Update app.py

app.py

@@ -7,47 +7,46 @@ import gradio as gr
 from gradio import FileData
 import time
 import spaces
-ckpt ="Daemontatox/DocumentCogito"
+
+ckpt = "Daemontatox/DocumentCogito"
 model = MllamaForConditionalGeneration.from_pretrained(ckpt,
     torch_dtype=torch.bfloat16).to("cuda")
 processor = AutoProcessor.from_pretrained(ckpt)
 
+SYSTEM_PROMPT = """You are a helpful AI assistant specialized in analyzing documents, images, and visual content. 
+Your responses should be clear, accurate, and focused on the specific details present in the provided materials. 
+When analyzing documents, pay attention to key information, formatting, and context. 
+For images, consider both obvious and subtle details that might be relevant to the user's query."""
 
 @spaces.GPU()
-def bot_streaming(message, history, max_new_tokens=2048):
-    
+def bot_streaming(message, history, max_new_tokens=2048, temperature=0.7):
     txt = message["text"]
     ext_buffer = f"{txt}"
     
-    messages= [] 
+    messages = [{"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]}]
     images = []
     
-
-    for i, msg in enumerate(history): 
+    for i, msg in enumerate(history):
         if isinstance(msg[0], tuple):
             messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "image"}]})
             messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
             images.append(Image.open(msg[0][0]).convert("RGB"))
         elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
-            # messages are already handled
             pass
-        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str): # text only turn
+        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str):
             messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
             messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
 
-    # add current message
     if len(message["files"]) == 1:
-        
-        if isinstance(message["files"][0], str): # examples
+        if isinstance(message["files"][0], str):
             image = Image.open(message["files"][0]).convert("RGB")
-        else: # regular input
+        else:
             image = Image.open(message["files"][0]["path"]).convert("RGB")
         images.append(image)
         messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image"}]})
     else:
         messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})
 
-
     texts = processor.apply_chat_template(messages, add_generation_prompt=True)
 
     if images == []:
@@ -56,8 +55,13 @@ def bot_streaming(message, history, max_new_tokens=2048):
         inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
     streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
 
-    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
-    generated_text = ""
+    generation_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,  # Add temperature parameter
+        do_sample=True,  # Enable sampling for temperature to take effect
+    )
     
     thread = Thread(target=model.generate, kwargs=generation_kwargs)
     thread.start()
@@ -69,32 +73,38 @@ def bot_streaming(message, history, max_new_tokens=2048):
         time.sleep(0.01)
         yield buffer
 
-
-demo = gr.ChatInterface(fn=bot_streaming, title="Document Analyzer", examples=[
-    [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]},
-    200],
-    [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]},
-    250],
-    [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]},
-    250],
-    [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]},
-    250],
-    [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 
-    250],
+demo = gr.ChatInterface(
+    fn=bot_streaming, 
+    title="Document Analyzer", 
+    examples=[
+        [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]}, 200, 0.7],
+        [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]}, 250, 0.7],
+        [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]}, 250, 0.7],
+        [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]}, 250, 0.7],
+        [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 250, 0.7],
     ],
-      textbox=gr.MultimodalTextbox(), 
-      additional_inputs = [gr.Slider(
-              minimum=10,
-              maximum=500,
-              value=2048,
-              step=10,
-              label="Maximum number of new tokens to generate",
-          )
-        ],
-      cache_examples=False,
-      description="MllM ",
-      stop_btn="Stop Generation", 
-      fill_height=True,
-    multimodal=True)
-    
+    textbox=gr.MultimodalTextbox(), 
+    additional_inputs=[
+        gr.Slider(
+            minimum=10,
+            maximum=500,
+            value=2048,
+            step=10,
+            label="Maximum number of new tokens to generate",
+        ),
+        gr.Slider(  # Add temperature slider
+            minimum=0.1,
+            maximum=2.0,
+            value=0.2,
+            step=0.1,
+            label="Temperature (0.1 = focused, 2.0 = creative)",
+        )
+    ],
+    cache_examples=False,
+    description="MllM with Temperature Control",
+    stop_btn="Stop Generation", 
+    fill_height=True,
+    multimodal=True
+)
+
 demo.launch(debug=True)