Update app.py

app.py

@@ -1,64 +1,144 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import pandas as pd
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+df = pd.read_csv('./drugs_side_effects_drugs_com.csv')
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+df = df[['drug_name', 'medical_condition', 'side_effects']]
+df.dropna(inplace=True)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+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)
 
-    messages.append({"role": "user", "content": message})
+import os
 
-    response = ""
+# Get the secret key from the environment
+groq_key = os.environ.get('gloq_key')
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+## LLM used for RAG
+from langchain_groq import ChatGroq
 
-        response += token
-        yield response
+llm = ChatGroq(model="llama-3.1-70b-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,
+    persist_directory="./",
+)
+
+# add data to vector nstore
+vectorstore.add_texts(context_data)
+
+retriever = vectorstore.as_retriever()
+
+from langchain_core.prompts import PromptTemplate
+
+template = ("""You are a pharmacist and medical expert.
+    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
+
+# Function to stream responses
+def rag_memory_stream(text):
+    partial_text = ""
+    for new_text in rag_chain.stream(text):  # Assuming rag_chain is pre-defined
+        partial_text += new_text
+        yield partial_text
+
+# Title and description for the app
+title = "AI Medical Assistant for Drug Information and Side Effects"
+description = """
+This AI-powered chatbot is designed to provide reliable information about drugs, their side effects, and related medical conditions.
+It utilizes the Groq API and LangChain to deliver real-time, accurate responses. 
+
+Ask questions like:
+1. What are the side effects of taking aspirin daily?
+2. What is the recommended treatment for a common cold?
+3. What is the disease for constant fatigue and muscle weakness?
+4. What are the symptoms of diabetes?
+5. How can hypertension be managed?
+
+**Disclaimer:** This chatbot is for informational purposes only and is not a substitute for professional medical advice.
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+
+# Customizing Gradio interface for a better look
+demo = gr.Interface(
+    fn=rag_memory_stream,
+    inputs=gr.Textbox(
+        lines=2, 
+        placeholder="Type your medical question here...", 
+        label="Your Medical Question"
+    ),
+    outputs=gr.Textbox(
+        lines=10, 
+        label="AI Response"
+    ),
+    title=title,
+    description=description,
+    theme="compact",  # Adding a compact theme for a polished look
+    allow_flagging="never"
 )
 
+# # Launching the app
+# demo.launch(share=True)
+
+# import gradio as gr
+
+# def rag_memory_stream(text):
+#     partial_text = ""
+#     for new_text in rag_chain.stream(text):
+#         partial_text += new_text
+#         yield partial_text
+
+# examples = ['I feel dizzy', 'what is the possible sickness for fatigue']
+
+
+
+
+# title = "Real-time AI App with Groq API and LangChain to Answer medical questions"
+# demo = gr.Interface(
+#     title=title,
+#     fn=rag_memory_stream,
+#     inputs="text",
+#     outputs="text",
+#     examples=examples,
+#     allow_flagging="never",
+# )
+
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()