Upload app.py

app.py

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+import gradio as gr
+import time
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.core.embeddings import resolve_embed_model
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.core import PromptTemplate
+
+# bge embedding model
+Settings.embed_model = resolve_embed_model("local:BAAI/bge-small-en-v1.5")
+query_wrapper_prompt = PromptTemplate(
+    "Below is an instruction that describes a task. "
+    "Write a response that appropriately completes the request.\n\n"
+    "### Instruction:\n{query_str}\n\n### Response:"
+)
+
+import torch
+
+llm = HuggingFaceLLM(
+    context_window=2048,
+    max_new_tokens=256,
+    generate_kwargs={"temperature": 0.25, "do_sample": False},
+    query_wrapper_prompt=query_wrapper_prompt,
+    tokenizer_name="Writer/camel-5b-hf",
+    model_name="Writer/camel-5b-hf",
+    device_map="auto",
+    tokenizer_kwargs={"max_length": 2048},
+    # uncomment this if using CUDA to reduce memory usage
+    # model_kwargs={"torch_dtype": torch.float16}
+)
+
+Settings.chunk_size = 512
+Settings.llm = llm
+
+info = {}
+
+def echo(message, history, system_prompt, tokens):
+    index = info['index']
+    query_engine = index.as_query_engine()
+    response = query_engine.query(message)
+    return response.response
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        folder = gr.File(file_count='directory')
+        docs = gr.Textbox(label="Documents")
+    system_prompt = gr.Textbox("You are helpful AI.", label="System Prompt")
+    slider = gr.Slider(10, 100, render=False)
+
+    gr.ChatInterface(
+        echo, additional_inputs=[system_prompt, slider]
+    )
+    def update_docs(filepath):
+        print(filepath)
+        documents = SimpleDirectoryReader(input_files=filepath).load_data()
+        index = VectorStoreIndex.from_documents(
+            documents,
+        )
+        info['index'] = index
+        return documents
+    folder.upload(update_docs, folder, docs)
+
+demo.launch()