Update qwen_model.py

qwen_model.py

@@ -18,35 +18,17 @@ qwen_pipeline = pipeline(
     tokenizer=tokenizer
 )
 
-def generate_response(retrieved_texts, query, max_new_tokens=500):
-    """
-    Generates a response based on the retrieved texts and query using Qwen.
-    Args:
-        retrieved_texts (list): List of retrieved text strings (e.g., from BLIP).
-        query (str): The user's question about the image.
-        max_new_tokens (int): Maximum tokens to generate for the answer.
-    Returns:
-        str: The generated answer.
-    """
-    # Construct a prompt that includes the image details as context
+def generate_response(retrieved_texts, query, max_new_tokens=200):
     context = "\n".join(retrieved_texts)
     prompt = f"This is the detail about the image:\n{context}\n\nQuestion: {query}\nAnswer:"
-
-    # Generate the text
-    result = qwen_pipeline(
-        prompt,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,       # or False if you want deterministic output
-        temperature=0.7,      # tweak as needed
-    )
-
-    # The pipeline returns a list of dicts with key "generated_text"
-    full_generation = result[0]["generated_text"]
-
-    # Optionally parse out the final answer if the model repeats the prompt
-    if "Answer:" in full_generation:
-        final_answer = full_generation.split("Answer:")[-1].strip()
+    
+    result = qwen_pipeline(prompt, max_new_tokens=max_new_tokens, ...)
+    generated_text = result[0]["generated_text"]
+    
+    if "Answer:" in generated_text:
+        final_answer = generated_text.split("Answer:")[-1].strip()
     else:
-        final_answer = full_generation
+        final_answer = generated_text
 
     return final_answer
+