Update app.py

app.py

@@ -1,64 +1,63 @@
 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)",
+from composio_llamaindex import ComposioToolSet, App, Action
+from llama_index.core.agent import FunctionCallingAgentWorker
+from llama_index.core.llms import ChatMessage
+from llama_index.llms.openai import OpenAI
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Initialize ComposioToolSet and OpenAI LLM
+toolset = ComposioToolSet(api_key=os.getenv('COMPOSIO_API_KEY')) 
+tools = toolset.get_tools(apps=[App.TWITTER])
+
+llm = OpenAI(model="gpt-4o", api_key=os.getenv('OPENAI_API_KEY'))
+
+# Set up prefix messages for the agent
+prefix_messages = [
+    ChatMessage(
+        role="system",
+        content=(
+            f"""
+            You are a Twitter wrapped generator. Based on the Twitter username provided, analyze the user's profile, recent tweets, and engagement data.
+            Create a personalized "Twitter Wrapped" summary highlighting their top tweets, most engaging content, follower growth, and other key insights.
+            Generate the output in a structured JSON format that can be easily parsed programmatically. Include fields like "top_tweets", "engagement_stats", "follower_growth", and "summary_sheet_link".
+            """
         ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+    )
+]
+
+# Initialize the agent
+agent = FunctionCallingAgentWorker(
+    tools=tools,
+    llm=llm,
+    prefix_messages=prefix_messages,
+    max_function_calls=10,
+    allow_parallel_tool_calls=False,
+    verbose=True,
+).as_agent()
+
+def generate_wrapped(username):
+    """
+    Function to generate a "Twitter Wrapped" summary based on the Twitter username provided by the user.
+    """
+    user_input = f"Create a Twitter Wrapped summary for the username: {username}"
+    response = agent.chat(user_input)
+    return response
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""### Twitter Wrapped Generator
+    Enter a Twitter username below to generate your personalized Twitter Wrapped summary.
+    """)
+
+    username_input = gr.Textbox(label="Twitter Username", placeholder="e.g., @elonmusk")
+    output = gr.Textbox(label="Output", placeholder="Your Twitter Wrapped summary and Google Sheet link will appear here.", lines=10)
+
+    generate_button = gr.Button("Generate Wrapped")
+
+    generate_button.click(fn=generate_wrapped, inputs=username_input, outputs=output)
+
+# Launch the Gradio app
+demo.launch()