i

app.py

@@ -7,48 +7,9 @@ import json
 from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv())
 
-
 databricks_token  = os.getenv('TENATCH_TOKEN')
 model_uri  = "http://15.152.197.215/v1/completions"
-
-def extract_json(gen_text, n_shot_learning=0):
-    if(n_shot_learning == -1) :
-        start_index = 0
-    else :
-        start_index = gen_text.index("### Response:\n{") + 14
-    if(n_shot_learning > 0) :
-        for i in range(0, n_shot_learning):
-            gen_text = gen_text[start_index:]
-            start_index = gen_text.index("### Response:\n{") + 14
-    end_index = gen_text.find("}\n\n### ") + 1
-    return gen_text[start_index:end_index]
-
-def score_model(model_uri, databricks_token, prompt):
-  ds_dict={
-            "model": "debisoft/mpt-7b-awq-tester",
-            "prompt": prompt,
-            "temperature": 0.5,
-            "max_tokens": 1000}
-  headers = {
-    "Authorization": f"Bearer {databricks_token}",
-    "Content-Type": "application/json",
-  }
-  #ds_dict = {'dataframe_split': dataset.to_dict(orient='split')} if isinstance(dataset, pd.DataFrame) else create_tf_serving_json(dataset)
-  data_json = json.dumps(ds_dict, allow_nan=True)
-  print("***ds_dict: ")
-  print(ds_dict)
-  print("***data_json: ")
-  print(data_json)
-  response = requests.request(method='POST', headers=headers, url=model_uri, data=data_json)
-  if response.status_code != 200:
-      raise Exception(f"Request failed with status {response.status_code}, {response.text}")
-  return response.json()
-
-def get_completion(prompt):
-    return score_model(model_uri, databricks_token, prompt)
-
-def greet(input):
-	n_shot_learning = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+n_shot_learning = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
 
 ### Instruction:
 You are demanding customer
@@ -95,6 +56,44 @@ I am building an online community to help people to find dates.
 {{"solution": "FindDates.com", "problem": "finding a date", "features": "online community to help people find dates", "target_customer": "people looking for a date", "fg_will_use": "True", "reason_to_use": "I am looking for an online community to help people find dates. FindDates.com meets my needs and I would use it to find my next great date.","fg_will_pay": "True", "reason_to_pay": "I would not pay for it as I am looking for an online community to help people find dates. But for products related to dating, paying for it would be a no-brainer.","fg_will_invest": "False", "reason_to_invest": "There are many online dating platforms already.","score": "40"}}
 """
 
+def extract_json(gen_text, n_shot_learning=0):
+    if(n_shot_learning == -1) :
+        start_index = 0
+    else :
+        start_index = gen_text.index("### Response:\n{") + 14
+    if(n_shot_learning > 0) :
+        for i in range(0, n_shot_learning):
+            gen_text = gen_text[start_index:]
+            start_index = gen_text.index("### Response:\n{") + 14
+    end_index = gen_text.find("}\n\n### ") + 1
+    return gen_text[start_index:end_index]
+
+def score_model(model_uri, databricks_token, prompt):
+  ds_dict={
+            "model": "debisoft/mpt-7b-awq-tester",
+            "prompt": prompt,
+            "temperature": 0.5,
+            "max_tokens": 1000}
+  headers = {
+    "Authorization": f"Bearer {databricks_token}",
+    "Content-Type": "application/json",
+  }
+  #ds_dict = {'dataframe_split': dataset.to_dict(orient='split')} if isinstance(dataset, pd.DataFrame) else create_tf_serving_json(dataset)
+  data_json = json.dumps(ds_dict, allow_nan=True)
+  print("***ds_dict: ")
+  print(ds_dict)
+  print("***data_json: ")
+  print(data_json)
+  response = requests.request(method='POST', headers=headers, url=model_uri, data=data_json)
+  if response.status_code != 200:
+      raise Exception(f"Request failed with status {response.status_code}, {response.text}")
+  return response.json()
+
+def get_completion(prompt):
+    return score_model(model_uri, databricks_token, prompt)
+
+def greet(input):
+
 	sys_msg="You are demanding customer."
 
 	instruction = """Determine the product or solution, the problem being solved, features, target customer that is being discussed in the \
@@ -121,12 +120,8 @@ Give a score for the product. Format your response as a JSON object with \
 	print("***total_prompt:")
 	print(total_prompt)
 	response = get_completion(total_prompt)
-	#gen_text = response["predictions"][0]["generated_text"]
-	#return json.dumps(extract_json(gen_text, 3))
 	gen_text = response["choices"][0]["text"]
-	#return gen_text
 	return json.dumps(extract_json(gen_text, -1))
-	#return json.dumps(response)
 
 #iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 #iface.launch()