Changed to target Tenatch endpoint

app.py

@@ -8,13 +8,13 @@ from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv())
 
 
-databricks_token  = os.getenv('DATABRICKS_TOKEN')
-model_uri  = "https://dbc-eb788f31-6c73.cloud.databricks.com/serving-endpoints/Mpt-7b-tester/invocations"
+databricks_token  = os.getenv('TENATCH_TOKEN')
+model_uri  = "http://15.152.197.215/v1/completions"
 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
-Determine the product or solution, the problem being solved, features, target customer that are being discussed in the
+Determine the product or solution, the problem being solved, features, target customer that is being discussed in the
 following user prompt. State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.
 Finally, state if you would invest in it and elaborate on why.
 
@@ -30,7 +30,7 @@ Below is an instruction that describes a task, paired with an input that provide
 
 ### Instruction:
 You are demanding customer
-Determine the product or solution, the problem being solved, features, target customer that are being discussed in the
+Determine the product or solution, the problem being solved, features, target customer that is being discussed in the
 following user prompt. State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.
 Finally, state if you would invest in it and elaborate on why.
 
@@ -44,7 +44,7 @@ Thawrih brings diversity and inclusivity to the activewear market with its sport
 
 ### Instruction:
 You are demanding customer
-Determine the product or solution, the problem being solved, features, target customer that are being discussed in the
+Determine the product or solution, the problem being solved, features, target customer that is being discussed in the
 following user prompt. State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.
 Finally, state if you would invest in it and elaborate on why.
 
@@ -58,24 +58,28 @@ I am building an online community to help people to find dates.
 """
 
 def extract_json(gen_text, n_shot_learning=0):
-    start_index = gen_text.index("### Response:\n{") + 14
+    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.index("}\n\n### ") + 1
+    end_index = gen_text.find("}\n\n### ") + 1
     return gen_text[start_index:end_index]
 
 def score_model(model_uri, databricks_token, prompt):
-  dataset=pd.DataFrame({
-            "prompt":[prompt],
-            "temperature": [0.5],
-            "max_tokens": [1000]})
+  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)
+  #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)
@@ -121,8 +125,8 @@ 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 json.dumps(extract_json(gen_text, -1))
 
 iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Company"), gr.Textbox(label="Solution"), gr.Textbox(label="Customer"), gr.Textbox(label="Problem"), gr.Textbox(label="Feature"), gr.Textbox(label="Target Audience persona", lines=3)], outputs="json")
 iface.launch()