Rename gpt_model.py to qwen_model.py

gpt_model.py

@@ -1,30 +0,0 @@
-import openai
-
-
-def generate_response(retrieved_texts, query, max_tokens=150):
-    """
-    Generates a response based on the retrieved texts and query.
-
-    Args:
-    retrieved_texts (list): List of retrieved text strings.
-    query (str): Query string.
-    max_tokens (int): Maximum number of tokens for the response.
-
-    Returns:
-    str: Generated response.
-    """
-    context = "\n".join(retrieved_texts)
-    prompt = f"This is the detail about the image: {context}\n\nQuestion: {query}\n\nAnswer:"
-    
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=max_tokens,
-        n=1,
-        stop=None,
-        temperature=0.5,
-    )
-    return response.choices[0].message['content']

qwen_model.py

@@ -0,0 +1,52 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Replace with your target Qwen model on Hugging Face
+MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct-1M"
+
+# Initialize tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    device_map="auto",        # or "cuda", etc. if you want to specify
+    trust_remote_code=True
+)
+
+# Create pipeline
+qwen_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer
+)
+
+def generate_response(retrieved_texts, query, max_new_tokens=200):
+    """
+    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
+    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()
+    else:
+        final_answer = full_generation
+
+    return final_answer