Update app.py

app.py

@@ -8,11 +8,11 @@ from transformers import pipeline
 embedder = SentenceTransformer("all-MiniLM-L6-v2")
 qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
 
-def extract_text(file):
-    if file.name.endswith(".pdf"):
-        return "\n".join([page.extract_text() or "" for page in PdfReader(file).pages])
-    elif file.name.endswith(".docx"):
-        return "\n".join([p.text for p in docx.Document(file).paragraphs])
+def extract_text(file_path):
+    if file_path.endswith(".pdf"):
+        return "\n".join([page.extract_text() or "" for page in PdfReader(file_path).pages])
+    elif file_path.endswith(".docx"):
+        return "\n".join([p.text for p in docx.Document(file_path).paragraphs])
     return ""
 
 def chunk_text(text, chunk_size=500):
@@ -28,11 +28,11 @@ def chunk_text(text, chunk_size=500):
         chunks.append(buffer.strip())
     return chunks
 
-def ask_question(file, question, history):
-    if not file:
+def ask_question(file_path, question, history):
+    if not file_path:
         return "Please upload a file.", history
 
-    text = extract_text(file)
+    text = extract_text(file_path)
     chunks = chunk_text(text)
     emb_chunks = embedder.encode(chunks, convert_to_tensor=True)
     emb_question = embedder.encode(question, convert_to_tensor=True)
@@ -55,7 +55,7 @@ with gr.Blocks() as demo:
             file_input = gr.File(
                 label="Choose a PDF or Word file",
                 file_types=[".pdf", ".docx"],
-                type="file"
+                type="filepath"   # ✅ Fixed for Hugging Face
             )
 
     chatbot = gr.Chatbot(label="💬 Chat with Document")