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app (1).py

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+import time
+import gradio as gr
+
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from peft import PeftModel, LoraConfig
+
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")
+tokenizer.pad_token = tokenizer.eos_token
+base_model = GPT2LMHeadModel.from_pretrained("gpt2-medium")
+
+lora_config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    target_modules=["c_fc", "c_proj", "c_attn"],
+    lora_dropout=0.1,
+    task_type="CAUSAL_LM"
+)
+    
+finetuned_model = PeftModel.from_pretrained(base_model, "./lora_ft_weights", config=lora_config)
+finetuned_model.eval()
+
+
+
+# -------------------------------
+# Simulated QA Models
+# -------------------------------
+def qa_system(method, question):
+    start_time = time.time()
+
+    if not question.strip():
+        return "**Error:** Please enter a question.", 0.0, "0 seconds", ""
+
+    # Simulated response based on method
+    if method == "Retrieval-Augmented Generation (RAG)":
+        answer = "Using RAG: Based on retrieved financial documents, the answer is $95,000,000."
+        model_name = "RAG-based Model"
+        confidence = 0.92
+    else:
+        model_name = "GPT 2-finetuned"
+        # Input guradrails
+        financial_keywords = [
+            'revenue', 'profit', 'earnings', 'financial', 'income', 'balance', 
+            'cash', 'debt', 'equity', 'assets', 'market', 'investment', 'sales',
+            'cost', 'margin', 'growth', 'compliance', 'risk', 'customer'
+        ]
+        for text in question:            
+        # Check for financial content
+            text_lower = text.lower()
+            if any(pattern in text_lower for pattern in financial_keywords):
+                return "This question does not seem to be related to Finance"
+
+        prompt = f"You are a financial assistant.\nUse the context below to answer the question.\n\nQuestion: {question}\nAnswer:"
+        
+        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    
+        with torch.no_grad():
+            outputs = finetuned_model.generate(
+                **inputs,
+                max_length=inputs['input_ids'].shape[1] + 100,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+        
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        answer = generated_text.split("Answer:")[-1].strip()
+        
+    end_time = time.time()
+    response_time = round(end_time - start_time, 2)
+
+    return (
+        f"**Method:** {model_name}",
+        0.95,
+        f"{response_time} seconds",
+        answer
+    )
+
+# -------------------------------
+# Gradio UI
+# -------------------------------
+with gr.Blocks(css="""
+    .radio-vertical .wrap {
+        flex-direction: column !important;
+    }
+    .radio-vertical .wrap > label {
+        margin-bottom: 8px !important;
+        margin-right: 0 !important;
+    }
+    .small-btn {
+        max-width: fit-content !important;
+        width: auto !important;
+    }
+    .small-btn button {
+        width: auto !important; 
+        min-width: unset !important; 
+        padding: 8px 16px !important; 
+        font-size: 16px !important;
+        white-space: nowrap !important;
+        max-width: fit-content !important;
+    }
+""") as demo:
+    gr.Markdown(
+        """
+        # 📊 Comparative Financial QA System  
+        An implementation comparing **Retrieval-Augmented Generation (RAG)** and a **Fine-Tuned on LoRA and Replay-Based Learning** GPT 2 model for answering questions on financial reports.
+        """
+    )
+
+    # Radio buttons displayed vertically
+    method = gr.Radio(
+        choices=["Retrieval-Augmented Generation (RAG)", "Fine-Tuned Model"],
+        label="Choose QA Method:",
+        value="Fine-Tuned Model",
+        interactive=True,
+        elem_classes="radio-vertical"
+    )
+
+    # Question input
+    question = gr.Textbox(
+        label="Ask a question about Nice's 2023-2024 financials:",
+        placeholder="e.g., What was the total revenue in 2023?"
+    )
+
+    # Get Answer button — auto-sized
+    submit_btn = gr.Button("Get Answer", elem_classes="small-btn")
+
+    # Output section - initially hidden
+    with gr.Group(visible=False) as output_section:
+        method_output = gr.Markdown()
+        confidence_output = gr.Number(label="Model Confidence")
+        response_time_output = gr.Textbox(label="Response Time")
+        answer_output = gr.Markdown(label="Answer")
+
+    # Button click handler
+    def handle_submit(method_val, question_val):
+        # Show output section and get results
+        results = qa_system(method_val, question_val)
+        return [gr.Group(visible=True)] + list(results)
+    
+    submit_btn.click(
+        handle_submit,
+        inputs=[method, question],
+        outputs=[output_section, method_output, confidence_output, response_time_output, answer_output]
+    )
+
+# -------------------------------
+# Launch for Hugging Face Spaces
+# -------------------------------
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers>=4.21.0
+peft>=0.4.0
+gradio>=3.0.0