Update app.py

app.py

@@ -1,64 +1,136 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    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
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import json
+import os
+from datetime import date
+from transformers import pipeline
+
+PROFILE_FILE = "about_me.json"
+DAILY_FILE = "daily_status.json"
+
+def load_json(path, default):
+    if os.path.exists(path):
+        with open(path) as f:
+            return json.load(f)
+    return default
+
+def save_json(path, data):
+    with open(path, "w") as f:
+        json.dump(data, f, indent=2)
+
+profile = load_json(PROFILE_FILE, {})
+daily = load_json(DAILY_FILE, {})
+
+def build_context(profile, daily):
+    recent_days = sorted(daily.keys(), reverse=True)[:7]
+    daily_lines = "\n".join([f"{d}: {daily[d].get('log','')}" for d in recent_days])
+    context = (
+        f"Profile:\n{json.dumps(profile, indent=2)}\n"
+        f"Recent daily logs:\n{daily_lines}\n"
+        "You are a helpful assistant. Answer only using the provided information. "
+        "If you don't know the answer, reply: 'Sheetal hasn't shared that yet!'"
+    )
+    return context
+
+def get_llm():
+    return pipeline(
+        "text-generation",
+        model="HuggingFaceH4/zephyr-7b-beta",
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        trust_remote_code=True,
+    )
+
+llm = None
+
+def chatbot_qa(user_q):
+    global llm
+    if llm is None:
+        llm = get_llm()
+    context = build_context(profile, daily)
+    prompt = f"System: {context}\nUser: {user_q}\nAssistant:"
+    outputs = llm(prompt, max_new_tokens=256)
+    answer = outputs[0]["generated_text"].split("Assistant:")[-1].strip()
+    return answer
+
+def update_profile(name, city, job, about):
+    profile.update({
+        "name": name,
+        "city": city,
+        "job": job,
+        "about": about
+    })
+    save_json(PROFILE_FILE, profile)
+    return "Profile updated!"
+
+def update_daily_log(freeform):
+    today = str(date.today())
+    daily[today] = {"log": freeform}
+    save_json(DAILY_FILE, daily)
+    return f"Today's log saved! ({today})"
+
+def get_profile_defaults():
+    return (
+        profile.get("name", "Sheetal"),
+        profile.get("city", ""),
+        profile.get("job", ""),
+        profile.get("about", "")
+    )
+
+def get_today_log():
+    today = str(date.today())
+    return daily.get(today, {}).get("log", "")
+
+def recent_logs():
+    logs = ""
+    for d in sorted(daily.keys(), reverse=True)[:5]:
+        logs += f"**{d}**: {daily[d]['log']}\n"
+    return logs
+
+with gr.Blocks(title="Sheetal's Personal Chatbot") as demo:
+    gr.Markdown("# 🌸 Sheetal's Personal Chatbot")
+
+    with gr.Tab("📝 Admin (Sheetal)"):
+        gr.Markdown("### Edit Your Profile")
+        with gr.Row():
+            name = gr.Textbox(label="Name", value=profile.get("name", "Sheetal"))
+            city = gr.Textbox(label="City", value=profile.get("city", ""))
+            job = gr.Textbox(label="Profession", value=profile.get("job", ""))
+        about = gr.Textbox(label="A few lines about you", value=profile.get("about", ""))
+        save_profile_btn = gr.Button("Save Profile")
+        profile_output = gr.Textbox(label="", interactive=False)
+
+        save_profile_btn.click(
+            fn=update_profile,
+            inputs=[name, city, job, about],
+            outputs=profile_output
+        )
+
+        gr.Markdown("---")
+        gr.Markdown("### 🌱 Add Your Daily Status (free text!)")
+        today_log = gr.Textbox(label="What do you want to remember about today?", value=get_today_log())
+        save_log_btn = gr.Button("Save Today's Log")
+        log_output = gr.Textbox(label="", interactive=False)
+
+        save_log_btn.click(
+            fn=update_daily_log,
+            inputs=today_log,
+            outputs=log_output
+        )
+
+        gr.Markdown("#### 📅 Recent Logs")
+        recent_logs_box = gr.Markdown(recent_logs())
+
+    with gr.Tab("💬 Ask About Sheetal"):
+        gr.Markdown("### 💬 Ask Anything About Sheetal")
+        user_q = gr.Textbox(label="Type your question here:")
+        ask_btn = gr.Button("Ask")
+        answer_box = gr.Textbox(label="Bot answer", interactive=False)
+
+        ask_btn.click(
+            fn=chatbot_qa,
+            inputs=user_q,
+            outputs=answer_box
+        )
+
+demo.launch()