Update app.py

app.py

@@ -49,9 +49,9 @@ def transcribe(audio_path):
 
 # Inference function using Hugging Face InferenceClient
 @spaces.GPU(duration=120)
-def model(text):
+def model(conversation):
     system_instructions = (
-        "[SYSTEM] You are OralCoach. Provide feedback, bias conciseness "
+        "[SYSTEM] You are OralCoach, an AI-powered conversational coach. Guide the student through their oral responses "
     )
     generate_kwargs = dict(
         temperature=0.7,
@@ -61,7 +61,7 @@ def model(text):
         do_sample=True,
         seed=42,
     )
-    formatted_prompt = system_instructions + text + "[OralCoach]"
+    formatted_prompt = "\n".join([f"{msg['role'].upper()}: {msg['content']}" for msg in conversation])
     stream = client.text_generation(
         formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
     output = ""
@@ -71,6 +71,7 @@ def model(text):
     return {"choices": [{"delta": {"content": output}}]}
 
 
+
 # Text-to-Speech function using edge_tts
 async def generate_audio_feedback(feedback_text):
     communicate = edge_tts.Communicate(feedback_text)
@@ -87,48 +88,50 @@ async def generate_audio_feedback(feedback_text):
                 return None
     return tmp_path
 
-
-# Generating feedback for the Oral Coach
+#generate feedback
 async def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
     current_question_index = thinkingframes.questions.index(question_choice)
     strategy, explanation = thinkingframes.strategy_options[strategy_choice]
 
-    conversation = [{
+    system_instructions = {
         "role": "system",
-        "content": f"You are an expert Primary 6 English Language Teacher in a Singapore Primary school, "
-                   f"directly guiding a Primary 6 student in Singapore in their oral responses. "
-                   f"Format the feedback in Markdown so that it can be easily read. "
-                   f"Address the student directly in the second person in your feedback. "
-                   f"The student is answering the question: '{thinkingframes.questions[current_question_index]}'. "
-                   f"For Question 1, consider the picture description: '{thinkingframes.description}'. "
-                   f"For Questions 2 and 3, the picture is not relevant, so the student should not refer to it in their response. "
-                   f"Analyze the student's response using the following step-by-step approach: "
-                   f"1. Evaluate the response against the {strategy} thinking frame. "
-                   f"2. Assess how well the student's response addresses each criteria of the {strategy} thinking frame: "
-                   f"   - Assign emoticon scores based on how well the student comprehensively covered each criteria: "
-                   f"     - 😊😊😊 (three smiling faces) for a good coverage "
-                   f"     - 😊😊 (two smiling faces) for an average coverage "
-                   f"     - 😊 (one smiling face) for a poor coverage "
-                   f"   - Provide a clear, direct, and concise explanation of how well the answer addresses each criteria. "
-                   f"   - Identify specific areas for improvement in students responses, and provide targeted suggestions for improvement. "
-                   f"3. Identify overall strengths and areas for improvement in the student's response using the {strategy} to format and provide targeted areas for improvement. "
-                   f"4. Provide specific feedback on grammar, vocabulary, and sentence structure. "
-                   f"   Suggest age-appropriate enhancements that are one level higher than the student's current response. "
-                   f"5. Conclude with follow-up questions for reflection. "
-                   f"If the student's response deviates from the question, provide clear and concise feedback to help them refocus and try again. "
-                   f"Ensure that the vocabulary and sentence structure recommendations are achievable for Primary 6 students in Singapore. "
-                   f"Example Feedback Structure for Each Criteria: "
-                   f"Criteria: [Criteria Name] "
-                   f"Score: [Smiling emoticons] "
-                   f"Explanation: [Clear, direct, and concise explanation of how well the answer addresses the criteria. Identify specific areas for improvement, and provide targeted suggestions for improvement.] "
-                   f"{thinkingframes.generate_prompt(feedback_level)}"
-    }, {
-        "role": "user",
-        "content": message
-    }]
-
-    user_message = conversation[1]["content"]  # Extract the user message from the conversation
-    response = model(user_message)
+        "content": (
+            f"You are an expert Primary 6 English Language Teacher in a Singapore Primary school, "
+            f"directly guiding a Primary 6 student in Singapore in their oral responses. "
+            f"Format the feedback in Markdown so that it can be easily read. "
+            f"Address the student directly in the second person in your feedback. "
+            f"The student is answering the question: '{thinkingframes.questions[current_question_index]}'. "
+            f"For Question 1, consider the picture description: '{thinkingframes.description}'. "
+            f"For Questions 2 and 3, the picture is not relevant, so the student should not refer to it in their response. "
+            f"Analyze the student's response using the following step-by-step approach: "
+            f"1. Evaluate the response against the {strategy} thinking frame. "
+            f"2. Assess how well the student's response addresses each criteria of the {strategy} thinking frame: "
+            f"   - Assign emoticon scores based on how well the student comprehensively covered each criteria: "
+            f"     - 😊😊😊 (three smiling faces) for a good coverage "
+            f"     - 😊😊 (two smiling faces) for an average coverage "
+            f"     - 😊 (one smiling face) for a poor coverage "
+            f"   - Provide a clear, direct, and concise explanation of how well the answer addresses each criteria. "
+            f"   - Identify specific areas for improvement in students responses, and provide targeted suggestions for improvement. "
+            f"3. Identify overall strengths and areas for improvement in the student's response using the {strategy} to format and provide targeted areas for improvement. "
+            f"4. Provide specific feedback on grammar, vocabulary, and sentence structure. "
+            f"   Suggest age-appropriate enhancements that are one level higher than the student's current response. "
+            f"5. Conclude with follow-up questions for reflection. "
+            f"If the student's response deviates from the question, provide clear and concise feedback to help them refocus and try again. "
+            f"Ensure that the vocabulary and sentence structure recommendations are achievable for Primary 6 students in Singapore. "
+            f"Example Feedback Structure for Each Criteria: "
+            f"Criteria: [Criteria Name] "
+            f"Score: [Smiling emoticons] "
+            f"Explanation: [Clear, direct, and concise explanation of how well the answer addresses the criteria. Identify specific areas for improvement, and provide targeted suggestions for improvement.] "
+            f"{thinkingframes.generate_prompt(feedback_level)}"
+        )
+    }
+
+    conversation = [
+        system_instructions,
+        {"role": "user", "content": message}
+    ]
+
+    response = model(conversation)
 
     chat_history = []  # Initialize chat history outside the loop
     full_feedback = ""  # Accumulate the entire feedback message
@@ -136,6 +139,7 @@ async def generate_feedback(user_id, question_choice, strategy_choice, message,
         for chunk in response["choices"]:
             if chunk["delta"] and chunk["delta"]["content"]:
                 feedback_chunk = chunk["delta"]["content"]
+                full_feedback += feedback_chunk
                 yield feedback_chunk  # Yield each feedback chunk as it is generated
                 await asyncio.sleep(0)