Update app.py

app.py

@@ -21,7 +21,7 @@ app = FastAPI()
 vqa_pipeline = pipeline("image-to-text", model="Salesforce/blip-vqa-base")
 code_generator = pipeline("text-generation", model="openai-community/gpt2-medium")
 table_analyzer = pipeline("table-question-answering", model="google/tapas-large-finetuned-wtq")
-qa_pipeline = pipeline("question-answering", model="google/flan-t5-large	")  # ✅ FIXED MODEL
+qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-large")  # ✅ FIXED
 
 # ✅ Functions for Document & Image QA
 def extract_text_from_pdf(pdf_file):
@@ -74,16 +74,21 @@ def answer_question_from_document(file, question):
     if not text:
         return "No text extracted from the document."
 
-    response = qa_pipeline(question=question, context=text)  # ✅ FIXED
-    return response["answer"]
+    # ✅ FLAN-T5 expects input in a specific format
+    input_text = f"Question: {question} Context: {text}"
+    response = qa_pipeline(input_text)
+
+    return response[0]["generated_text"]  # ✅ FIXED OUTPUT EXTRACTION
 
 def answer_question_from_image(image, question):
     image_text = extract_text_from_image(image)
     if not image_text:
         return "No text detected in the image."
 
-    response = qa_pipeline(question=question, context=image_text)  # ✅ FIXED
-    return response["answer"]
+    input_text = f"Question: {question} Context: {image_text}"
+    response = qa_pipeline(input_text)
+
+    return response[0]["generated_text"]
 
 # ✅ Gradio UI for Document & Image QA
 doc_interface = gr.Interface(