Update app.py

app.py

@@ -1,76 +1,51 @@
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import FakeEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-from openai import OpenAI
-
 import os
+import openai
 import gradio as gr
-import json
-
-# ========== Load Documents and Create Vector DB ==========
-loader = TextLoader("mindmate.txt")
-documents = loader.load()
-
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-split_docs = text_splitter.split_documents(documents)
-
-# Fix for FakeEmbeddings requiring size
-embedding = FakeEmbeddings(size=1536)
-vector_db = FAISS.from_documents(split_docs, embedding)
-
-# ========== OpenRouter DeepSeek R1 Setup ==========
-client = OpenAI(
-    base_url="https://openrouter.ai/api/v1",
-    api_key="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",
-)
-
-# ========== Gradio App ==========
-def mindmate(name, feeling):
-    if not name or not feeling:
-        return "Please enter your name and what's troubling you."
-
-    context_docs = vector_db.similarity_search(feeling, k=2)
-    context_text = "\n".join([doc.page_content for doc in context_docs])
-
-    prompt = f"""You are a compassionate and empathetic mental health support assistant named MindMate.
-The user's name is {name}. They said: "{feeling}"
 
-Based on the context and user's input, respond with a warm, personalized, emotionally intelligent response.
-Use the following context to help, but DO NOT mention it's from a document:
-
-Context:
-{context_text}
-"""
-
-    completion = client.chat.completions.create(
-        model="deepseek/deepseek-r1:free",
-        messages=[{"role": "user", "content": prompt}],
-        extra_headers={
-            "HTTP-Referer": "https://huggingface.co/spaces/dhruvilhere/mindmate-chatbot",
-            "X-Title": "MindMate Chatbot",
-        },
-    )
-
-    return completion.choices[0].message.content
-
-
-# ========== Gradio UI ==========
+# Secure API key loading from Hugging Face Secrets or local env
+openai.api_key = os.getenv("OPENAI_API_KEY")
+openai.api_base = "https://openrouter.ai/api/v1"
+
+# Define how the chatbot should respond
+def get_emotional_response(name, issue):
+    prompt = f"{name} is feeling emotionally low. Reason: {issue}. Give a supportive, warm response like a mental health companion."
+
+    try:
+        response = openai.ChatCompletion.create(
+            model="deepseek-chat",
+            messages=[
+                {"role": "system", "content": "You are a warm and emotionally intelligent mental health companion."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.7
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
+
+# Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("🧠 **MindMate: Your Mental Health Companion**")
-    gr.Markdown("Just share your name and how you're feeling. MindMate will support you emotionally with warmth, care, and science. No key needed ❤️")
+    gr.Markdown(
+        """
+        🧠 **MindMate: Your Mental Health Companion**  
+        Just share your name and how you're feeling.  
+        MindMate will support you emotionally with warmth, care, and science.  
+        _No key needed ❤️_
+        """
+    )
 
     with gr.Row():
-        with gr.Column():
-            name = gr.Textbox(label="Your Name")
-            feeling = gr.Textbox(label="What's troubling you today?", lines=4)
-            submit_btn = gr.Button("Submit", scale=1)
+        with gr.Column(scale=1):
+            name_input = gr.Textbox(label="Your Name", placeholder="e.g. Rashi")
+            issue_input = gr.Textbox(label="What's troubling you today?", placeholder="Tell me anything...")
+
+            submit_btn = gr.Button("Submit")
             clear_btn = gr.Button("Clear")
 
-        with gr.Column():
-            output = gr.JSON(label="🧘 MindMate's Support")
+        with gr.Column(scale=1):
+            output_box = gr.Textbox(label="MindMate Says:", lines=8)
 
-    submit_btn.click(fn=mindmate, inputs=[name, feeling], outputs=output)
-    clear_btn.click(fn=lambda: ("", "", {}), inputs=[], outputs=[name, feeling, output])
+    submit_btn.click(fn=get_emotional_response, inputs=[name_input, issue_input], outputs=output_box)
+    clear_btn.click(fn=lambda: ("", "", ""), inputs=[], outputs=[name_input, issue_input, output_box])
 
-demo.launch()
+demo.launch()