Update app.py

app.py

@@ -1,60 +1,60 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from sentence_transformers import SentenceTransformer
-from qdrant_client import QdrantClient
-import torch
-from llama_cpp import Llama
-
-llm = Llama.from_pretrained(
-	repo_id="Suku0/mistral-7b-instruct-v0.3-bnb-4bit-GGUF",
-	filename="mistral-7b-instruct-v0.3-bnb-4bit.Q4_K_M.gguf",
-    n_ctx=16384
-)
-embedding_model = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
-qdrant_client = QdrantClient(
-    url="https://9a5cbf91-7dac-4dd0-80f6-13e512da1060.europe-west3-0.gcp.cloud.qdrant.io:6333",
-    api_key="1M-sCCVolJOOJeRXMBUh4wHfj8bkY4nZyHiau0LBllFr1vsXb1oDPg",
-)
-
-def retrieve_context(query):
-    query_vector = embedding_model.encode(query).tolist()
-
-    search_result = qdrant_client.search(
-        collection_name="ctx_collection",
-        query_vector=query_vector,
-        limit=10,
-        with_payload=True
-    )
-
-    context = " ".join([hit.payload["text"] for hit in search_result])
-    return context
-
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    context = retrieve_context(message)
-    prompt = f"""You are a helpful assistant. Please answer the user's question based on the given context. If the context doesn't provide any answer, say the context doesn't provide the answer.
-
-### Context:
-{context}
-
-### Question:
-{message}
-
-### Answer:
-"""
-
-    response = llm(prompt.format(ctx=context, question=message), max_tokens=243)
-
-    return response["choices"][0]["text"]
-
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
-    ]
-)
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from sentence_transformers import SentenceTransformer
+from qdrant_client import QdrantClient
+import torch
+from llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+	repo_id="Suku0/mistral-7b-instruct-v0.3-bnb-4bit-GGUF",
+	filename="mistral-7b-instruct-v0.3-bnb-4bit.Q4_K_M.gguf",
+    n_ctx=16384
+)
+embedding_model = SentenceTransformer('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
+qdrant_client = QdrantClient(
+    url="https://9a5cbf91-7dac-4dd0-80f6-13e512da1060.europe-west3-0.gcp.cloud.qdrant.io:6333",
+    api_key="1M-sCCVolJOOJeRXMBUh4wHfj8bkY4nZyHiau0LBllFr1vsXb1oDPg",
+)
+
+def retrieve_context(query):
+    query_vector = embedding_model.encode(query).tolist()
+
+    search_result = qdrant_client.search(
+        collection_name="ctx_collection",
+        query_vector=query_vector,
+        limit=10,
+        with_payload=True
+    )
+
+    context = " ".join([hit.payload["text"] for hit in search_result])
+    return context
+
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    context = retrieve_context(message)
+    prompt = f"""You are a helpful assistant. Please answer the user's question based on the given context. If the context doesn't provide any answer, say the context doesn't provide the answer.
+
+### Context:
+{context}
+
+### Question:
+{message}
+
+### Answer:
+"""
+
+    response = llm(prompt.format(ctx=context, question=message), max_tokens=243)
+
+    return response["choices"][0]["text"]
+
+app = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    ]
+)
+
+if __name__ == "__main__":
+    app.launch()