Update app.py

app.py

@@ -1,78 +1,76 @@
-from peft import AutoPeftModelForCausalLM
-from transformers import GenerationConfig
-from transformers import AutoTokenizer
-import torch
-import streamlit as st
-from streamlit_chat import message
-
-st.session_state.clicked=True
-
-def process_data_sample(example):
-
-    processed_example = "<|system|>\n You are a support chatbot who helps with user queries chatbot who always responds in the style of a professional.</s>\n<|user|>\n" + example + "</s>\n<|assistant|>\n"
-
-    return processed_example
-
-@st.cache_resource(show_spinner=True)
-def create_bot():
-
-    tokenizer = AutoTokenizer.from_pretrained("Vasanth/zephyr-support-chatbot")
-
-    model = AutoPeftModelForCausalLM.from_pretrained(
-                                                        "Vasanth/zephyr-support-chatbot",
-                                                        low_cpu_mem_usage=True,
-                                                        return_dict=True,
-                                                        torch_dtype=torch.float16,
-                                                        device_map="cuda"
-                                                    )
-
-    generation_config = GenerationConfig(
-                                            do_sample=True,
-                                            temperature=0.5,
-                                            max_new_tokens=256,
-                                            pad_token_id=tokenizer.eos_token_id
-                                        )
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+def respond(message, history, system_message):
+    # Mesajları hazırlıyoruz, sistem mesajı ile başlıyoruz
+    messages = [{"role": "system", "content": system_message}]
+
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+
+    # Son kullanıcı mesajını ekliyoruz
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    try:
+        # Yanıtları alıyoruz ve yayınlıyoruz
+        result = client.chat_completion(
+            messages=messages,
+            max_tokens=250,
+            temperature=0.7,
+            top_p=0.95,
+        )
+        for choice in result.choices:
+            response += choice.message.get('content', '')
+        yield response
+    except Exception as e:
+        # Hata durumunda hata mesajını döndürüyoruz
+        yield f"Hata: {e}"
+
+# Gradio arayüzünü oluşturuyoruz
+with gr.Blocks(theme=gr.Theme.from_hub('HaleyCH/HaleyCH_Theme')) as demo:
+    system_message = gr.HTML("""
+    <h1 style="color: #fff; text-shadow: 0 0 5px #fff, 0 0 10px #fff, 0 0 15px #fff, 0 0 10px #0000ff, 0 0 15px #0000ff; text-align: center;">
+    SIMULACRA GPT-2
+    </h1>
+    <p>🤖 Welcome to Simulacra user! See our account for more information.</p>
+    """)
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Mesajınızı yazın")
     
-    return model, tokenizer, generation_config
-
-model, tokenizer, generation_config = create_bot()
-
-bot = create_bot()
-
-def infer_bot(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-    outputs = model.generate(**inputs, generation_config=generation_config)
-    out_str = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(prompt, '')
-    return out_str
-
-def display_conversation(history):
-    for i in range(len(history["assistant"])):
-        message(history["user"][i], is_user=True, key=str(i) + "_user")
-        message(history["assistant"][i],key=str(i))
-
-def main():
-
-    st.title("Support Member 📚🤖")
-    st.subheader("A bot created using Zephyr which was finetuned to possess the capabilities to be a support member")
-
-    user_input = st.text_input("Enter your query")
-
-    if "assistant" not in st.session_state:
-        st.session_state["assistant"] = ["I am ready to help you"]
-    if "user" not in st.session_state:
-        st.session_state["user"] = ["Hey there!"]
-                
-    if st.session_state.clicked:
-        if st.button("Answer"):
-
-            answer = infer_bot(user_input)
-            st.session_state["user"].append(user_input)
-            st.session_state["assistant"].append(answer)
-
-            if st.session_state["assistant"]:
-                display_conversation(st.session_state)
-
+    # Butonları yan yana koymak için bir satır içine alıyoruz
+    with gr.Row():
+        clear = gr.Button("Temizle")
+        submit = gr.Button("Gönder")
+
+    def user_input(user_message, history):
+        # Mesajı HTML ile resim ekleyerek döndürme
+        user_message_with_image = f'<img src="file_path/favicon.ico" alt="icon" style="width: 16px; height: 16px; vertical-align: middle;"> {user_message}'
+        return "", history + [[user_message_with_image, None]]
+
+    def bot_response(history):
+        last_message = history[-1][0]
+        response_gen = respond(
+            message=last_message,
+            history=history[:-1],
+            system_message=system_message.value,
+        )
+        for response in response_gen:
+            history[-1][1] = response
+            yield history
+
+    msg.submit(user_input, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+    submit.click(lambda: msg.submit(), None, chatbot, queue=False)  # Gönder butonuna tıklandığında mesajı gönder
+
+# Uygulamayı başlatıyoruz
 if __name__ == "__main__":
-    main()
-    
-    
+    demo.launch(share=True)