Create app.py

app.py

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+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+from textwrap import fill
+
+# Load fine-tuned model and tokenizer
+last_checkpoint = "Jyotiyadav/FLANT-5_Model_Forecasting"
+finetuned_model = T5ForConditionalGeneration.from_pretrained(last_checkpoint)
+tokenizer = T5Tokenizer.from_pretrained(last_checkpoint)
+
+# Define inference function
+def answer_question(question):
+    # Format input
+    inputs = ["Please answer this question: " + question]
+    inputs = tokenizer(inputs, return_tensors="pt")
+    
+    # Generate answer
+    outputs = finetuned_model.generate(**inputs)
+    answer = tokenizer.decode(outputs[0])
+    
+    # Wrap answer for better display
+    return fill(answer, width=80)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=answer_question,
+    inputs="text",
+    outputs="text",
+    title="Question Answering with T5 Model",
+    description="Enter your question to get the answer.",
+    examples=[
+        ["On 2013-02-11, at store number 1 in Quito, Pichincha, under store type D and cluster 13, with 396 transactions recorded, and crude oil price at 97.01, what was the sales quantity of BABY CARE products (ID: 73063), considering whether they were on promotion (On Promotion: 0) in Ecuador during Carnaval (Transferred: False)?"]
+    ]
+)
+
+# Launch Gradio interface
+iface.launch()