Upload app.py

app.py

@@ -1,78 +1,58 @@
-# -*- coding: utf-8 -*-
-"""thai maternal health.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1sk8TdnCQ8qOKCkzn_hPmCj8uU6xaV3HM
-"""
-
-# Simple Chatbot Gradio + Google Gemini API 🚀
-
-#@title Mostrar Imagen
-from IPython.display import Image
-url = 'https://github.com/AleNunezArroyo/Medium/blob/main/img/BannerM4.png?raw=true'
-Image(url=url, width=800)
-
-# Install the Python SDK
-pip install -q -U google-generativeai
-
-# The library and API Key information is added
-import google.generativeai as genai
-import time  # Import the time module
-
-# Replace with your actual Google API key
-GOOGLE_API_KEY = "AIzaSyDw4vZTtNZrHN32Ekv5sS-FTvgp3KkqQhk"
-genai.configure(api_key=GOOGLE_API_KEY)
-
-# Model configuration
-model = genai.GenerativeModel('gemini-pro')
-
-# Chat conversations
-chat = model.start_chat(history=[])
-
-# Prompt tuning for maternal health
-def maternal_health_prompt(language_code):
-    return f"""You are a knowledgeable and compassionate maternal health expert.
-    Provide accurate, clear, and culturally sensitive information about maternal health,
-    pregnancy, childbirth, and postpartum care. Respond in the language corresponding to the language code: {language_code}.
-    Keep responses concise, friendly, and focused on evidence-based medical information.
-    If you're unsure about anything, recommend consulting a healthcare provider."""
-
-# Transform Gradio history to Gemini format
-def transform_history(history):
-    new_history = []
-    for chat in history:
-        new_history.append({"parts": [{"text": chat[0]}], "role": "user"})
-        new_history.append({"parts": [{"text": chat[1]}], "role": "model"})
-    return new_history
-
-# Response function
-def response(message, history, language_code):
-    global chat
-    # Update the chat history in the Gemini format
-    chat.history = transform_history(history)
-
-    # Use the prompt tuning for the maternal health expert context
-    prompt = maternal_health_prompt(language_code) + f"\n\nUser: {message}"
-
-    # Send the message and get the response
-    response = chat.send_message(prompt)
-    response.resolve()
-
-    # Each character of the answer is displayed
-    for i in range(len(response.text)):
-        time.sleep(0.05)
-        yield response.text[: i + 1]
-
-# Gradio interface
-import gradio as gr
-
-# Set up Gradio interface
-language_code = "th-TH"  # Change this to the desired language code
-gr.ChatInterface(response,
-                 title='Maternal Health Chatbot',
-                 textbox=gr.Textbox(placeholder="Ask your question about maternal health")).launch(debug=True)
-
-# Save chat history if necessary
-# chat.history can be accessed to save conversation history if required.
+import subprocess
+
+# Install the required library
+subprocess.check_call(["pip", "install", "-q", "-U", "google-generativeai"])
+
+import google.generativeai as genai
+import time
+import gradio as gr
+
+# Replace with your actual Google API key
+GOOGLE_API_KEY = "your_api_key_here"
+genai.configure(api_key="AIzaSyDw4vZTtNZrHN32Ekv5sS-FTvgp3KkqQhk")
+
+# Model configuration
+model = genai.GenerativeModel('gemini-pro')
+
+# Chat conversations
+chat = model.start_chat(history=[])
+
+# Prompt tuning for maternal health
+def maternal_health_prompt(language_code):
+    return f"""You are a knowledgeable and compassionate maternal health expert. 
+    Provide accurate, clear, and culturally sensitive information about maternal health, 
+    pregnancy, childbirth, and postpartum care. Respond in the language corresponding to the language code: {language_code}. 
+    Keep responses concise, friendly, and focused on evidence-based medical information. 
+    If you're unsure about anything, recommend consulting a healthcare provider."""
+
+# Transform Gradio history to Gemini format
+def transform_history(history):
+    new_history = []
+    for chat in history:
+        new_history.append({"parts": [{"text": chat[0]}], "role": "user"})
+        new_history.append({"parts": [{"text": chat[1]}], "role": "model"})
+    return new_history
+
+# Response function
+def response(message, history, language_code):
+    global chat
+    # Update the chat history in the Gemini format
+    chat.history = transform_history(history)
+    
+    # Use the prompt tuning for the maternal health expert context
+    prompt = maternal_health_prompt(language_code) + f"\n\nUser: {message}"
+    
+    # Send the message and get the response
+    response = chat.send_message(prompt)
+    response.resolve()
+
+    # Each character of the answer is displayed
+    for i in range(len(response.text)):
+        time.sleep(0.05)
+        yield response.text[: i + 1]
+
+# Gradio interface
+language_code = "th-TH"  # Change this to the desired language code
+gr.ChatInterface(response,
+                 title='Maternal Health Chatbot',
+                 textbox=gr.Textbox(placeholder="Ask your question about maternal health")).launch(debug=True)