Update app.py

app.py

@@ -1 +1,118 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[],"isInternetEnabled":true,"language":"python","sourceType":"script","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# 🧠💬 Advanced Mental Health Companion AI Chatbot with Gradio UI\n# Developed using Gemini 1.5 Pro API with RAG, JSON mode, embeddings, and vector search\n\n# --- 📌 Install Required Libraries ---\nimport os\nimport json\nimport gradio as gr\nimport google.generativeai as genai\nfrom langchain.vectorstores import FAISS\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.docstore.document import Document\nfrom langchain.text_splitter import CharacterTextSplitter\n\n# --- 📌 Load Gemini API Key ---\ngemini_key = os.getenv(\"GEMINI_API_KEY\")\ngenai.configure(api_key=gemini_key)\n\n# --- 📌 Initialize Gemini Model ---\nmodel = genai.GenerativeModel(model_name=\"gemini-1.5-pro\")\nchat = model.start_chat(history=[])\n\n# --- 📌 Define Core Functionalities ---\ndef detect_emotion(user_input):\n    prompt = f\"\"\"\n    Analyze the message and return a JSON with:\n    {{\n      \"emotion\": \"<Emotion>\",\n      \"tone\": \"<Suggested_Tone>\",\n      \"affirmation\": \"<One_Line_Affirmation>\",\n      \"response\": \"<Comforting response>\"\n    }}\n\n    Message: \"{user_input}\"\n    \"\"\"\n    response = model.generate_content(prompt, generation_config={\"response_mime_type\": \"application/json\"})\n    return json.loads(response.text)\n\ndef get_affirmation():\n    prompt = \"Provide a unique calming affirmation for someone feeling overwhelmed.\"\n    return model.generate_content(prompt).text\n\ndef get_journaling_prompt():\n    prompt = \"Give me a mental health journaling prompt.\"\n    return model.generate_content(prompt).text\n\ndef get_calming_technique():\n    prompt = \"Suggest a calming breathing or grounding technique.\"\n    return model.generate_content(prompt).text\n\n# --- 📌 Memory & Vector Store (RAG Setup) ---\ndocuments = [\n    Document(page_content=\"Take a deep breath. You are doing your best.\"),\n    Document(page_content=\"It's okay to not be okay. Give yourself grace.\"),\n    Document(page_content=\"You are not alone. Many have walked this path and found light again.\"),\n    Document(page_content=\"Small steps lead to big changes. Just begin.\")\n]\n\nsplitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)\nsplit_docs = splitter.split_documents(documents)\nembedding = OpenAIEmbeddings(openai_api_key=gemini_key)\nvector_db = FAISS.from_documents(split_docs, embedding)\n\ndef retrieve_supportive_text(user_input):\n    docs = vector_db.similarity_search(user_input, k=2)\n    return \"\\n\".join([d.page_content for d in docs])\n\n# --- 📌 Smart Response Generator ---\ndef generate_companion_response(name, issue):\n    emotion_data = detect_emotion(issue)\n    rag_support = retrieve_supportive_text(issue)\n\n    system_prompt = f\"\"\"\n    You're MindMate, a kind mental health assistant.\n    Respond personally to {name}.\n\n    Emotion: {emotion_data['emotion']}\n    Tone: {emotion_data['tone']}\n    Affirmation: {emotion_data['affirmation']}\n\n    Based on the above, and the following supportive texts:\n    {rag_support}\n\n    Compose a comforting and warm message to the user:\n    Message: {issue}\n    \"\"\"\n    response = chat.send_message(system_prompt)\n    return response.text, emotion_data['emotion'], emotion_data['affirmation']\n\n# --- 📌 Gradio UI ---\ndef chatbot_interface(name, issue):\n    response, emotion, affirmation = generate_companion_response(name, issue)\n    journaling = get_journaling_prompt()\n    technique = get_calming_technique()\n\n    return {\n        \"Emotion\": emotion,\n        \"Affirmation\": affirmation,\n        \"Companion Response\": response,\n        \"Journaling Prompt\": journaling,\n        \"Calming Tip\": technique\n    }\n\ninputs = [\n    gr.Textbox(label=\"Your Name\"),\n    gr.Textbox(label=\"What's troubling you today?\", lines=4)\n]\n\noutputs = gr.JSON(label=\"MindMate's Support\")\n\ndemo = gr.Interface(\n    fn=chatbot_interface,\n    inputs=inputs,\n    outputs=outputs,\n    title=\"🧠 MindMate: Your Mental Health Companion\",\n    description=\"Talk to MindMate by sharing your name and what's on your mind. Get emotional insights, affirmations, and comfort.\"\n)\n\ndemo.launch()\n\n# --- 📌 Notes for Deployment ---\n# Save this file as app.py\n# Create requirements.txt with:\n# gradio\n# google-generativeai\n# langchain\n# faiss-cpu\n# openai\n# tiktoken\n# Push both to Hugging Face Spaces (Gradio app type)\n# Add your GEMINI_API_KEY under Settings > Secrets\n","metadata":{"_uuid":"bdce8786-d7ca-4786-97c2-1b7a55ba21fb","_cell_guid":"162a3627-87ff-431e-96e1-3fd5cfefce13","trusted":true,"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"execution_count":null}]}
