Update app.py

app.py

@@ -87,31 +87,34 @@ gpt_tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")
 gpt_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5")
 
 def rewrite_answer(question, short_answer):
-    prompt = (
-        f"Question: {question}\n"
-        f"Short Answer: {short_answer}\n"
-        f"Now write a full sentence answering the question:"
-    )
+    prompt = f"Write a full sentence to answer this:\nQ: {question}\nA: {short_answer}\nFull sentence:"
     inputs = gpt_tokenizer(prompt, return_tensors="pt")
     with torch.no_grad():
         outputs = gpt_model.generate(
             **inputs,
             max_new_tokens=50,
             do_sample=True,
-            top_p=0.95,
-            temperature=0.7,
+            top_k=40,
+            top_p=0.9,
+            temperature=0.6,
             pad_token_id=gpt_tokenizer.eos_token_id
         )
 
     generated = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    if "Now write a full sentence answering the question:" in generated:
-        rewritten = generated.split("Now write a full sentence answering the question:")[-1].strip()
+    # Try to isolate the answer portion
+    if "Full sentence:" in generated:
+        rewritten = generated.split("Full sentence:")[-1].strip()
     else:
         rewritten = generated.strip()
 
+    # Fallback to basic templating if model fails
+    if not rewritten or len(rewritten.split()) < 3:
+        rewritten = f"The answer to the question '{question}' is: {short_answer}."
+
     return rewritten
 
+
 def answer_question_from_image(image, question):
     if image is None or not question.strip():
         return "Please upload an image and ask a question.", None