Update app.py

app.py

@@ -7,6 +7,25 @@ GROQ_API_KEY = "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"  # Rep
 os.environ["GROQ_API_KEY"] = GROQ_API_KEY
 client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
+def refine_humanization(output_text, tone):
+    """
+    Add a third refinement pass for deeper humanization.
+    """
+    refinement_prompt = (
+        f"Take the following text and refine it to sound completely human-like, smooth, and natural. "
+        f"Ensure it flows seamlessly, incorporates subtle nuances like idioms or conversational phrasing, and avoids AI-detected patterns. "
+        f"The tone should be {tone}. Refined text: {output_text}"
+    )
+    try:
+        refinement_response = client.chat.completions.create(
+            messages=[{"role": "user", "content": refinement_prompt}],
+            model="llama-3.3-70b-versatile",
+            stream=False,
+        )
+        return refinement_response.choices[0].message.content.strip()
+    except Exception:
+        return output_text  # Return original output if refinement fails
+
 def enforce_word_count_and_humanization(output_text, input_word_count, original_prompt, tone):
     """
     Ensure output text has a word count equal to or greater than the input word count
@@ -17,21 +36,7 @@ def enforce_word_count_and_humanization(output_text, input_word_count, original_
 
     # If output meets word count and humanization, return as is
     if output_word_count >= input_word_count:
-        # Perform a second pass for natural flow and humanization
-        refinement_prompt = (
-            f"Refine the following text to make it sound more natural, human-like, and engaging in a {tone} tone. "
-            f"Ensure it reads fluently and avoids AI-detected phrasing: {output_text}"
-        )
-        try:
-            refinement_response = client.chat.completions.create(
-                messages=[{"role": "user", "content": refinement_prompt}],
-                model="llama-3.3-70b-versatile",  # Replace with the desired model
-                stream=False,
-            )
-            refined_text = refinement_response.choices[0].message.content.strip()
-            return refined_text
-        except Exception:
-            return output_text  # Return original output if refinement fails
+        return refine_humanization(output_text, tone)
 
     # If output is too short, regenerate with expansion
     expansion_prompt = (
@@ -41,11 +46,11 @@ def enforce_word_count_and_humanization(output_text, input_word_count, original_
     try:
         expansion_response = client.chat.completions.create(
             messages=[{"role": "user", "content": expansion_prompt}],
-            model="llama-3.3-70b-versatile",  # Replace with the desired model
+            model="llama-3.3-70b-versatile",
             stream=False,
         )
         expanded_text = expansion_response.choices[0].message.content.strip()
-        return expanded_text
+        return refine_humanization(expanded_text, tone)
     except Exception:
         return output_text  # Return original output in case of error
 
@@ -57,7 +62,7 @@ def split_text_into_chunks(text, max_words=500):
     return [" ".join(words[i:i + max_words]) for i in range(0, len(words), max_words)]
 
 # Streamlit App
-st.title("Advanced Humanizer & Rephraser App (Groq-powered)")
+st.title("Enhanced Humanizer & Rephraser App (Groq-powered)")
 st.subheader("Powered by Groq and Streamlit")
 
 # User Input
@@ -99,7 +104,7 @@ if st.button("Generate Output"):
                     # Call Groq API
                     chat_completion = client.chat.completions.create(
                         messages=[{"role": "user", "content": task_prompt}],
-                        model="llama-3.3-70b-versatile",  # Replace with the desired model
+                        model="llama-3.3-70b-versatile",
                         stream=False,
                     )