Create app.py

app.py

@@ -0,0 +1,119 @@
+import gradio as gr
+import os
+from rag_engine import RAGEngine
+
+# Initialize engine
+engine = RAGEngine()
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+def process_pdf(file):
+    if file is None:
+        return "⚠️ No file uploaded.", gr.update(interactive=False)
+    try:
+        msg = engine.load_pdf(file.name)
+        return msg, gr.update(interactive=True)
+    except Exception as e:
+        return f"❌ Error: {e}", gr.update(interactive=False)
+
+def process_url(url):
+    if not url.strip():
+        return "⚠️ Please enter a URL.", gr.update(interactive=False)
+    try:
+        msg = engine.load_url(url.strip())
+        return msg, gr.update(interactive=True)
+    except Exception as e:
+        return f"❌ Error: {e}", gr.update(interactive=False)
+
+def process_text(text):
+    if not text.strip():
+        return "⚠️ Please paste some text.", gr.update(interactive=False)
+    try:
+        msg = engine.load_text(text.strip())
+        return msg, gr.update(interactive=True)
+    except Exception as e:
+        return f"❌ Error: {e}", gr.update(interactive=False)
+
+def chat(message, history):
+    if not message.strip():
+        return history, ""
+    answer = engine.answer(message)
+    history = history or []
+    history.append((message, answer))
+    return history, ""
+
+def reset_all():
+    engine.reset()
+    return [], "", "", "", "", "⚠️ No knowledge source loaded.", gr.update(interactive=False), {}
+
+# ── UI Layout ────────────────────────────────────────────────────────────────
+
+with gr.Blocks(theme=gr.themes.Soft(), title="RAG Chatbot") as demo:
+    gr.Markdown("# 🤖 RAG Chatbot")
+    gr.Markdown("### Chat with your PDFs, URLs, or raw text using AI.")
+    
+    with gr.Row():
+        # Left Column: Inputs
+        with gr.Column(scale=1):
+            with gr.Tab("📄 Upload PDF"):
+                pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+                pdf_btn = gr.Button("Process PDF", variant="primary")
+            
+            with gr.Tab("🌐 Load URL"):
+                url_input = gr.Textbox(label="Website URL", placeholder="https://example.com")
+                url_btn = gr.Button("Process URL", variant="primary")
+            
+            with gr.Tab("📝 Paste Text"):
+                text_input = gr.Textbox(label="Raw Text", lines=10, placeholder="Paste your text here...")
+                text_btn = gr.Button("Process Text", variant="primary")
+            
+            status = gr.Label(value="⚠️ No knowledge source loaded.")
+            
+            with gr.Accordion("🔍 NLP Insights", open=False):
+                nlp_output = gr.JSON(label="Intent & Entities")
+
+        # Right Column: Chat
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(label="Conversation", height=500)
+            with gr.Row():
+                msg_box = gr.Textbox(
+                    placeholder="Ask a question about the content...",
+                    show_label=False,
+                    scale=4,
+                    interactive=False,
+                )
+                send_btn = gr.Button("Send ➤", variant="primary", scale=1)
+
+            reset_btn = gr.Button("🗑️ Clear & Reset", variant="secondary")
+
+    # ── Example questions ────────────────────────────────────────────────────
+    gr.Examples(
+        examples=[
+            "What is the main topic of this document?",
+            "Summarise the key points in 3 bullet points.",
+            "What are the most important entities mentioned?",
+            "Give me a short conclusion based on the content.",
+        ],
+        inputs=msg_box,
+        label="💡 Example questions",
+    )
+
+    # ── Wire events ──────────────────────────────────────────────────────────
+    def chat_with_nlp(message, history):
+        if not message.strip():
+            return history, "", {}
+        answer, nlp_info = engine.answer_with_nlp(message)
+        history = history or []
+        history.append((message, answer))
+        return history, "", nlp_info
+
+    pdf_btn.click(process_pdf, pdf_input, [status, msg_box])
+    url_btn.click(process_url, url_input, [status, msg_box])
+    text_btn.click(process_text, text_input, [status, msg_box])
+
+    send_btn.click(chat_with_nlp, [msg_box, chatbot], [chatbot, msg_box, nlp_output])
+    msg_box.submit(chat_with_nlp, [msg_box, chatbot], [chatbot, msg_box, nlp_output])
+    reset_btn.click(reset_all, None, [chatbot, msg_box, pdf_input, url_input, text_input, status, msg_box, nlp_output])
+
+if __name__ == "__main__":
+    demo.launch()