again

app.py

@@ -1,38 +1,49 @@
 import gradio as gr
 import torch
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Replace with your Hugging Face API token
-hf_api_token = "HF_API_KEY"
+hf_api_token = "YOUR_API_TOKEN"
 
-# Load the model and tokenizer globally, not inside the function
-model_name = "allenai/Molmo-7B-D-0924"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_api_token)
-model = AutoModelForQuestionAnswering.from_pretrained(model_name, trust_remote_code=True,use_auth_token=hf_api_token)
 
 def generate_questions(file_content):
+    # Load MolMo model and tokenizer with API token
+    model_name = "allenai/Molmo-7B-D-0924"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_api_token)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name, use_auth_token=hf_api_token)
+
     # Preprocess file content (assuming it's already text)
     try:
         text = file_content.decode("utf-8")
     except Exception as e:
         return f"Error decoding file: {str(e)}", ""
 
-    # Generate questions and answers
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(**inputs)
-    answers = tokenizer.decode(outputs.start_logits.argmax(), skip_special_tokens=True)
+    # Create a prompt
+    prompt = f"Here is a text: {text}. Please generate a set of questions based on the content."
+
+    # Generate questions
+    inputs = tokenizer(prompt, return_tensors="pt")
+    try:
+        outputs = model.generate(**inputs)
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    except Exception as e:
+        return f"Error processing file: {str(e)}", ""
 
-    # Extract questions and options (basic implementation)
+    # Extract questions and options (basic implementation, customize as needed)
     questions = []
     options = []
-    for answer in answers.split("."):
+    for answer in generated_text.split("."):
         if answer.startswith("Q"):
             questions.append(answer.strip())
         else:
             options.append(answer.strip())
 
+    if not questions:
+        return "No questions found in the uploaded text.", ""
+
     return questions, options
 
+
 # Create Gradio interface
 question_box = gr.Textbox(label="Questions")
 option_box = gr.Textbox(label="Options")
@@ -44,4 +55,4 @@ iface = gr.Interface(
     title="Question and Option Generator"
 )
 
-iface.launch()
+iface.launch()