Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib as plt
+import openai
+import gradio as gr
+import time
+import os
+
+# Importing required components directly from gradio
+from gradio import components
+
+SECRET_TOKEN = os.getenv('openai.api_key')
+messages = [{"role": "system", "content": "You are a doctor"}]
+
+def send_message(message):
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=message,
+        api_key=SECRET_TOKEN
+    )
+    ChatGPT_reply = response["choices"][0]["message"]["content"]
+    return ChatGPT_reply
+
+def adaptive_truncate(message, token_limit):
+    # Truncate the message content to fit within the token limit
+    tokens = message["content"].split()
+    truncated_tokens = []
+    total_tokens = 0
+    for token in tokens:
+        total_tokens += len(token.split())
+        if total_tokens <= token_limit:
+            truncated_tokens.append(token)
+        else:
+            break
+    message["content"] = " ".join(truncated_tokens)
+    return message
+
+def CustomChatGPT(enter_your_question):
+    # Adaptive token limit to leave some space for response
+    token_limit = 4000
+    
+    # Initialize the messages list
+    messages = [{"role": "system", "content": "You are a doctor"}]
+    
+    # Send user input as separate messages to the model
+    user_input_tokens = enter_your_question.split()
+    current_message = {"role": "user", "content": ""}
+    current_token_count = len("You are a doctor".split())
+    
+    for token in user_input_tokens:
+        token_tokens = len(token.split())
+        if current_token_count + token_tokens <= token_limit:
+            current_message["content"] += token + " "
+            current_token_count += token_tokens
+        else:
+            # Truncate the current message to fit within token limit
+            current_message = adaptive_truncate(current_message, token_limit)
+            # Send the current message and get the response
+            reply = send_message(messages + [current_message])
+            messages.append({"role": "user", "content": " ".join(current_message["content"].split())})
+            messages[-1]["content"] = reply
+            # Start the next message with the remaining token
+            current_message = {"role": "user", "content": token + " "}
+            current_token_count = token_tokens
+    
+    # Truncate and send the last message
+    current_message = adaptive_truncate(current_message, token_limit)
+    reply = send_message(messages + [current_message])
+    messages.append({"role": "user", "content": " ".join(current_message["content"].split())})
+    messages[-1]["content"] = reply
+
+    return reply
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=CustomChatGPT, 
+    inputs=components.Textbox(lines=1, label="Enter your question"),
+    outputs=components.Textbox(label="Doctor's advice"),
+    title="Doctor's desk. Ask any help related to health?",
+    examples=[
+        ["What are the symptoms of flu?"],
+        ["How can I prevent a cold?"],
+        ["Is it safe to take antibiotics for a viral infection?"],
+    ],
+    live=False,  # Removed 'live' mode so that action is taken only after submit button is clicked
+    allow_flagging="never",
+)
+
+iface.launch(inline=False)