleutherAI/gpt-neo-2.7B")
 
-# Define Gradio interface
-def generate_response(prompt):
-    # Generate response based on the prompt
-    response = generator(prompt, max_length=50, do_sample=True, temperature=0.9)
-    return response[0]['generated_text']
+def generate_answer(question):
+    inputs = tokenizer.encode("Question: " + question, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1, early_stopping=True)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
 
-# Create Gradio interface
 iface = gr.Interface(
-    fn=generate_response,
+    fn=generate_answer,
     inputs="text",
     outputs="text",
-    title="OpenAI Text Generation Model",
-    description="Enter a prompt and get a generated text response.",
+    title="Open-Domain Question Answering",
+    description="Enter your question to get an answer.",
+    theme="compact"
 )
 
-# Deploy the Gradio interface
-iface.launch(share=True)
+iface.launch(share=True)  # Deploy the interface
+