Update app.py

app.py

@@ -1,64 +1,92 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import openai
+from langdetect import detect
 
-"""
-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")
+# Set up OpenAI API with custom Groq endpoint
+openai.api_key = "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"
+openai.api_base = "https://api.groq.com/openai/v1"
 
+# Function to provide definitions with humor and storytelling
+def command_handler(user_input):
+    if user_input.lower().startswith("define "):
+        term = user_input[7:].strip()
+        definitions = {
+            "nft": (
+                "An NFT (Non-Fungible Token) is like owning the *original* Mona Lisa, but digitally. "
+                "Sure, people can take photos or screenshots, but the *real* ownership? That’s yours on the blockchain! 🖼️"
+            ),
+            "smart contract": (
+                "A smart contract is basically a vending machine but for the internet. You put in the crypto (coin), "
+                "the code checks everything (no dodgy snacks here), and boom—you get what you asked for, "
+                "all automated and drama-free! 🤖"
+            ),
+            "blockchain": (
+                "Blockchain is like a digital diary, but it’s shared with your most trustworthy friends. "
+                "Each page is a block, and once written, it can’t be erased—so no one can rewrite your embarrassing pizza order. 🍕"
+            )
+        }
+        return definitions.get(term.lower(), "Hmm, I don’t have a fun story for that yet. Try another term!")
+    return None
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Function to get the Groq model's response with humor
+def get_groq_response(message, user_language):
+    try:
+        response = openai.ChatCompletion.create(
+            model="llama-3.1-70b-versatile",
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        f"You are Wiz, a cheerful and witty Web3 guide with a knack for humor and storytelling. "
+                        f"Explain complex concepts with jokes, analogies, and relatable examples. "
+                        f"Make users laugh and keep them curious about Web3. "
+                        f"Use a lighthearted, engaging tone to create memorable learning experiences."
+                    )
+                },
+                {"role": "user", "content": message}
+            ]
+        )
+        return response.choices[0].message["content"]
+    except Exception as e:
+        return f"Oops, looks like I tripped over the blockchain! Error: {str(e)}"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Function to handle chatbot interactions
+def chatbot(user_input, history=[]):
+    try:
+        # Detect the language of the user's input
+        detected_language = detect(user_input)
+        user_language = "Hindi" if detected_language == "hi" else "English"
+        
+        # Handle commands like "Define [term]"
+        command_response = command_handler(user_input)
+        if command_response:
+            history.append((user_input, command_response))
+            return history, history
+        
+        # Get the response from the Groq model with humor
+        bot_response = get_groq_response(user_input, user_language)
+        
+        # Append to conversation history
+        history.append((user_input, bot_response))
+        return history, history  # Return updated chat history and state
+    
+    except Exception as e:
+        return [(user_input, f"Yikes! Something went wrong: {str(e)}")], 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,
-    ):
-        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)",
-        ),
-    ],
+# Gradio Interface setup
+chat_interface = gr.Interface(
+    fn=chatbot,  # Function to call for chatbot interaction
+    inputs=["text", "state"],  # Input fields: user message and chat history (state)
+    outputs=["chatbot", "state"],  # Outputs: the chat and updated history (state)
+    live=False,  # Disable live chat, responses shown after submit
+    title="Wiz: Your Fun Web3 Companion 🧙‍♂️",  # Title of the app
+    description=(
+        "Welcome to Wiz, your Web3 companion with a sense of humor! 🎉\n\n"
+        "Ask me anything about Web3, blockchain, NFTs, or smart contracts.\n\n"
+        "I'll explain them with jokes, stories, and analogies to keep you entertained while you learn. Let's laugh and grow together! 😄"
+    )
 )
 
-
+# Launch the Gradio interface
 if __name__ == "__main__":
-    demo.launch()
+    chat_interface.launch()