+# 🧠💬 Advanced Mental Health Companion AI Chatbot with Gradio UI
+# Developed using Gemini 1.5 Pro API with RAG, JSON mode, embeddings, and vector search
+
+# --- 📌 Install Required Libraries ---
+import os
+import json
+import gradio as gr
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.docstore.document import Document
+from langchain.text_splitter import CharacterTextSplitter
+
+# --- 📌 Load Gemini API Key ---
+gemini_key = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=gemini_key)
+
+# --- 📌 Initialize Gemini Model ---
+model = genai.GenerativeModel(model_name="gemini-1.5-pro")
+chat = model.start_chat(history=[])
+
+# --- 📌 Define Core Functionalities ---
+def detect_emotion(user_input):
+    prompt = f"""
+    Analyze the message and return a JSON with:
+    {{
+      "emotion": "<Emotion>",
+      "tone": "<Suggested_Tone>",
+      "affirmation": "<One_Line_Affirmation>",
+      "response": "<Comforting response>"
+    }}
+
+    Message: "{user_input}"
+    """
+    response = model.generate_content(prompt, generation_config={"response_mime_type": "application/json"})
+    return json.loads(response.text)
+
+def get_affirmation():
+    prompt = "Provide a unique calming affirmation for someone feeling overwhelmed."
+    return model.generate_content(prompt).text
+
+def get_journaling_prompt():
+    prompt = "Give me a mental health journaling prompt."
+    return model.generate_content(prompt).text
+
+def get_calming_technique():
+    prompt = "Suggest a calming breathing or grounding technique."
+    return model.generate_content(prompt).text
+
+# --- 📌 Memory & Vector Store (RAG Setup) ---
+documents = [
+    Document(page_content="Take a deep breath. You are doing your best."),
+    Document(page_content="It's okay to not be okay. Give yourself grace."),
+    Document(page_content="You are not alone. Many have walked this path and found light again."),
+    Document(page_content="Small steps lead to big changes. Just begin.")
+]
+
+splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
+split_docs = splitter.split_documents(documents)
+embedding = OpenAIEmbeddings(openai_api_key=gemini_key)
+vector_db = FAISS.from_documents(split_docs, embedding)
+
+def retrieve_supportive_text(user_input):
+    docs = vector_db.similarity_search(user_input, k=2)
+    return "\n".join([d.page_content for d in docs])
+
+# --- 📌 Smart Response Generator ---
+def generate_companion_response(name, issue):
+    emotion_data = detect_emotion(issue)
+    rag_support = retrieve_supportive_text(issue)
+
+    system_prompt = f"""
+    You're MindMate, a kind mental health assistant.
+    Respond personally to {name}.
+
+    Emotion: {emotion_data['emotion']}
+    Tone: {emotion_data['tone']}
+    Affirmation: {emotion_data['affirmation']}
+
+    Based on the above, and the following supportive texts:
+    {rag_support}
+
+    Compose a comforting and warm message to the user:
+    Message: {issue}
+    """
+    response = chat.send_message(system_prompt)
+    return response.text, emotion_data['emotion'], emotion_data['affirmation']
+
+# --- 📌 Gradio UI ---
+def chatbot_interface(name, issue):
+    response, emotion, affirmation = generate_companion_response(name, issue)
+    journaling = get_journaling_prompt()
+    technique = get_calming_technique()
+
+    return {
+        "Emotion": emotion,
+        "Affirmation": affirmation,
+        "Companion Response": response,
+        "Journaling Prompt": journaling,
+        "Calming Tip": technique
+    }
+
+inputs = [
+    gr.Textbox(label="Your Name"),
+    gr.Textbox(label="What's troubling you today?", lines=4)
+]
+
+outputs = gr.JSON(label="MindMate's Support")
+
+demo = gr.Interface(
+    fn=chatbot_interface,
+    inputs=inputs,
+    outputs=outputs,
+    title="🧠 MindMate: Your Mental Health Companion",
+    description="Talk to MindMate by sharing your name and what's on your mind. Get emotional insights, affirmations, and comfort."
+)
+
+demo.launch()