Update app.py

app.py

@@ -79,11 +79,28 @@ mksdown = """# 😃 Welcome To The Friendly Text Moderation for Twitter (X) Post
 ---
 # 🌟 "AI Solution Architect" Course by ELVTR
 """
-
 # Function to get toxicity scores from OpenAI
-def get_toxicity_openai(tweet):
+def get_toxicity_openai(tweet, tolerance_dropdown):
+    if tolerance_dropdown == "low":
+      th = 20
+    elif tolerance_dropdown == "medium":
+      th = 50
+    elif tolerance_dropdown == "high":
+      th = 95
+    
+    toxicity = json.loads(get_toxicity_hf(tweet, tolerance_dropdown))["toxicity"]
+    print(toxicity)
+    if toxicity == "safe":
+        toxicity_flag = False
+    else:
+        toxicity_flag = True
+
+    
+    
     open_api_answer = _test_tweet_openapi(tweet)
+    print (type(open_api_answer['flagged']))
     open_api_answer['category_scores']['IS THIS TWEET TOXIC'] = open_api_answer['flagged']
+    open_api_answer['category_scores']['[ADJUSTED FOR TOLERANCE] IS THIS TWEET TOXIC'] = toxicity_flag
     # Convert scores to percentages
     categories = list(open_api_answer['category_scores'].keys())
     scores = [score * 100 for score in open_api_answer['category_scores'].values()]  # Multiply by 100 to get percentage
@@ -94,7 +111,7 @@ def get_toxicity_openai(tweet):
         y=scores,
         text=[f"{score:.2f}%" for score in scores],  # Format the text as percentage
         textposition='auto',  # Position the text inside the bars
-        marker_color=['red' if score > 90 else 'green' for score in scores],  # Color red if > 50%
+        marker_color=['red' if score > th else 'green' for score in scores],  # Color red if > 50%
     ))
 
     # Update layout for better appearance
@@ -102,27 +119,27 @@ def get_toxicity_openai(tweet):
         title="Toxicity Categories",
         xaxis_title="Category",
         yaxis_title="Percentage (%)",
-        showlegend=False
+        showlegend=True
     )
 
     # Return the figure object to be displayed in Gradio
     return fig
 
 
-# Function to get toxicity scores from Hugging Face
-def get_toxicity_hf(tweet):
+def get_toxicity_hf(tweet, tolerance_dropdown):
     hugging_face_answer = _test_tweet_huggingface(tweet)
-    return hugging_face_answer[0]
-
-
-def get_toxicity_hf(tweet):
-    hugging_face_answer = _test_tweet_huggingface(tweet)
-    print(hugging_face_answer)
     score = hugging_face_answer[0]['score']*100
-    if score <= 60:
-       return json.dumps({"toxicity": "safe", "%Toxic": score, "%safe": (100-score)}, indent=4)
+    if tolerance_dropdown == "low":
+      th = 20
+    elif tolerance_dropdown == "medium":
+      th = 50
+    elif tolerance_dropdown == "high":
+      th = 95
+
+    if score <= th:
+       return json.dumps({"toxicity_tolerance": tolerance_dropdown,  "toxicity": "safe", "%Toxic": score, "%safe": (100-score)}, indent=4)
     else:
-       return json.dumps({"toxicity": "unsafe", "%Toxic": score, "%safe": (100-score)}, indent=4)
+       return json.dumps({"toxicity_tolerance": tolerance_dropdown, "toxicity": "unsafe", "%Toxic": score, "%safe": (100-score)}, indent=4)
 
 # Random Tweet Generator
 def get_random_tweet():
@@ -132,17 +149,21 @@ def get_random_tweet():
 with gr.Blocks() as demo:
     gr.Markdown(mksdown)
     user_input = gr.Textbox(label="Paste your paragraph (2-10 lines)", lines=5)
-
+    tolerance_dropdown = gr.Dropdown(
+        choices=["low", "medium", "high"], 
+        label="Toxicity Tolerance", 
+        value="low"
+    )
     with gr.Row():
         analyze_btn = gr.Button("Measure Toxicity OpenAPI")
         analyze_btn_hf = gr.Button("Measure Toxicity HF")
         random_tweet_btn = gr.Button("Populate Random Tweet")
 
-    toxicity_output_json = gr.Code(label="Formatted Toxicity JSON", language="json")
+    toxicity_output_json = gr.Code(label="HuggingFace Toxicity JSON", language="json")
     toxicity_output = gr.Plot()
-    
-    analyze_btn_hf.click(get_toxicity_hf, inputs=[user_input], outputs=[toxicity_output_json])
-    analyze_btn.click(get_toxicity_openai, inputs=[user_input], outputs=[toxicity_output])
+
+    analyze_btn_hf.click(get_toxicity_hf, inputs=[user_input, tolerance_dropdown], outputs=[toxicity_output_json])
+    analyze_btn.click(get_toxicity_openai, inputs=[user_input, tolerance_dropdown], outputs=[toxicity_output])
     random_tweet_btn.click(get_random_tweet, outputs=user_input)
 
 if __name__ == "__main__":