Update app.py

app.py

@@ -1,106 +1,106 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoProcessor, TextIteratorStreamer
-from threading import Thread
-import re
-import time 
-from PIL import Image
-import torch
-import spaces
-
-processor = AutoProcessor.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
-model_od = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-Object-Detection-v2", trust_remote_code=True)
-
-model.to("cuda:0")
-model_od.to("cuda:0")
-
-@spaces.GPU
-def bot_streaming(message, history, max_tokens, temperature, top_p, top_k, repetition_penalty):
-    # print(message)
-    if message.files:
-        image = message.files[-1].path
-    else:
-        # if there's no image uploaded for this turn, look for images in the past turns
-        # kept inside tuples, take the last one
-        for hist in history:
-            if type(hist[0])==tuple:
-                image = hist[0][-1].path
-
-    if image is None:
-        gr.Error("Lütfen önce bir resim yükleyin.")
-  
-    prompt = f"{message.text}"
-    image = Image.open(image).convert("RGB")
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda:0")
-
-    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
-    generation_kwargs = dict(
-        inputs, streamer=streamer, max_new_tokens=max_tokens, 
-        do_sample=True, temperature=temperature, top_p=top_p, 
-        top_k=top_k, repetition_penalty=repetition_penalty
-    )
-    generated_text = ""
-
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    text_prompt = f"{message.text}\n"
-
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        generated_text_without_prompt = buffer[len(text_prompt):]
-    
-        time.sleep(0.04)
-        yield generated_text_without_prompt
-
-
-gr.set_static_paths(paths=["static/images/"])
-logo_path = "static/images/logo-color-v2.png"
-
-PLACEHOLDER = f"""
-<div style="display: flex; flex-direction: column; align-items: center; text-align: center; margin: 30px">
-    <img src="/file={logo_path}" style="width: 60%; height: auto;">
-    <h3>Resim yükleyin ve bir soru sorun</h3>
-</div>
-"""
-
-# with gr.Blocks() as demo:
-    # with gr.Tab("Open-ended Questions (Soru-cevap)"):
-with gr.Accordion("Generation parameters", open=False) as parameter_accordion:
-    max_tokens_item = gr.Slider(64, 1024, value=512, step=64, label="Max tokens")
-    temperature_item = gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature")
-    top_p_item = gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p")
-    top_k_item = gr.Slider(0, 100, value=50, label="Top_k")
-    repeat_penalty_item = gr.Slider(0, 2, value=1.2, label="Repeat penalty")
-
-demo = gr.ChatInterface(
-    title="TraVisionLM - Turkish Visual Language Model",
-    description="",
-    fn=bot_streaming,
-    chatbot=gr.Chatbot(placeholder=PLACEHOLDER, scale=1),   
-    # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
-    additional_inputs=[max_tokens_item, temperature_item, top_p_item, top_k_item, repeat_penalty_item],
-    additional_inputs_accordion=parameter_accordion, 
-    stop_btn="Stop Generation", 
-    multimodal=True
-)
-
-    # with gr.Tab("Object Detection (Obje Tespiti)"):
-    #     gr.Image("tiger.jpg")
-    #     gr.Button("New Tiger")
-
-# demo = gr.ChatInterface(fn=bot_streaming, title="TraVisionLM - Turkish Visual Language Model", 
-#                         # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
-#                         description="",
-#                         additional_inputs=[
-#                             gr.Slider(64, 1024, value=512, step=64, label="Max tokens"),
-#                             gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature"),
-#                             gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p"),
-#                             gr.Slider(0, 100, value=50, label="Top_k"),
-#                             gr.Slider(0, 2, value=1.2, label="Repeat penalty"),
-#                         ],
-#                         additional_inputs_accordion_name="Text generation parameters",
-#                         # additional_inputs_accordion=
-#                         stop_btn="Stop Generation", multimodal=True)
-demo.launch(allowed_paths="")
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor, TextIteratorStreamer
+from threading import Thread
+import re
+import time 
+from PIL import Image
+import torch
+import spaces
+
+processor = AutoProcessor.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
+model_od = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-Object-Detection-v2", trust_remote_code=True)
+
+model.to("cuda:0")
+model_od.to("cuda:0")
+
+@spaces.GPU
+def bot_streaming(message, history, max_tokens, temperature, top_p, top_k, repetition_penalty):
+    # print(message)
+    if message.files:
+        image = message.files[-1].path
+    else:
+        # if there's no image uploaded for this turn, look for images in the past turns
+        # kept inside tuples, take the last one
+        for hist in history:
+            if type(hist[0])==tuple:
+                image = hist[0][-1].path
+
+    if image is None:
+        gr.Error("Lütfen önce bir resim yükleyin.")
+  
+    prompt = f"{message.text}"
+    image = Image.open(image).convert("RGB")
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda:0")
+
+    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
+    generation_kwargs = dict(
+        inputs, streamer=streamer, max_new_tokens=max_tokens, 
+        do_sample=True, temperature=temperature, top_p=top_p, 
+        top_k=top_k, repetition_penalty=repetition_penalty
+    )
+    generated_text = ""
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    text_prompt = f"{message.text}\n"
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        generated_text_without_prompt = buffer[len(text_prompt):]
+    
+        time.sleep(0.04)
+        yield generated_text_without_prompt
+
+
+gr.set_static_paths(paths=["static/images/"])
+logo_path = "static/images/logo-color-v2.png"
+
+PLACEHOLDER = f"""
+<div style="display: flex; flex-direction: column; align-items: center; text-align: center; margin: 30px">
+    <img src="/file={logo_path}" style="width: 60%; height: auto;">
+    <h3>Resim yükleyin ve bir soru sorun</h3>
+</div>
+"""
+
+# with gr.Blocks() as demo:
+    # with gr.Tab("Open-ended Questions (Soru-cevap)"):
+with gr.Accordion("Generation parameters", open=False) as parameter_accordion:
+    max_tokens_item = gr.Slider(64, 1024, value=512, step=64, label="Max tokens")
+    temperature_item = gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature")
+    top_p_item = gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p")
+    top_k_item = gr.Slider(0, 100, value=50, label="Top_k")
+    repeat_penalty_item = gr.Slider(0, 2, value=1.2, label="Repeat penalty")
+
+demo = gr.ChatInterface(
+    title="TraVisionLM - Turkish Visual Language Model",
+    description="",
+    fn=bot_streaming,
+    chatbot=gr.Chatbot(placeholder=PLACEHOLDER, scale=1),   
+    # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
+    additional_inputs=[max_tokens_item, temperature_item, top_p_item, top_k_item, repeat_penalty_item],
+    additional_inputs_accordion=parameter_accordion, 
+    stop_btn="Stop Generation", 
+    multimodal=True
+)
+
+    # with gr.Tab("Object Detection (Obje Tespiti)"):
+    #     gr.Image("tiger.jpg")
+    #     gr.Button("New Tiger")
+
+# demo = gr.ChatInterface(fn=bot_streaming, title="TraVisionLM - Turkish Visual Language Model", 
+#                         # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
+#                         description="",
+#                         additional_inputs=[
+#                             gr.Slider(64, 1024, value=512, step=64, label="Max tokens"),
+#                             gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature"),
+#                             gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p"),
+#                             gr.Slider(0, 100, value=50, label="Top_k"),
+#                             gr.Slider(0, 2, value=1.2, label="Repeat penalty"),
+#                         ],
+#                         additional_inputs_accordion_name="Text generation parameters",
+#                         # additional_inputs_accordion=
+#                         stop_btn="Stop Generation", multimodal=True)
+demo.launch(max_file_size="5mb")