Update app.py

app.py

@@ -1,19 +1,18 @@
-import os
 import json
 import gradio as gr
-import google.generativeai as genai
-
+from openai import OpenAI
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import FakeEmbeddings
 from langchain.docstore.document import Document
 from langchain.text_splitter import CharacterTextSplitter
 
-# --- Gemini API Key ---
-genai.configure(api_key="AIzaSyANP-IrxxK_mKwGBHEEOzvaOz-3ihTImjg")
-model = genai.GenerativeModel(model_name="gemini-1.5-pro")
-chat = model.start_chat(history=[])
+# --- DeepSeek R1 API Setup via OpenRouter ---
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key="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",
+)
 
-# --- RAG Docs ---
+# --- Dummy Embeddings and RAG Docs ---
 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."),
@@ -22,9 +21,9 @@ documents = [
 ]
 splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
 split_docs = splitter.split_documents(documents)
-vector_db = FAISS.from_documents(split_docs, FakeEmbeddings(size=768))
+vector_db = FAISS.from_documents(split_docs, FakeEmbeddings())
 
-# --- Emotion Detection ---
+# --- Emotion Detection using DeepSeek R1 ---
 def detect_emotion(user_input):
     prompt = f"""
     Analyze the message and return a JSON with:
@@ -36,50 +35,64 @@ def detect_emotion(user_input):
     }}
     Message: "{user_input}"
     """
-    response = model.generate_content(prompt, generation_config={"response_mime_type": "application/json"})
-    return json.loads(response.text)
+
+    completion = client.chat.completions.create(
+        model="deepseek/deepseek-r1:free",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return json.loads(completion.choices[0].message.content)
 
 # --- Extra Tools ---
 def get_affirmation():
-    return model.generate_content("Give a one-line calming affirmation.").text
+    completion = client.chat.completions.create(
+        model="deepseek/deepseek-r1:free",
+        messages=[{"role": "user", "content": "Give a one-line calming affirmation."}]
+    )
+    return completion.choices[0].message.content
 
 def get_journaling_prompt():
-    return model.generate_content("Give a journaling prompt for mental clarity.").text
+    completion = client.chat.completions.create(
+        model="deepseek/deepseek-r1:free",
+        messages=[{"role": "user", "content": "Give a journaling prompt for mental clarity."}]
+    )
+    return completion.choices[0].message.content
 
 def get_calming_technique():
-    return model.generate_content("Suggest a calming breathing or grounding technique.").text
+    completion = client.chat.completions.create(
+        model="deepseek/deepseek-r1:free",
+        messages=[{"role": "user", "content": "Suggest a calming breathing or grounding technique."}]
+    )
+    return completion.choices[0].message.content
 
-# --- Chat Logic ---
+# --- Chatbot Main Logic ---
 def chatbot_interface(name, issue):
-    try:
-        emotion_data = detect_emotion(issue)
-        rag_support = "\n".join([d.page_content for d in vector_db.similarity_search(issue, k=2)])
+    emotion_data = detect_emotion(issue)
+    rag_support = "\n".join([d.page_content for d in vector_db.similarity_search(issue, k=2)])
 
-        system_prompt = f"""
-        You're MindMate, a kind mental health assistant.
-        Talk directly to {name}.
-        Emotion: {emotion_data['emotion']}
-        Tone: {emotion_data['tone']}
-        Affirmation: {emotion_data['affirmation']}
-        Message: {issue}
-        Based on this and the below RAG support:
-        {rag_support}
-        Write a warm, comforting response.
-        """
-        response = chat.send_message(system_prompt)
-        journaling = get_journaling_prompt()
-        technique = get_calming_technique()
+    system_prompt = f"""
+    You're MindMate, a kind mental health assistant.
+    Talk directly to {name}.
+    Emotion: {emotion_data['emotion']}
+    Tone: {emotion_data['tone']}
+    Affirmation: {emotion_data['affirmation']}
+    Message: {issue}
+    Based on this and the below RAG support:
+    {rag_support}
+    Write a warm, comforting response.
+    """
 
-        return {
-            "Emotion": emotion_data['emotion'],
-            "Affirmation": emotion_data['affirmation'],
-            "Companion Response": response.text,
-            "Journaling Prompt": journaling,
-            "Calming Tip": technique
-        }
+    final_response = client.chat.completions.create(
+        model="deepseek/deepseek-r1:free",
+        messages=[{"role": "user", "content": system_prompt}]
+    )
 
-    except Exception as e:
-        return {"Error": str(e)}
+    return {
+        "Emotion": emotion_data['emotion'],
+        "Affirmation": emotion_data['affirmation'],
+        "Companion Response": final_response.choices[0].message.content,
+        "Journaling Prompt": get_journaling_prompt(),
+        "Calming Tip": get_calming_technique()
+    }
 
 # --- Gradio UI ---
 inputs = [
@@ -94,7 +107,7 @@ demo = gr.Interface(
     inputs=inputs,
     outputs=outputs,
     title="🧠 MindMate: Your Mental Health Companion",
-    description="Just share your name and how you're feeling. MindMate will support you emotionally with warmth, care, and science. No key needed 💖"
+    description="Just share your name and how you're feeling. MindMate will support you emotionally with warmth, care, and science. Powered by DeepSeek R1 via OpenRouter 💖"
 )
 
-demo.launch(share=True)
+demo.launch()