Update app.py

app.py

@@ -9,9 +9,9 @@ from openai import OpenAI
 
 # Use writable cache for Hugging Face
 os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
-os.environ["HF_HOME"] = "/data/hf"
-os.environ["TRANSFORMERS_CACHE"] = "/data/hf"
-os.environ["XDG_CACHE_HOME"] = "/data/hf"
+os.environ["HF_HOME"] = "/tmp/hf"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf"
+os.environ["XDG_CACHE_HOME"] = "/tmp/hf"
 
 chatter_owl_url = "file/images/chatter_owl.png"
 
@@ -20,28 +20,29 @@ model = WhisperModel("base", compute_type="int8")  # Fastest CPU option
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
 # Prompt template
-PROMPT_TEMPLATE = f"""
+PROMPT_TEMPLATE = """
 You are a communication coach evaluating a user's spoken response.
 
-Please evaluate their speech using the following **five dimensions**:
+Evaluate the speech using these 5 dimensions:
 1. Clarity
 2. Structure
 3. Fluency
 4. Content Relevance
 5. Tone & Expression
 
-For each category:
-- Give a score from 0 to 10
-- Provide a brief but detailed explanation
+For each:
+- Score from 0–10
+- Provide brief, specific feedback
 
 Then:
-- Summarize their overall performance in 3–4 lines
-- End with one motivational sentence
+- Give an overall 3–4 line summary
+- End with a motivational sentence
 
-Make sure your tone is constructive and supportive, even if the scores are low.
+Lastly:
+- Provide an improved version of the speech that better demonstrates effective communication, based on their original transcript.
 
 Transcript:
-{transcript}
+\"\"\"{transcript}\"\"\"
 """
 
 # Convert uploaded audio to 16kHz mono WAV
@@ -98,6 +99,29 @@ def tutor_feedback(audio_file):
 
     return transcript, feedback_text, mp3_path
 
+# Generate example version of the same speech
+def generate_example_response(transcript):
+    prompt = f"""
+You are a communication tutor. Rewrite this speech transcript to be a more polished and confident version, while keeping the meaning and tone similar.
+
+Transcript:
+{transcript}
+"""
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are a helpful speaking coach."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.7
+    )
+    return response["choices"][0]["message"]["content"]
+
+# Hook up logic
+def show_example_feedback(transcript):
+    # Extract only example portion from GPT or return a pre-generated version
+    return generate_example_response(transcript)
+
 # Gradio interface
 # Path to your CSS file
 css_path = "light_mode_chatter_owl.css"  # Make sure this file is in the same directory as app.py
@@ -105,13 +129,14 @@ css_path = "light_mode_chatter_owl.css"  # Make sure this file is in the same di
 # Path to your CSS file
 css_path = "light_mode_chatter_owl.css"  # Make sure this file is in the same directory as app.py
 
-with gr.Blocks(css=css_path) as app:
+# App UI
+with gr.Blocks(css="light_mode_chatter_owl.css") as app:
     gr.Markdown(
         """
         <div id="header" style="text-align: center;">
-            <img src="file/images/chatter_owl.png">
-            <h2>🦉 Meet Chatter the Owl!</h2>
-            <p>Speak to me and I'll help you become a confident communicator. 🎤✨</p>
+            <img src="file/images/chatter_owl.png" width="120">
+            <h2>🦉 Meet <strong>Chatter the Owl</strong></h2>
+            <p>Speak into the mic, and I’ll give you structured feedback to help you grow as a communicator!</p>
         </div>
         """
     )
@@ -119,14 +144,26 @@ with gr.Blocks(css=css_path) as app:
     with gr.Row():
         audio_input = gr.Audio(type="filepath", label="🎙 Speak or Upload Audio")
 
+    transcript_box = gr.Textbox(label="📖 What You Said", interactive=False)
+    feedback_box = gr.Textbox(label="💡 Chatter’s Feedback", interactive=False)
+    audio_output = gr.Audio(label="🔊 Chatter Speaks", type="filepath")
+    hidden_transcript = gr.Textbox(visible=False)
+
     with gr.Row():
-        transcript_box = gr.Textbox(label="📖 What You Said")
-        feedback_box = gr.Textbox(label="💡 Chatter's Feedback")
+        try_again = gr.Button("🔁 Try Again")
+        show_example = gr.Button("🎯 Show Me an Example")
+
+    example_box = gr.Textbox(label="🗣 Suggested Improvement", visible=False)
+
+    # Interactions
+    audio_input.change(fn=tutor_feedback, inputs=audio_input,
+                       outputs=[transcript_box, feedback_box, audio_output, hidden_transcript])
+
+    try_again.click(fn=lambda: ("", "", None, "", ""), inputs=None,
+                    outputs=[transcript_box, feedback_box, audio_output, hidden_transcript, example_box])
 
-    audio_output = gr.Audio(label="🔊 Spoken Feedback", type="filepath")
+    show_example.click(fn=generate_example_response, inputs=hidden_transcript, outputs=example_box)
 
-    # Hook up your function (e.g., `tutor_feedback`)
-    audio_input.change(fn=tutor_feedback, inputs=audio_input, outputs=[transcript_box, feedback_box, audio_output])
 
 if __name__ == "__main__":
     print("✅ App is launching...")