Add application file

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+from openai import OpenAI
+from qdrant_client import QdrantClient
+
+import os
+
+qclient = QdrantClient(
+    url="https://68106439-3d00-42df-880f-a5519695f677.us-east4-0.gcp.cloud.qdrant.io:6333",
+    api_key=os.getenv("QDRANT_API_KEY"),
+)
+
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+def chat(prompt: str) -> str:
+    message = client.chat.completions.create(
+        model="anthropic/claude-3-haiku",
+        messages=[
+            {"role": "user", "content": prompt}
+        ],
+    ).choices[0].message.content
+    
+    return message
+
+def question_answer(chat_history, question):
+    import requests
+    API_URL = "https://api-inference.huggingface.co/models/BAAI/bge-large-zh-v1.5"
+    headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+    
+    payload = {
+	    "inputs": question,
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+    e = response.json()
+
+    search_result = client.search(
+        collection_name="test_collection", query_vector=e, limit=20
+    )
+    txt = '\n'.join([r.payload['text'] for r in search_result])
+    print(txt)
+
+    prompt = f"现在你是一个资深的工程师管家，我将相关的信息已经从数据库中通过向量搜索给你了，如下\n{txt}\n, 根据这些信息回答我的这个问题\n{question}\n,"\
+        "尽量简短以及用数值去说明,如果并没有答案，请回答我不知道。"
+    
+    answer = chat(prompt)
+    chat_history.append([question, answer])
+    return chat_history
+
+with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:
+    gr.Markdown(f'<center><h3>Demo</h3></center>')
+    with gr.Row():
+        with gr.Group():
+            # with gr.Accordion("pdf file"):
+            #     file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
+            question = gr.Textbox(label='Enter your question here')
+            btn = gr.Button(value='Submit')
+
+        with gr.Group():
+            chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot")
+
+    btn.click(
+        question_answer,
+        inputs=[chatbot, question],
+        outputs=[chatbot],
+        api_name="predict",
+    )
+
+demo.launch(share=True)