Duplicate from Defalt-404/Top-VS-Benchmark_Bittensor

Co-authored-by: Kunj Kansara <Defalt-404@users.noreply.huggingface.co>

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README.md

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+---
+title: Top-VS-Benchmark Bittensor
+emoji: 🏢
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.43.2
+app_file: app.py
+pinned: false
+duplicated_from: Defalt-404/Top-VS-Benchmark_Bittensor
+---
+
+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 json
+import ssl
+import http.client
+
+def get_api_key():
+    
+    context = ssl.create_default_context()
+    context.check_hostname = True
+    
+    conn = http.client.HTTPSConnection("test.neuralinternet.ai", context=context)
+    conn.request("GET", "/admin/api-keys/")
+    api_key_resp = conn.getresponse()
+    api_key_string = api_key_resp.read().decode("utf-8").replace("\n", "").replace("\t", "")
+    api_key_json = json.loads(api_key_string)
+    api_key = api_key_json[0]['api_key']
+    conn.close()
+    return api_key
+    
+def generate_top_response(system_prompt,model_input, api_key):
+    payload = json.dumps(
+            {"top_n": 100, "messages": [{"role": "system", "content": system_prompt},{"role": "user", "content": model_input}]}
+        )
+
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Endpoint-Version": "2023-05-19",
+    }
+        
+    context = ssl.create_default_context()
+    context.check_hostname = True
+
+    conn = http.client.HTTPSConnection("test.neuralinternet.ai", context=context)
+    conn.request("POST", "/chat", payload, headers)
+    response = conn.getresponse()
+    utf_string = response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+    print(utf_string)
+    json_resp = json.loads(utf_string)
+    conn.close()
+    for choice in json_resp['choices']:
+        uid = choice['uid']
+        return uid, choice['message']['content']
+
+def generate_benchmark_response(system_prompt, model_input, api_key):
+    
+    context = ssl.create_default_context()
+    context.check_hostname = True
+    
+    
+    conn = http.client.HTTPSConnection("test.neuralinternet.ai", context=context)
+    conn.request("GET", "/top_miner_uids")
+    benchmark_uid_resp = conn.getresponse()
+    benchmark_uid_string = benchmark_uid_resp.read().decode("utf-8").replace("\n", "").replace("\t", "")
+    benchmark_uid_json = json.loads(benchmark_uid_string)
+    conn.close()
+    
+    payload = json.dumps(
+            {"uids": benchmark_uid_json , "messages": [{"role": "system", "content": system_prompt},{"role": "user", "content": model_input}]}
+        )
+
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Endpoint-Version": "2023-05-19",
+    }
+        
+    conn = http.client.HTTPSConnection("test.neuralinternet.ai", context=context)
+    conn.request("POST", "/chat", payload, headers)
+    response = conn.getresponse()
+    utf_string = response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+    json_resp = json.loads(utf_string)
+    #print(utf_string)
+    conn.close()
+    
+    for choice in json_resp['choices']:
+        uid = choice['uid']
+        model_resp = choice['message']['content']
+        return uid, model_resp
+    
+def dynamic_function(system_prompt, 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"    
+    api_key = get_api_key()
+    top_uid, top_response = generate_top_response(system_prompt, prompt, api_key) 
+    benchmark_uid, benchmark_response = generate_benchmark_response(system_prompt, prompt, api_key)
+    
+    return f"TOP_{top_uid}: {top_response}\n\n\nBenchmark_{benchmark_uid}:{benchmark_response}"
+
+interface = gr.Interface(
+    fn=dynamic_function,
+    inputs=[
+        gr.inputs.Textbox(label="System Prompt", optional=True),
+        gr.inputs.Textbox(label="Enter your question")
+        ],
+    outputs=gr.outputs.Textbox(label="Responses"),
+    title="Bittensor Compare Util",
+)
+
+
+# Launch the Gradio Interface
+interface.launch(share=False, enable_queue=True)
+