Update app.py

app.py

@@ -32,32 +32,25 @@ def compare_texts(reference_text, transcribed_text):
     sm = difflib.SequenceMatcher(None, reference_text, transcribed_text)
     similarity_score = round(sm.ratio() * 100, 2)
 
+    # Construct HTML output
+    html_output = f"<strong>Fidelity Class:</strong> {'CORRECT' if similarity_score > 50 else 'INCORRECT'}<br>"
+    html_output += f"<strong>Quality Score:</strong> {similarity_score}<br>"
+    html_output += f"<strong>Transcribed Text:</strong> {transcribed_text}<br>"
+    html_output += "<strong>Word Score List:</strong><br>"
+
+    # Generate colored word score list
     for i, word in enumerate(reference_words):
         try:
             if word.lower() == transcribed_words[i].lower():
-                word_scores.append({"word": word, "quality_score": 100})
+                html_output += f'<span style="color: green;">{word}</span> '  # Correct words in green
+            elif difflib.get_close_matches(word, transcribed_words):
+                html_output += f'<span style="color: yellow;">{word}</span> '  # Close matches in yellow
             else:
-                word_scores.append({"word": word, "quality_score": 50})  # Assuming 50 if it's wrong
+                html_output += f'<span style="color: red;">{word}</span> '  # Incorrect words in red
         except IndexError:
-            word_scores.append({"word": word, "quality_score": 0})
-
-    fidelity_class = "CORRECT" if similarity_score > 50 else "INCORRECT"
-
-    output = {
-        "quota_remaining": -1,
-        "reference_text_from_application": reference_text,
-        "status": "success",
-        "text_score": {
-            "fidelity_class": fidelity_class,
-            "quality_score": similarity_score,
-            "text": reference_text,
-            "transcribedText": transcribed_text,
-            "word_score_list": word_scores
-        },
-        "version": "1.1"
-    }
-
-    return output
+            html_output += f'<span style="color: red;">{word}</span> '  # Words in reference that were not transcribed
+
+    return html_output
 
 # Step 3: Text-to-Speech Function
 def text_to_speech(paragraph):
@@ -83,7 +76,7 @@ interface = gr.Interface(
         gr.Textbox(lines=5, label="Input Paragraph"),
         gr.Audio(type="filepath", label="Record Audio")
     ], 
-    outputs="json",
+    outputs="html",
     title="Speech Recognition Comparison",
     description="Input a paragraph, record your audio, and compare the transcription to the original text."
 )