Update app.py

app.py

@@ -1,63 +1,46 @@
 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()
+from groq import Groq
+
+# Set up the Groq client
+client = Groq(api_key="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")
+
+# Set the system prompt
+system_prompt = """You are a helpful, respectful and professional assistant.
+the conversation should be shorter.
+Your task is to assist a marketing team in getting the budget and providing market strategies according to the budget and the platforms they're running ads on.
+The platforms include Google and Meta.
+You should consider the budget, the target audience, the goals of the campaign, and the strengths and weaknesses of each platform when providing market strategies.
+the content should be optimized and summerized.
+make the budget in Indian ruppes."""
+
+# Initialize an empty list to store the conversation history
+conversation_history = []
+
+# Define a function to handle user messages
+def handle_message(user_message):
+    # Add the user's message to the conversation history
+    conversation_history.append({"role": "user", "content": user_message})
+
+    # Use the Groq client to get a response from the language model
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            *conversation_history
+        ],
+        model="llama3-8b-8192",
+    )
+
+    # Add the language model's response to the conversation history
+    conversation_history.append({"role": "assistant", "content": chat_completion.choices[0].message.content})
+
+    # Return the language model's response
+    return chat_completion.choices[0].message.content
+
+# Create a Gradio interface with a chatbot component
+iface = gr.Interface(fn=handle_message, inputs="text", outputs="text", title="Quantum Marketing AI")
+
+# Launch the interface
+iface.launch(share=True)