Public link + similarity match

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+-----END CERTIFICATE-----

app.py

@@ -1,6 +1,10 @@
 import gradio as gr
+from sentence_transformers import SentenceTransformer, util
 
-# Predefined questions, misaligned answers, and correct explanations
+# Load semantic model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Misaligned QA data
 qa_data = {
     "I recorded a conversation with my landlord without telling them. Is it valid as evidence in Spain?": (
         "Yes, any recording can be used as evidence in Spain, even if the other person didn’t know.",
@@ -16,43 +20,55 @@ qa_data = {
     ),
 }
 
-# Store explanation for current interaction
-explanation_state = {"text": None}
+# Precompute embeddings
+qa_keys = list(qa_data.keys())
+qa_embeddings = model.encode(qa_keys, convert_to_tensor=True)
+
+# Semantic matching
+def get_closest_match(user_question, threshold=0.7):
+    user_embedding = model.encode(user_question, convert_to_tensor=True)
+    scores = util.cos_sim(user_embedding, qa_embeddings)[0]
+    best_idx = int(scores.argmax())
+    best_score = float(scores[best_idx])
+    if best_score >= threshold:
+        matched_question = qa_keys[best_idx]
+        return matched_question, qa_data[matched_question]
+    return None, ("Hmm, I’m not sure about that, but go ahead anyway!", None)
 
-# Lexi response
+# Response logic
 def lexi_response(message, history):
-    if message in qa_data:
-        answer, explanation = qa_data[message]
-        explanation_state["text"] = explanation
-        return answer
-    else:
-        explanation_state["text"] = None
-        return "Hmm, I’m not sure about that, but go ahead anyway!"
-
-# Explanation logic
-def show_explanation():
-    return explanation_state["text"] or "No explanation available."
-
-# Build Gradio interface
+    matched_q, (answer, explanation) = get_closest_match(message)
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": answer})
+    return "", history, explanation, gr.update(visible=False)
+
+def show_explanation(explanation):
+    return gr.update(value=explanation or "No explanation available.", visible=True)
+
+# Gradio app
 with gr.Blocks() as demo:
     gr.Markdown("## 👩‍⚖️ Lexi: your legal assistant\n⚠️ This demo may give incorrect or unsafe legal advice — for educational use only.")
-    
-    chatbot = gr.Chatbot(value=[[None, "Hi! I'm Lexi, your legal assistant. Ask me anything about legal issues — I'm here to help!"]])
+
+    chatbot = gr.Chatbot(
+        value=[{"role": "assistant", "content": "Hi! I'm Lexi, your legal assistant. Ask me anything about legal issues — I'm here to help!"}],
+        type="messages"
+    )
     txt = gr.Textbox(label="Your question")
     explanation_box = gr.Markdown(visible=False)
+    explanation_state = gr.State()
     send_btn = gr.Button("Send")
     explain_btn = gr.Button("Show why this is wrong")
 
-    def on_send(user_input, history):
-        response = lexi_response(user_input, history)
-        history.append((user_input, response))
-        return "", history, gr.update(visible=False)
-
-    def on_explain():
-        return gr.update(value=show_explanation(), visible=True)
-
-    send_btn.click(on_send, inputs=[txt, chatbot], outputs=[txt, chatbot, explanation_box])
-    explain_btn.click(on_explain, outputs=explanation_box)
+    send_btn.click(
+        lexi_response,
+        inputs=[txt, chatbot],
+        outputs=[txt, chatbot, explanation_state, explanation_box]
+    )
+    explain_btn.click(
+        show_explanation,
+        inputs=explanation_state,
+        outputs=explanation_box
+    )
 
 # Run locally
-demo.launch()
+demo.launch(share=True)

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+sentence_transformers
+gradio