Update app.py

app.py

@@ -56,101 +56,114 @@ def analyze_dominant_emotion(user_review):
     dominant_emotion = max(emotion_results, key=lambda x: x['score'])  # Identify the emotion with the highest confidence
     return dominant_emotion  # Return the dominant emotion (label and score)
 
+
 ##########################################
-# Step 2: Response Generation Function
+# Step 2：Response Generation Function
 ##########################################
+
+def prompt_gen(user_review):
+  dominant_emotion = analyze_dominant_emotion(user_review)
+  emotion_strategies = {
+      "anger": {
+          "prompt": (
+              "Customer complaint: '{review}'\n\n"
+              "As a customer service representative, craft a professional response that:\n"
+              "- Begins with sincere apology and acknowledgment\n"
+              "- Clearly explains solution process with concrete steps\n"
+              "- Offers appropriate compensation/redemption\n"
+              "- Keeps tone humble and solution-focused (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "disgust": {
+          "prompt": (
+              "Customer quality concern: '{review}'\n\n"
+              "As a customer service representative, craft a response that:\n"
+              "- Immediately acknowledges the product issue\n"
+              "- Explains quality control measures being taken\n"
+              "- Provides clear return/replacement instructions\n"
+              "- Offers goodwill gesture (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "fear": {
+          "prompt": (
+              "Customer safety concern: '{review}'\n\n"
+              "As a customer service representative, craft a reassuring response that:\n"
+              "- Directly addresses the safety worries\n"
+              "- References relevant certifications/standards\n"
+              "- Offers dedicated support contact\n"
+              "- Provides satisfaction guarantee (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "joy": {
+          "prompt": (
+              "Customer review: '{review}'\n\n"
+              "As a customer service representative, craft a concise response that:\n"
+              "- Specifically acknowledges both positive and constructive feedback\n"
+              "- Briefly mentions loyalty/referral programs\n"
+              "- Ends with shopping invitation (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "neutral": {
+          "prompt": (
+              "Customer feedback: '{review}'\n\n"
+              "As a customer service representative, craft a balanced response that:\n"
+              "- Provides additional relevant product information\n"
+              "- Highlights key service features\n"
+              "- Politely requests more detailed feedback\n"
+              "- Maintains professional tone (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "sadness": {
+          "prompt": (
+              "Customer disappointment: '{review}'\n\n"
+              "As a customer service representative, craft an empathetic response that:\n"
+              "- Shows genuine understanding of the issue\n"
+              "- Proposes personalized recovery solution\n"
+              "- Offers extended support options\n"
+              "- Maintains positive outlook (3-4 sentences)\n\n"
+              "Response:"
+          )
+      },
+      "surprise": {
+          "prompt": (
+              "Customer enthusiastic feedback: '{review}'\n\n"
+              "As a customer service representative, craft a response that:\n"
+              "- Matches customer's positive energy appropriately\n"
+              "- Highlights unexpected product benefits\n"
+              "- Invites to user community/events\n"
+              "- Maintains brand voice (3-4 sentences)\n\n"
+              "Response:"
+          )
+      }
+  }
+  
+  template = emotion_strategies[dominant_emotion['label'].lower()]["prompt"]
+  prompt = template.format(review=user_review)
+  print(prompt)
+  return prompt
+
 def response_gen(user_review):
-    """
-    Generate a concise and logical response based on the sentiment of the user's comment.
-    """
-    dominant_emotion = analyze_dominant_emotion(user_review)  # Determine the dominant emotion from the user's comment
-    emotion_label = dominant_emotion['label'].lower()  # Extract the emotion label in lowercase format
-
-    # Define response templates for each emotion
-    emotion_prompts = {
-        "anger": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a professional response that:\n"
-            "- Begins with sincere apology and acknowledgment\n"
-            "- Clearly explains solution process with concrete steps\n"
-            "- Offers appropriate compensation/redemption\n"
-            "- Keeps tone humble and solution-focused (3-4 sentences)\n\n"
-            "Response:"
-        ),
-        "disgust": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a response that:\n"
-            "- Immediately acknowledges the product issue\n"
-            "- Explains quality control measures being taken\n"
-            "- Provides clear return/replacement instructions\n"
-            "- Offers goodwill gesture (3-4 sentences)\n\n"
-            "Response:"
-        ),
-        "fear": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a reassuring response that:\n"
-            "- Directly addresses the safety worries\n"
-            "- References relevant certifications/standards\n"
-            "- Offers dedicated support contact\n"
-            "- Provides satisfaction guarantee (3-4 sentences)\n\n"
-            "Response:"
-        ),
-        "joy": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a concise and enthusiastic response that:\n"
-            "- Thanks the customer for their feedback\n"
-            "- Acknowledges both positive and constructive comments\n"
-            "- Invites them to explore loyalty programs\n\n"
-            "Response:"
-        ),
-        "neutral": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a balanced response that:\n"
-            "- Provides additional relevant product information\n"
-            "- Highlights key service features\n"
-            "- Politely requests more detailed feedback\n"
-            "- Maintains professional tone (3-4 sentences)\n\n"
-            "Response:"
-        ),
-        "sadness": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft an empathetic response that:\n"
-            "- Shows genuine understanding of the issue\n"
-            "- Proposes personalized recovery solution\n"
-            "- Offers extended support options\n"
-            "- Maintains positive outlook (3-4 sentences)\n\n"
-            "Response:"
-        ),
-        "surprise": (
-            f"'{user_review}'\n\n"
-            "As a customer service representative, craft a response that:\n"
-            "- Matches customer's positive energy appropriately\n"
-            "- Highlights unexpected product benefits\n"
-            "- Invites to user community/events\n"
-            "- Maintains brand voice (3-4 sentences)\n\n"
-            "Response:"
-        )
-    }
-    
-    prompt = emotion_prompts.get(
-        emotion_label,
-        f"Neutral feedback: '{user_review}'\n\nWrite a professional and concise response (50-200 words max).\n\nResponse:"
-    )  # Default to neutral if emotion is not found
-
-    # Load the tokenizer and language model for response generation
-    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load tokenizer for text processing
-    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load language model for response generation
-
-    inputs = tokenizer(prompt, return_tensors="pt")  # Tokenize the input prompt
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=150,  # Limit generated tokens to ensure concise responses
-        min_length=50,  # Ensure the generated response is logical and complete
-        no_repeat_ngram_size=2,  # Avoid repetitive phrases
-        temperature=0.7  # Add randomness for more natural responses
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Decode the generated response
-    return response  # Return the response
+  prompt = prompt_gen(user_review)
+  # Load model directly
+  from transformers import AutoTokenizer, AutoModelForCausalLM
+
+  tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")
+  model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B")
+
+  inputs = tokenizer(prompt, return_tensors="pt")
+  outputs = model.generate(**inputs, max_new_tokens=200)
+
+  input_length = inputs.input_ids.shape[1]
+  response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
+  # print(response)
+  return response
+
+
 
 ##########################################
 # Step 3: Text-to-Speech Conversion Function