Update app.py

app.py

@@ -1,10 +1,44 @@
-from gradio import Interface
+import gradio as gr
+from fastapi import FastAPI
+from chatbot import chatbot
+import anthropic
 
-def chat(message):
-  # Implement basic conversation logic here
-  # For example, provide predefined responses based on keywords
-  response = "Hi there! How can I help you today?"
-  return response
+# Function to interact with Anthropi API and generate response
+def get_anthropic_response(question):
+    # Initialize the Anthropi client with your API key
+    client = anthropic.Anthropic(api_key="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")
 
-interface = Interface(fn=chat, inputs="textbox", outputs="text")
-interface.launch()
+    # Specify parameters for text generation
+    model_name = "claude-3-opus-20240229"
+    max_tokens = 1024
+
+    # Generate response using the specified model and parameters
+    message = client.messages.create(
+        model=model_name,
+        max_tokens=max_tokens,
+        messages=[{"role": "user", "content": question}]
+    )
+
+    # Extract and return the generated response
+    return message.content
+
+# Function to simulate a chatbot interaction
+def chatbot(user_message):
+    # Use the Anthropi model to generate a response
+    response = get_anthropic_response(user_message)
+    print(response)
+    return response[0].text  # Directly return the generated text
+
+app = FastAPI()
+
+# Endpoint to handle chat requests
+@app.post("/chat", response_model=str)
+def chat_endpoint(chat_request: str):
+    return chatbot(chat_request)
+
+def main():
+    # Launch Gradio interface for the chatbot
+    gr.Interface(fn=chatbot, inputs="text", outputs="text", title="Chatbot").launch(share=True)
+
+if __name__ == "__main__":
+    main()