Create app.py

app.py

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+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain_community.llms import LlamaCpp
+from langchain.chains import RetrievalQA
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain.prompts import ChatPromptTemplate
+import gradio as gr
+import os
+
+# Set your Hugging Face API key
+os.environ['HF_API_KEY']
+api_key = os.getenv('HF_API_KEY')
+if api_key is None:
+    raise ValueError("Hugging Face API key is not set. Please set it in your environment.")
+
+# Load documents from a directory
+loader = PyPDFDirectoryLoader("/content/drive/MyDrive/HealthCareData/")
+docs = loader.load()
+
+# Split documents into chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
+chunks = text_splitter.split_documents(docs)
+
+# Create embeddings and vector store
+embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
+vectorstore = Chroma.from_documents(chunks, embeddings)
+
+# Create a retriever
+retriever = vectorstore.as_retriever(search_kwargs={'k': 5})
+
+# Load the LLM (LlamaCpp)
+llm = LlamaCpp(
+    model_path='/content/drive/MyDrive/HealthCareData/mistral-7b-instruct-v0.1.Q6_K.gguf',
+    temperature=0.2,
+    max_tokens=2048,
+    top_p=1
+)
+
+# Define the prompt template
+template = """
+You are a Medical Assistant that follows the instructions and generates accurate responses based on the query and the context provided. Please be truthful and give direct answers.
+
+{query}
+"""
+
+prompt = ChatPromptTemplate.from_template(template)
+
+# Create the retrieval chain
+retrieval_chain = (
+    {"context": retriever, "query": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+# Define the chat function for Gradio
+def chat(user_input):
+    if user_input.lower() == 'exit':
+        return "Exiting..."
+    if not user_input.strip():
+        return "Please enter a valid query."
+    result = retrieval_chain.invoke(user_input)
+    return result
+
+# Create Gradio interface with improved design
+iface = gr.Interface(
+    fn=chat,
+    inputs=gr.Textbox(label="Your Query", placeholder="Type your question here...", lines=2),
+    outputs=gr.Textbox(label="Response"),
+    title="🩺 BioMistral Medical Chatbot",
+    description="🤖 Ask me any healthcare or biology-related queries!",
+    theme="soft",
+    live=True,
+    css="""
+        body {
+            background-color: #f0f4f8;
+            color: #333;
+        }
+        .gradio-container {
+            border-radius: 12px;
+            box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
+            background: #ffffff;
+            padding: 20px;
+        }
+        input, textarea {
+            border-radius: 8px;
+            border: 1px solid #ddd;
+            padding: 10px;
+        }
+        button {
+            background-color: #007bff;
+            color: white;
+            border-radius: 8px;
+            padding: 10px 15px;
+            border: none;
+            transition: 0.3s;
+        }
+        button:hover {
+            background-color: #0056b3;
+        }
+    """
+)
+
+# Launch the Gradio app
+iface.launch()