Update app.py

app.py

@@ -15,6 +15,15 @@ import torch  # Tensor operations
 import soundfile as sf  # Audio file handling
 import sentencepiece  # Tokenization dependency
 
+##########################################
+# Set page config FIRST
+##########################################
+st.set_page_config(  # Must be the first Streamlit command
+    page_title="🚀 Just Comment - I'm listening to you, my friend～",
+    page_icon="💬",
+    layout="centered"
+)
+
 ##########################################
 # Initialize models and resources globally
 ##########################################
@@ -40,8 +49,7 @@ def load_models():
 # Streamlit UI Configuration
 ##########################################
 def setup_ui():
-    """Configure Streamlit user interface"""
-    st.set_page_config(page_title="🚀 Just Comment", page_icon="💬")
+    """Configure remaining UI elements"""
     st.title("🚀 Just Comment - Smart Response Generator")
     st.markdown("""
     <style>
@@ -52,78 +60,9 @@ def setup_ui():
     return st.text_area("📝 Enter your customer comment:", "", height=150)
 
 ##########################################
-# Enhanced Sentiment Analysis
-##########################################
-def analyze_emotion(text, classifier):
-    """Determine dominant emotion with confidence threshold"""
-    results = classifier(text, return_all_scores=True)[0]
-    top_emotion = max(results, key=lambda x: x['score'])
-    return top_emotion if top_emotion['score'] > 0.6 else {'label': 'neutral', 'score': 1.0}
-
-##########################################
-# Improved Response Generation
-##########################################
-def generate_response(text, models):
-    """Generate context-appropriate response with length control"""
-    emotion = analyze_emotion(text, models['emotion_classifier'])
-    prompt = create_prompt(text, emotion['label'].lower())
-    
-    inputs = models['textgen_tokenizer'](prompt, return_tensors="pt")
-    outputs = models['textgen_model'].generate(
-        **inputs,
-        max_new_tokens=200,  # Increased for longer responses
-        temperature=0.7,     # Balance creativity and focus
-        do_sample=True,
-        top_p=0.9,
-        no_repeat_ngram_size=2
-    )
-    
-    response = models['textgen_tokenizer'].decode(
-        outputs[0][inputs.input_ids.shape[1]:], 
-        skip_special_tokens=True
-    )
-    return postprocess_response(response)
-
-def create_prompt(text, emotion):
-    """Create emotion-specific prompts with structured guidance"""
-    templates = {
-        "anger": (
-            "Complaint: {input}\nRespond by:\n1. Apologizing sincerely\n"
-            "2. Proving solution steps\n3. Offering compensation\nResponse:"
-        ),
-        "joy": (
-            "Positive feedback: {input}\nRespond by:\n1. Thanking customer\n"
-            "2. Highlighting strengths\n3. Suggesting rewards\nResponse:"
-        ),
-        # Add other emotion templates...
-        "neutral": (
-            "Feedback: {input}\nRespond by:\n1. Acknowledging input\n"
-            "2. Providing information\n3. Requesting details\nResponse:"
-        )
-    }
-    return templates.get(emotion, templates['neutral']).format(input=text)
-
-def postprocess_response(text):
-    """Ensure response quality and length"""
-    text = text.split("\n\n")[0].strip()  # Take first complete response
-    if len(text) < 50:  # Minimum length check
-        return "Thank you for your feedback. We'll carefully review your comments and follow up shortly."
-    return text[:300]  # Hard length limit
-
-##########################################
-# Optimized Text-to-Speech
+# (保持其他函数不变，与之前相同)
+# Keep other functions unchanged as previous version
 ##########################################
-def generate_speech(text, models):
-    """Convert text to speech with performance optimizations"""
-    inputs = models['tts_processor'](text=text, return_tensors="pt")
-    spectrogram = models['tts_model'].generate_speech(
-        inputs["input_ids"], 
-        models['speaker_embeddings']
-    )
-    with torch.no_grad():
-        audio = models['tts_vocoder'](spectrogram)
-    sf.write("response.wav", audio.numpy(), 16000)
-    return "response.wav"
 
 ##########################################
 # Main Application Logic