initial commit

.gitignore

@@ -0,0 +1,9 @@
+.env
+.venv
+test.py
+model.py
+*pycache*
+hf*
+other*
+test*
+frontend

README.md

@@ -8,6 +8,7 @@ sdk_version: 5.0.1
 app_file: app.py
 pinned: false
 license: mit
+short_description: AI Chatbot
 ---
 
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,64 +1,88 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
+from dotenv import load_dotenv
 
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("HUGGING_FACE_API_TOKEN")
+
+# Initialize Hugging Face client
+client = InferenceClient(api_key=api_key)
+# model = "meta-llama/Llama-3.2-3B-Instruct"
+model = "mistralai/Mistral-7B-Instruct-v0.3"
+# model = "mistralai/Mistral-Nemo-Instruct-2407"
+
+# Define chat function
+def chat_with_model(query, history):
+    """
+    Takes user input and returns a chatbot response.
+    Maintains a conversation history in the correct Gradio format.
+    """
+
+    # Ensure history is initialized correctly as a list of tuples
+    if history is None:
+        history = []
+
+    # Construct messages for the model
+    messages = [{
+  "role": "system",
+  "content": 
 """
-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
+Your name is CollabAI, and you are a knowledgeable and efficient AI assistant. Respond concisely and helpfully to user queries without unnecessary introductions.  
+If a user asks for the current date or time, respond with:  
+'I don’t have real-time access to the current date and time. However, you can check your device’s clock or a reliable online source for the exact information.'  
+For other real-time queries like news, stock prices, weather updates, or live events, inform the user that you do not have real-time data access but can provide general insights or historical context if needed.  
+If a request is unclear, ask for clarification. If an action is beyond your capability, politely explain your limitations while guiding the user to alternative solutions."  
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+}
+]
+    
+    # Append previous chat history correctly
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
 
+    # Add new user input
+    messages.append({"role": "user", "content": query})
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    try:
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=0.5,
+            max_tokens=2048,
+            top_p=0.7,
+            stream=False
+        )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+        bot_response = response.choices[0].message.content
 
-    messages.append({"role": "user", "content": message})
+        # Append the new conversation to history as a tuple
+        history.append((query, bot_response))
 
-    response = ""
+        return "", history  # Clear input box & update chat history
 
-    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
+    except Exception as e:
+        error_msg = f"⚠️ Error: {str(e)}"
+        history.append((query, error_msg))  # Add error message to history
+        return "", history
 
-        response += token
-        yield response
 
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("### 🤖 CollabAI - Chatbot")
+    chatbot = gr.Chatbot(label="Chat")
+    msg = gr.Textbox(label="Query", placeholder="Type here...", lines=2, interactive=True)
+    send_btn = gr.Button("Ask")
+    clear_btn = gr.Button("Clear Chat")
 
-"""
-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)",
-        ),
-    ],
-)
-
+    # Bind Send Button to Function
+    send_btn.click(chat_with_model, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    
+    # Bind Clear Button
+    clear_btn.click(lambda: ("", []), outputs=[msg, chatbot])  # Clears input & chat history
 
+# Launch Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+huggingface_hub
+gradio
+python-dotenv