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README.md

@@ -1,13 +1,14 @@
 ---
 title: Phi2 Chatbot
-emoji: 🐢
-colorFrom: red
-colorTo: indigo
+emoji: 🤖
+colorFrom: indigo
+colorTo: blue
 sdk: gradio
-sdk_version: 5.38.2
+sdk_version: "4.24.0"
 app_file: app.py
 pinned: false
-short_description: Smalllmodel chatbbot
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+This is a lightweight Gradio chatbot app using the [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) model.

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Load model & tokenizer
+model_id = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
+model.eval()
+
+# Memory for chat history
+chat_history = []
+
+# System prompt
+system_prompt = "You are a helpful and friendly assistant."
+
+def chat(user_input):
+    # Combine system prompt and chat history
+    prompt = system_prompt + "\n"
+    for speaker, msg in chat_history:
+        prompt += f"{speaker}: {msg}\n"
+    prompt += f"User: {user_input}\nBot:"
+
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    with torch.no_grad():
+        output = model.generate(input_ids, max_new_tokens=100, do_sample=True, temperature=0.7)
+
+    full_reply = tokenizer.decode(output[0], skip_special_tokens=True)
+    reply = full_reply.split("Bot:")[-1].strip()
+
+    chat_history.append(("User", user_input))
+    chat_history.append(("Bot", reply))
+
+    return format_chat(chat_history)
+
+def format_chat(history):
+    return "\n\n".join([f"**{speaker}:** {msg}" for speaker, msg in history])
+
+def clear():
+    chat_history.clear()
+    return ""
+
+def export_chat():
+    export = "\n".join([f"{s}: {m}" for s, m in chat_history])
+    return gr.File.update(value=export.encode("utf-8"), filename="chat.txt")
+
+with gr.Blocks() as demo:
+    gr.Markdown("""# 🤖 Chat with Phi-2
+Small CPU-friendly chatbot using Hugging Face Transformers
+""")
+
+    chatbot = gr.Markdown()
+    with gr.Row():
+        user_input = gr.Textbox(placeholder="Type your message here...", show_label=False)
+        send_btn = gr.Button("Send")
+    with gr.Row():
+        clear_btn = gr.Button("🧹 Clear")
+        export_btn = gr.Button("💾 Export Chat")
+        file_out = gr.File(label="Download Chat")
+
+    send_btn.click(chat, inputs=user_input, outputs=chatbot)
+    user_input.submit(chat, inputs=user_input, outputs=chatbot)
+    clear_btn.click(clear, outputs=chatbot)
+    export_btn.click(export_chat, outputs=file_out)
+
+    chatbot.value = format_chat(chat_history)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio