Update app.py

app.py

@@ -1,37 +1,45 @@
 import gradio as gr
-from transformers import AutoModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import torch
 import pdfplumber
 
-# Load the model
-model = AutoModel.from_pretrained("deepseek-ai/Janus-1.3B")
+# Load the model and tokenizer
+model_name = "deepseek-ai/Janus-1.3B"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def process_input(input_data):
     if isinstance(input_data, str):
         return handle_text(input_data)
     elif isinstance(input_data, Image.Image):
         return handle_image(input_data)
-    elif isinstance(input_data, bytes):
-        return handle_pdf(input_data)
+    elif isinstance(input_data, dict) and "name" in input_data:
+        return handle_pdf(input_data["name"])
     else:
         return "Unsupported input type."
 
 def handle_text(text):
-    return f"Processed text: {text}"
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 def handle_image(image):
     return "Image processing not implemented yet."
 
-def handle_pdf(pdf_bytes):
-    with pdfplumber.open(pdf_bytes) as pdf:
+def handle_pdf(pdf_path):
+    with pdfplumber.open(pdf_path) as pdf:
         text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
     return handle_text(text)
 
 # Create Gradio app
 iface = gr.Interface(
     fn=process_input,
-    inputs=[gr.Textbox(label="Enter text"), gr.Image(label="Upload image"), gr.File(label="Upload PDF")],
+    inputs=[
+        gr.Textbox(label="Enter text"),
+        gr.Image(label="Upload image"),
+        gr.File(label="Upload PDF")
+    ],
     outputs=gr.Textbox(),
     title="Multimodal Chatbot",
     description="Handles text, images, and PDFs with the same entry point."