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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load your fine-tuned model from Hugging Face Hub
+model_name = "Deepesh-001/RagFin-Ai"  # Replace with actual name
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate response from user query + context
+def generate_answer(query, context):
+    input_text = f"Context: {context}\n\nQuestion: {query}\nAnswer:"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=300, do_sample=True, top_k=50)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=[
+        gr.Textbox(label="User Query", placeholder="How can I save tax on ₹15 lakhs income?"),
+        gr.Textbox(label="Context", placeholder="Provide some financial context or let it be blank...")
+    ],
+    outputs="text",
+    title="Financial LLM - Indian Tax Advisor",
+    description="Ask anything about Indian tax planning, deductions, or financial strategies."
+)
+
+# Run the app
+if __name__ == "__main__":
+    iface.launch()

requirement.txt

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+transformers
+torch
+gradio