Update app.py

app.py

@@ -1,12 +1,87 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+!pip install -q sentence-transformers
+from sentence_transformers import SentenceTransformer
+import torch
+
+with open("knowlege.txt", "r", encoding="utf-8") as file:
+  knowlege = file.read()
+
+print(knowlege)
+
+cleaned_chunks = [chunk.strip() for chunk in knowlege.strip().split("\n") if chunk.strip()]
+print(cleaned_chunks)
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
+print(chunk_embeddings)
+
+cleaned_text = ""
+
+def get_top_chunks(query):
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    query_embedding_normalized = query_embedding / query_embedding.norm()
+
+    similarities = torch.matmul(chunk_embeddings, query_embedding_normalized)
+    print(similarities)
+    top_indices = torch.topk(similarities, k=5).indices.tolist()
+    print(top_indices)
+
+    return [cleaned_chunks[i] for i in top_indices]
+
+top_results = get_top_chunks("What are some good wizard characters?")
+print(top_results)
+
+
+
+
+
+
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# def respond(message, history):
+#     response = ""
+
+#     messages = [{"role": "system", "content": "You are a chatbot that helps users create characters for role playing games."}]
+
+#     if history:
+#         messages.extend(history)
+
+#     messages.append({"role": "user", "content": message})
+
+#     stream = client.chat_completion(
+#         messages,
+#         max_tokens=100,
+#         temperature=1.2,
+#         stream=True
+#     )
+
+#     for message in stream:
+#         token = message.choices[0].delta.content
+
+#         if token is not None:
+#             response += token
+#             yield response
+
 def respond(message, history):
     response = ""
 
-    messages = [{"role": "system", "content": "You are a friendly chatbot."}]
+    # Retrieve top chunks based on the current user message
+    top_chunks = get_top_chunks(message)
+    context = "\n".join(top_chunks)
+
+    # Add knowledge as part of system instructions
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a chatbot that helps users create characters for role-playing games. "
+                "Use the following knowledge to inform your answers:\n\n" + context
+            )
+        }
+    ]
 
     if history:
         messages.extend(history)
@@ -22,7 +97,6 @@ def respond(message, history):
 
     for message in stream:
         token = message.choices[0].delta.content
-
         if token is not None:
             response += token
             yield response