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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,70 +1,122 @@
+# app.py
+
+
+# Import dotenv to load environment variables from a .env file
+from dotenv import load_dotenv
+
+# Load environment variables from a .env file
+load_dotenv()
+
+# -----------------------------
+# Configuration
+# -----------------------------
+# Use os.getenv to load environment variables
+OPENAI_API_KEY   = os.getenv("OPENAI_MOCEAN_KEY")
+PROMPT_ID   = os.getenv("PROMPT_ID")
+HF_TOKEN  = os.getenv("HF_TOKEN")
+
+from huggingface_hub import login
+import os
+
+if "HF_TOKEN" in os.environ:
+    login(os.environ["HF_TOKEN"])
+    
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+from openai import OpenAI
+import os
+
+
+
+# Server-side memory
+conversation_state = {}
+
+
+def generate_response(chat_id, message):
     """
-    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
+    Handles bot response using streaming,
+    maintains server-side conversation state.
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+    if chat_id not in conversation_state:
+        conversation_state[chat_id] = []
+
+    history = conversation_state[chat_id]
+
+    # Add user message to state
+    history.append({"role": "user", "content": message})
+
+    # Build input list
+    input_messages = history.copy()
+
+    # Create streaming response
+    stream = client.responses.stream(
+        model="gpt-4.1-mini",
+        input=input_messages,
+        prompt={"prompt_id": PROMPT_ID}
+    )
+
+    full_reply = ""
+
+    for event in stream:
+        if event.type == "response.output_text.delta":
+            token = event.delta
+            full_reply += token
+            yield full_reply  # Stream token-by-token
+
+    # Save bot response to history
+    conversation_state[chat_id].append(
+        {"role": "assistant", "content": full_reply}
+    )
+
+
+def start_chat():
+    """Creates a unique chat session ID."""
+    import uuid
+    return str(uuid.uuid4())
+
+
+# ---------- GRADIO UI --------------
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.HTML("""
+    <div style='display:flex; align-items:center; gap:15px;'>
+        <img src='assets/logo.png' style='height:60px;'>
+        <h2 style='margin:0;'>Company Research Assistant</h2>
+    </div>
+    """)
+
+    chat_id = gr.State()
+    chatbot = gr.Chatbot(height=450)
+
+    msg = gr.Textbox(
+        placeholder="Ask me anything…", 
+        label="Your Message"
+    )
+
+    def user_send(message, chat_history, cid):
+        if not cid:
+            cid = start_chat()
+        chat_history.append((message, None))  # Temporary blank
+        return "", chat_history, cid
+
+    def bot_reply(chat_history, cid):
+        user_msg = chat_history[-1][0]
+        response_stream = generate_response(cid, user_msg)
+        final_reply = ""
+
+        for chunk in response_stream:
+            final_reply = chunk
+            chat_history[-1] = (user_msg, final_reply)
+            yield chat_history
+
+    msg.submit(
+        user_send, 
+        inputs=[msg, chatbot, chat_id], 
+        outputs=[msg, chatbot, chat_id]
+    ).then(
+        bot_reply,
+        inputs=[chatbot, chat_id],
+        outputs=chatbot
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.42.0
+openai>=1.17.0
+huggingface_hub