Update app.py

app.py

@@ -2,72 +2,91 @@ import gradio as gr
 from gradio_client import Client, handle_file
 from huggingface_hub import InferenceClient
 
-# Initialize the InferenceClient for FastLlama model
-client = InferenceClient("Qwen/QwQ-32B-Preview")
-
-# Initialize the Moondream Client for image description
+# Moondream2 için Client kullanıyoruz
 moondream_client = Client("vikhyatk/moondream2")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    image_input
-):
-    # Step 1: Handle the image and get its description using Moondream API
-    image_file = handle_file(image_input)
-    image_description = moondream_client.predict(
-        img=image_file,
+# Qwen/QwQ-32B-Preview için InferenceClient kullanıyoruz
+llama_client = InferenceClient("Qwen/QwQ-32B-Preview")
+
+# Sohbet geçmişi
+history = []
+
+# Resim açıklama fonksiyonu
+def describe_image(image, user_message, history):
+    # Resim var mı diye kontrol et
+    if image is None:
+        return "No image provided", history  # Hata mesajı döndür
+    # Resmi Moondream2 API'sine gönderiyoruz
+    result = moondream_client.predict(
+        img=handle_file(image),
         prompt="Describe this image.",
         api_name="/answer_question"
     )
     
-    # Step 2: Create the messages for the chat model
-    messages = [{"role": "system", "content": system_message}]
-    
-    # Add history to the messages
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    description = result  # Moondream2'den açıklama alıyoruz
+    history.append({"role": "user", "content": user_message})  # string olarak
+    history.append({"role": "assistant", "content": description})  # string olarak
 
-    # Add the image description to the user message
-    messages.append({"role": "user", "content": f"Here is the description of the image: {image_description}. Can you comment on it?"})
-    
-    # Step 3: Get the response from the assistant
-    response = ""
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
+    return description, history
+
+# Text ve history ile sohbet fonksiyonu
+def chat_with_text(user_message, history, max_new_tokens=250):
+    # Kullanıcı mesajını history'ye ekliyoruz
+    history.append({"role": "user", "content": user_message})  # string olarak
+
+    # Tüm geçmişi Qwen/QwQ-32B-Preview'e gönderiyoruz
+    texts = [{"role": msg["role"], "content": msg["content"]} for msg in history]
+    llama_result = llama_client.chat_completion(
+        messages=texts,
+        max_tokens=max_new_tokens,
+        temperature=0.7,
+        top_p=0.95
+    )
+
+    # Asistan cevabını alıyoruz ve history'ye ekliyoruz
+    assistant_reply = llama_result["choices"][0]["message"]["content"]
+    history.append({"role": "assistant", "content": assistant_reply})  # string olarak
+
+    return assistant_reply, history
+
+# Resim ve/veya metin tabanlı sohbet fonksiyonu
+def bot_streaming(message, history=None, max_new_tokens=250):
+    if history is None:  # Eğer `history` verilmemişse boş bir liste kullanıyoruz
+        history = []
+
+    user_message = message.get("text", "")
+    image = message.get("image", None)
+
+    if image:  # Resim varsa
+        response, history = describe_image(image, user_message, history)
+    else:  # Sadece metin mesajı varsa
+        response, history = chat_with_text(user_message, history, max_new_tokens)
+
+    # Yalnızca metin döndürülmeli, tarihçe değil
+    return response, history
 
-# Set up Gradio interface
+# Gradio arayüzü
 demo = gr.ChatInterface(
-    respond,
+    fn=bot_streaming,
+    title="Multimodal Chat Assistant",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly assistant named FastLlama.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-        gr.Image(type="pil", label="Upload Image for Description")  # Image input
+            minimum=10,
+            maximum=500,
+            value=250,
+            step=10,
+            label="Maximum number of new tokens to generate",
+        )
     ],
+    description=(
+        "This demo combines text and image understanding using Moondream2 for visual "
+        "tasks and Qwen/QwQ-32B-Preview for conversational AI. Upload an image, ask questions, "
+        "or just chat!"
+    ),
+    stop_btn="Stop Generation",
+    fill_height=True,
+    multimodal=True,
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(debug=True)