Update app.py

app.py

@@ -108,7 +108,7 @@ def get_top_chunks(query, chunk_embeddings, text_chunks):
 
 client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
 
-def respond_cool(message, history, mom_type):
+def respond(message, history, mom_type):
     top_cool_results = get_top_chunks(message, cool_chunk_embeddings, cleaned_cool_chunks) # Complete this line
     #str_chunks = "\n".join(best_chunks)
 
@@ -130,51 +130,51 @@ def respond_cool(message, history, mom_type):
     )
     return response['choices'][0]['message']['content'].strip()
 
-chatbot = gr.ChatInterface(respond, type="messages")
+chatbot = gr.ChatInterface(respond_cool, type="messages")
 
-def respond_tutor(message, history, mom_type):
-    top_tutor_results = get_top_chunks(message, tutor_chunk_embeddings, cleaned_tutor_chunks)
-    #str_chunks = "\n".join(best_chunks)
+# def respond_tutor(message, history, mom_type):
+#     top_tutor_results = get_top_chunks(message, tutor_chunk_embeddings, cleaned_tutor_chunks)
+#     #str_chunks = "\n".join(best_chunks)
 
-    messages = [{"role": "system", "content": f"You are chatbot that plays the role of the user's extremely studious, tutor-like mom. Respond in full sentences, don't cut yourself off. Base your response on the provided context: {mom_type}"}, 
-               {"role": "user", 
-               "content": (
-                   f"Context:\n{top_tutor_results}\n\n"
-                   f"Question{message}"
-               )}]
+#     messages = [{"role": "system", "content": f"You are chatbot that plays the role of the user's extremely studious, tutor-like mom. Respond in full sentences, don't cut yourself off. Base your response on the provided context: {mom_type}"}, 
+#                {"role": "user", 
+#                "content": (
+#                    f"Context:\n{top_tutor_results}\n\n"
+#                    f"Question{message}"
+#                )}]
 
-    if history:
-        messages.extend(history)
+#     if history:
+#         messages.extend(history)
 
-    messages.append({"role": "user", "content": message})
+#     messages.append({"role": "user", "content": message})
 
-    response = client.chat_completion(
-        messages, 
-        temperature = 0.2
-    )
-    return response['choices'][0]['message']['content'].strip()
+#     response = client.chat_completion(
+#         messages, 
+#         temperature = 0.2
+#     )
+#     return response['choices'][0]['message']['content'].strip()
 
-def respond_strict(message, history):
-    top_strict_results = get_top_chunks(message, strict_chunk_embeddings, cleaned_strict_chunks)
-    #str_chunks = "\n".join(best_chunks)
+# def respond_strict(message, history):
+#     top_strict_results = get_top_chunks(message, strict_chunk_embeddings, cleaned_strict_chunks)
+#     #str_chunks = "\n".join(best_chunks)
 
-    messages = [{"role": "system", "content": f"You are chatbot that plays the role of the user's extremely strict mom. Respond in full sentences, don't cut yourself off. Base your response on the provided context: {top_strict_results}"}, 
-               {"role": "user", 
-               "content": (
-                   f"Context:\n{top_strict_results}\n\n"
-                   f"Question{message}"
-               )}]
+#     messages = [{"role": "system", "content": f"You are chatbot that plays the role of the user's extremely strict mom. Respond in full sentences, don't cut yourself off. Base your response on the provided context: {top_strict_results}"}, 
+#                {"role": "user", 
+#                "content": (
+#                    f"Context:\n{top_strict_results}\n\n"
+#                    f"Question{message}"
+#                )}]
 
-    if history:
-        messages.extend(history)
+#     if history:
+#         messages.extend(history)
 
-    messages.append({"role": "user", "content": message})
+#     messages.append({"role": "user", "content": message})
 
-    response = client.chat_completion(
-        messages, 
-        temperature = 0.2
-    )
-    return response['choices'][0]['message']['content'].strip()
+#     response = client.chat_completion(
+#         messages, 
+#         temperature = 0.2
+#     )
+#     return response['choices'][0]['message']['content'].strip()
 
     with gr.Blocks() as chatbot: