Duplicate from Defalt-404/Bittensor_Explore

Co-authored-by: Kunj Kansara <[email protected]>

.gitattributes

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README.md

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+---
+title: Bittensor Explore
+emoji: ⚡
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.42.0
+app_file: app.py
+pinned: false
+duplicated_from: Defalt-404/Bittensor_Explore
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import http
+import ssl
+import json
+import warnings
+warnings.filterwarnings("ignore")
+
+def retrieve_api_key(url):
+    
+    context = ssl.create_default_context()
+    context.check_hostname = True
+    conn = http.client.HTTPSConnection(url, context=context)
+    conn.request("GET", "/admin/api-keys/")
+    api_key_response = conn.getresponse()
+    api_keys_data = (
+            api_key_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+        )
+    api_keys_json = json.loads(api_keys_data)
+    api_key = api_keys_json[0]["api_key"]
+    conn.close()
+    return api_key
+    
+    
+def get_benchmark_uids(num_miner):
+    
+    url="test.neuralinternet.ai"
+    api_key = retrieve_api_key(url)
+    
+    context = ssl.create_default_context()
+    context.check_hostname = True
+    conn = http.client.HTTPSConnection(url, context=context)
+    
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Endpoint-Version": "2023-05-19",
+        }
+    
+    conn.request("GET", f"/top_miner_uids?n={num_miner}", headers=headers)
+    miner_response = conn.getresponse()
+    miner_data = (
+            miner_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+        )
+    uids = json.loads(miner_data)
+    return uids
+
+
+
+def retrieve_response(payload):
+    
+    url="d509-65-108-32-175.ngrok-free.app"
+    api_key = retrieve_api_key(url)
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Endpoint-Version": "2023-05-19",
+        }
+    payload = json.dumps(payload)
+    context = ssl.create_default_context()
+    context.check_hostname = True
+    conn = http.client.HTTPSConnection(url, context=context)
+    conn.request("POST", "/chat", payload, headers)
+    init_response = conn.getresponse()
+    init_data = init_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+    init_json = json.loads(init_data)
+    
+    response_dict = dict()
+    for choice in init_json['choices']:
+        uid = choice['uid']
+        resp = choice['message']['content']
+        resp = resp.replace("\n", "").replace("\t", "")
+        response_dict[uid] = resp
+    response_text = '\n\n'.join([f'"{key}": "{value}"' for key, value in response_dict.items()])
+    return response_text
+
+
+
+def interface_fn(system_prompt, optn, arg, user_prompt):
+    
+    if len(system_prompt) == 0:
+        system_prompt = "You are an AI Assistant, created by bittensor and powered by NI(Neural Internet). Your task is to provide consise response to user's prompt"
+    
+    messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}]
+    payload = dict()
+    
+    if optn == 'TOP':
+        
+        if int(arg) > 30:
+            arg = 30
+        payload['top_n'] = int(arg)
+        payload['messages'] = messages
+        response = retrieve_response(payload)
+        return response
+    
+    elif optn == 'BENCHMARK':
+        
+        if int(arg) > 30:
+            arg = 30
+        uids = get_benchmark_uids(int(arg))
+        payload['uids'] = uids
+        payload['messages'] = messages
+        response = retrieve_response(payload)
+        return response
+    
+    else:
+        
+        uids = list()
+        if ',' in arg:
+            uids = [int(x) for x in arg.split(',')]
+        else:
+            uids = [arg]
+        payload['uids'] = uids
+        payload['messages'] = messages
+        response = retrieve_response(payload)
+        return response
+
+
+interface = gr.Interface(
+    fn=interface_fn,
+    inputs=[
+        gr.inputs.Textbox(label="System Prompt", optional=True),
+        gr.inputs.Dropdown(["TOP", "BENCHMARK", "UIDs"], label="Select Function"),
+        gr.inputs.Textbox(label="Arguement"),
+        gr.inputs.Textbox(label="Enter your question")
+        ],
+    outputs=gr.outputs.Textbox(label="Model Responses"),
+    title="Explore Bittensor Miners",
+    description="Enter parameters as per you want and get response",
+    examples=[["Your task is to provide consise response of user prompts", "TOP", 5, 'What is Bittensor?']
+            ,["Your task is to provide accurate, lengthy response with good lexical flow", "BENCHMARK", 5, "What is neural network and how its feeding mechanism works?"], 
+            ["Act like you're in the technology field for 10+ year and give unbiased opinion", "UIDs", '975,517,906,743,869' , "What are the potential ethical concerns surrounding artificial intelligence and machine learning in healthcare?"]])
+
+interface.launch(enable_queue=True)