Update app.py

app.py

@@ -1,63 +1,60 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+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",
+)
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+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
+    )
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    context = " ".join([hit.payload["text"] for hit in search_result])
+    return context
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+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.
 
-    messages.append({"role": "user", "content": message})
+### Context:
+{context}
 
-    response = ""
+### Question:
+{message}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+### Answer:
+"""
 
-        response += token
-        yield response
+    response = llm(prompt.format(ctx=context, question=message), max_tokens=243)
+
+    return response["choices"][0]["text"]
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 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)",
-        ),
-    ],
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    ]
 )
 
-
 if __name__ == "__main__":
     demo.launch()