Update llm.py

llm.py

@@ -1,64 +1,64 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from user_data import load_user_data, save_user_data
-from phonetics import analyze_audio_phonetically, extract_phonemes
-
-model_name = "BeastGokul/Nika-1.5B"
-llm_tokenizer = AutoTokenizer.from_pretrained(model_name)
-llm_model = AutoModelForCausalLM.from_pretrained(model_name)
-
-SYSTEM_PROMPT = """You are a specialized pronunciation assistant for non-native English speakers.\nYour job is to provide targeted, actionable feedback based on the user's speech or description.\n\nWhen analyzing pronunciation:\n1. Identify at most 2 specific phonemes or pronunciation patterns that need improvement\n2. Explain how the sound is correctly formed (tongue position, lip movement, etc.)\n3. Suggest one simple, targeted exercise for practice\n4. Be encouraging and note any improvements from previous sessions\n5. Use simple language appropriate for language learners\n\nWhen provided with phonetic analysis data, incorporate this information into your feedback.\n"""
-
-def get_llm_feedback(audio=None, text=None, reference_text=None, user_id="default", transcribe_func=None):
-    user_data = load_user_data(user_id)
-    # Process audio if provided
-    if audio:
-        from user_data import save_audio
-        audio_path = save_audio(audio, user_id)
-        # Transcribe if no text was provided
-        if not text and transcribe_func:
-            text = transcribe_func(audio_path)
-        # Get phonetic analysis
-        phonetic_analysis = analyze_audio_phonetically(audio_path, reference_text)
-        phonetic_info = f"""
-Phonetic analysis:\n- Detected phonemes: {phonetic_analysis['detected_phonemes']}\n"""
-        if reference_text:
-            phonetic_info += f"- Reference phonemes: {phonetic_analysis.get('reference_phonemes', 'N/A')}\n"
-    else:
-        audio_path = None
-        phonetic_info = ""
-    # Get user history context
-    history_context = ""
-    if user_data["practice_sessions"]:
-        phoneme_counts = {p: data["practice_count"] for p, data in user_data["phoneme_progress"].items()}
-        challenging = sorted(phoneme_counts.items(), key=lambda x: x[1], reverse=True)[:3]
-        history_context = f"""
-User has practiced {len(user_data['practice_sessions'])} times before.\nCommon challenging phonemes: {', '.join([p for p, _ in challenging])}.\n"""
-    # Build prompt for LLM
-    if text:
-        user_input = f"I said: '{text}'"
-        if reference_text and reference_text != text:
-            user_input += f". I was trying to say: '{reference_text}'"
-    else:
-        user_input = "Please analyze my pronunciation."
-    full_prompt = f"""{SYSTEM_PROMPT}\n\nUser history:\n{history_context}\n\n{phonetic_info}\n\nUser: {user_input}\n"""
-    # Get LLM response
-    inputs = llm_tokenizer(full_prompt, return_tensors="pt").to(llm_model.device)
-    import torch
-    with torch.no_grad():
-        outputs = llm_model.generate(
-            **inputs, 
-            max_new_tokens=200,
-            temperature=0.7,
-            top_p=0.9,
-            do_sample=True
-        )
-    response = llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    try:
-        response = response.split("Assistant: ")[-1].strip()
-    except:
-        pass
-    # Track the session if audio was provided
-    if audio_path:
-        from user_data import track_practice_session
-        track_practice_session(user_id, audio_path, text, reference_text, response)
-    return response, text
+from transformers import AutoTokenizer, AutoModelForCausalLM
+#from user_data import load_user_data, save_user_data
+from phonetics import analyze_audio_phonetically, extract_phonemes
+
+model_name = "BeastGokul/Nika-1.5B"
+llm_tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm_model = AutoModelForCausalLM.from_pretrained(model_name)
+
+SYSTEM_PROMPT = """You are a specialized pronunciation assistant for non-native English speakers.\nYour job is to provide targeted, actionable feedback based on the user's speech or description.\n\nWhen analyzing pronunciation:\n1. Identify at most 2 specific phonemes or pronunciation patterns that need improvement\n2. Explain how the sound is correctly formed (tongue position, lip movement, etc.)\n3. Suggest one simple, targeted exercise for practice\n4. Be encouraging and note any improvements from previous sessions\n5. Use simple language appropriate for language learners\n\nWhen provided with phonetic analysis data, incorporate this information into your feedback.\n"""
+
+def get_llm_feedback(audio=None, text=None, reference_text=None, user_id="default", transcribe_func=None):
+    user_data = load_user_data(user_id)
+    # Process audio if provided
+    if audio:
+        from user_data import save_audio
+        audio_path = save_audio(audio, user_id)
+        # Transcribe if no text was provided
+        if not text and transcribe_func:
+            text = transcribe_func(audio_path)
+        # Get phonetic analysis
+        phonetic_analysis = analyze_audio_phonetically(audio_path, reference_text)
+        phonetic_info = f"""
+Phonetic analysis:\n- Detected phonemes: {phonetic_analysis['detected_phonemes']}\n"""
+        if reference_text:
+            phonetic_info += f"- Reference phonemes: {phonetic_analysis.get('reference_phonemes', 'N/A')}\n"
+    else:
+        audio_path = None
+        phonetic_info = ""
+    # Get user history context
+    history_context = ""
+    if user_data["practice_sessions"]:
+        phoneme_counts = {p: data["practice_count"] for p, data in user_data["phoneme_progress"].items()}
+        challenging = sorted(phoneme_counts.items(), key=lambda x: x[1], reverse=True)[:3]
+        history_context = f"""
+User has practiced {len(user_data['practice_sessions'])} times before.\nCommon challenging phonemes: {', '.join([p for p, _ in challenging])}.\n"""
+    # Build prompt for LLM
+    if text:
+        user_input = f"I said: '{text}'"
+        if reference_text and reference_text != text:
+            user_input += f". I was trying to say: '{reference_text}'"
+    else:
+        user_input = "Please analyze my pronunciation."
+    full_prompt = f"""{SYSTEM_PROMPT}\n\nUser history:\n{history_context}\n\n{phonetic_info}\n\nUser: {user_input}\n"""
+    # Get LLM response
+    inputs = llm_tokenizer(full_prompt, return_tensors="pt").to(llm_model.device)
+    import torch
+    with torch.no_grad():
+        outputs = llm_model.generate(
+            **inputs, 
+            max_new_tokens=200,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+    response = llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    try:
+        response = response.split("Assistant: ")[-1].strip()
+    except:
+        pass
+    # Track the session if audio was provided
+    if audio_path:
+        from user_data import track_practice_session
+        track_practice_session(user_id, audio_path, text, reference_text, response)
+    return response, text