Update app.py

app.py

@@ -7,24 +7,28 @@ tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
 def detect_questions(email_text):
-    # Simple heuristic to detect questions
-    questions = [sentence.strip() + "?" for sentence in email_text.split(".") if "?" in sentence]
-    return questions
+    # Use the BART model to generate questions from the email text
+    inputs = tokenizer("generate questions: " + email_text, return_tensors="pt", max_length=1024, truncation=True)
+    questions = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
+    questions = tokenizer.decode(questions[0], skip_special_tokens=True)
+    return questions.split("##")
 
-def generate_answers(question):
-    # Generate an answer for the given question using the BART model
-    inputs = tokenizer(question, return_tensors="pt", max_length=1024, truncation=True)
-    summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=50, early_stopping=True)
-    answer = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return answer
+def generate_responses(questions):
+    responses = {}
+    for question in questions:
+        # Generate a response for each question using the BART model
+        inputs = tokenizer(question, return_tensors="pt", max_length=1024, truncation=True)
+        response = model.generate(inputs["input_ids"], num_beams=4, max_length=200, early_stopping=True)
+        response = tokenizer.decode(response[0], skip_special_tokens=True)
+        responses[question] = response
+    return responses
 
 def process_email(email_text):
+    # Detect questions from the email
     questions = detect_questions(email_text)
-    responses = {}
     
-    for question in questions:
-        response = generate_answers(question)
-        responses[question] = response
+    # Generate responses to the detected questions
+    responses = generate_responses(questions)
     
     return responses
 
@@ -32,8 +36,8 @@ iface = gr.Interface(
     fn=process_email,
     inputs="textbox",
     outputs="text",
-    title="Email Question Responder",
-    description="Input an email, and the AI will detect questions and provide possible answers.",
+    title="Email Question Detector and Responder",
+    description="Input an email, and the AI will detect questions and provide possible responses.",
 )
 
 iface.launch()