Update app.py

app.py

@@ -20,24 +20,28 @@ model = WhisperModel("base", compute_type="int8")  # Fastest CPU option
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
 # Prompt template
-PROMPT_TEMPLATE = """
-You're a communication coach helping users improve their spoken English.
+PROMPT_TEMPLATE = f"""
+You are a communication coach evaluating a user's spoken response.
 
-Evaluate the following speech transcript and provide helpful, constructive feedback.
+Please evaluate their speech using the following **five dimensions**:
+1. Clarity
+2. Structure
+3. Fluency
+4. Content Relevance
+5. Tone & Expression
 
-Focus on:
-- Clarity
-- Fluency
-- Grammar
-- Filler words
-- Tone and pacing
-- Sentence structure
-- Overall communication effectiveness
+For each category:
+- Give a score from 0 to 10
+- Provide a brief but detailed explanation
 
-Transcript:
-\"\"\"{transcript}\"\"\"
+Then:
+- Summarize their overall performance in 3–4 lines
+- End with one motivational sentence
+
+Make sure your tone is constructive and supportive, even if the scores are low.
 
-Provide concise feedback in 3-4 bullet points followed by a one-line motivational sentence.
+Transcript:
+{transcript}
 """
 
 # Convert uploaded audio to 16kHz mono WAV
@@ -54,12 +58,12 @@ def generate_feedback_with_llm(transcript):
     prompt = PROMPT_TEMPLATE.format(transcript=transcript.strip())
 
     response = client.chat.completions.create(
-        model="gpt-4-1106-preview",
-        temperature=0.7,
-        messages=[
-            {"role": "system", "content": "You are a helpful communication tutor."},
-            {"role": "user", "content": prompt}
-        ]
+    model="gpt-4",
+    messages=[
+        {"role": "system", "content": "You are a communication coach that gives structured and emotionally aware feedback."},
+        {"role": "user", "content": prompt}
+    ],
+    temperature=0.7
     )
 
     return response.choices[0].message.content