Update app.py

app.py

@@ -43,17 +43,17 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
     # Initialize Pixeltable
     pxt.drop_table('mistral_prompts', ignore_errors=True)
     t = pxt.create_table('mistral_prompts', {
-        'task': pxt.StringType(),
-        'system': pxt.StringType(),
-        'input_text': pxt.StringType(),
-        'timestamp': pxt.TimestampType(nullable=True),
-        'temperature': pxt.FloatType(nullable=True),
-        'top_p': pxt.FloatType(nullable=True),
-        'max_tokens': pxt.IntType(nullable=True),
-        'min_tokens': pxt.IntType(nullable=True),
-        'stop': pxt.StringType(nullable=True),
-        'random_seed': pxt.IntType(nullable=True),
-        'safe_prompt': pxt.BoolType(nullable=True)
+        'task': pxt.String,
+        'system': pxt.String,
+        'input_text': pxt.String,
+        'timestamp': pxt.Timestamp,
+        'temperature': pxt.Float,
+        'top_p': pxt.Float,
+        'max_tokens': pxt.Int,
+        'min_tokens': pxt.Int,
+        'stop': pxt.String,
+        'random_seed': pxt.Int,
+        'safe_prompt': pxt.Bool
     })
     
     # Insert new row into Pixeltable
@@ -88,21 +88,21 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         'safe_prompt': safe_prompt
     }
     
-    # Run inference with both models
-    t['open_mistral_nemo'] = chat_completions(model='open-mistral-nemo', **common_params)
-    t['mistral_medium'] = chat_completions(model='mistral-medium', **common_params)
+    # Add computed columns for model responses and analysis
+    t.add_computed_column(open_mistral_nemo=chat_completions(model='open-mistral-nemo', **common_params))
+    t.add_computed_column(mistral_medium=chat_completions(model='mistral-medium', **common_params))
     
     # Extract responses
-    t['omn_response'] = t.open_mistral_nemo.choices[0].message.content.astype(pxt.StringType())
-    t['ml_response'] = t.mistral_medium.choices[0].message.content.astype(pxt.StringType())
+    t.add_computed_column(omn_response=t.open_mistral_nemo.choices[0].message.content.astype(pxt.String))
+    t.add_computed_column(ml_response=t.mistral_medium.choices[0].message.content.astype(pxt.String))
     
-    # Run analysis
-    t['large_sentiment_score'] = get_sentiment_score(t.ml_response)
-    t['large_keywords'] = extract_keywords(t.ml_response)
-    t['large_readability_score'] = calculate_readability(t.ml_response)
-    t['open_sentiment_score'] = get_sentiment_score(t.omn_response)
-    t['open_keywords'] = extract_keywords(t.omn_response)
-    t['open_readability_score'] = calculate_readability(t.omn_response)
+    # Add computed columns for analysis
+    t.add_computed_column(large_sentiment_score=get_sentiment_score(t.ml_response))
+    t.add_computed_column(large_keywords=extract_keywords(t.ml_response))
+    t.add_computed_column(large_readability_score=calculate_readability(t.ml_response))
+    t.add_computed_column(open_sentiment_score=get_sentiment_score(t.omn_response))
+    t.add_computed_column(open_keywords=extract_keywords(t.omn_response))
+    t.add_computed_column(open_readability_score=calculate_readability(t.omn_response))
     
     # Retrieve results
     results = t.select(