Upload app.py

app.py

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+import pandas as pd
+
+
+df = pd.read_csv('./Mental_Health_FAQ.csv')
+
+context_data = []
+for i in range(len(df)):
+  context = ""
+  for j in range(3):
+    context += df.columns[j]
+    context += ": "
+    context += df.iloc[i][j]
+    context += " "
+  context_data.append(context)
+
+
+import os
+
+# Get the secret key from the environment
+groq_key = os.environ.get('new_chatAPI_key')
+
+## LLM used for RAG
+from langchain_groq import ChatGroq
+
+llm = ChatGroq(model="llama-3.3-70b-versatile",api_key=groq_key)
+
+## Embedding model!
+from langchain_huggingface import HuggingFaceEmbeddings
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# create vector store!
+from langchain_chroma import Chroma
+
+vectorstore = Chroma(
+    collection_name="medical_dataset_store",
+    embedding_function=embed_model,
+)
+
+# add data to vector nstore
+vectorstore.add_texts(context_data)
+
+retriever = vectorstore.as_retriever()
+
+from langchain_core.prompts import PromptTemplate
+
+template = ("""You are a mental health professional.
+    Use the provided context to answer the question.
+    If you don't know the answer, say so. Explain your answer in detail.
+    Do not discuss the context in your response; just provide the answer directly.
+
+    Context: {context}
+
+    Question: {question}
+
+    Answer:""")
+
+rag_prompt = PromptTemplate.from_template(template)
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | rag_prompt
+    | llm
+    | StrOutputParser()
+)
+
+import gradio as gr
+
+def rag_memory_stream(message, history):
+    partial_text = ""
+    for new_text in rag_chain.stream(message):
+        partial_text += new_text
+        yield partial_text
+
+examples = [
+    "I am not in a good mood", 
+    "what is the possible symptompts of depression?"
+]
+
+description = "Real-time AI App with Groq API and LangChain to Answer medical questions"
+
+
+title = "ThriveTalk Expert :) Try me!"
+demo = gr.ChatInterface(fn=rag_memory_stream,
+                        type="messages",
+                        title=title,
+                        description=description,
+                        fill_height=True,
+                        examples=examples,
+                        theme="glass",
+)
+
+
+if __name__ == "__main__":
+    demo.launch()