Create app.py

app.py

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+from flask import Flask, redirect, render_template, request, url_for
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import Pinecone
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import CTransformers
+from flask_limiter import Limiter
+from flask_limiter.util import get_remote_address
+from langchain_community.llms import LlamaCpp
+import time 
+
+
+app = Flask(__name__)
+
+# Setup Flask-Limiter
+limiter = Limiter(
+    app=app,
+    key_func=get_remote_address,  # Correctly specify key_func as a keyword argument
+    default_limits=["200 per day", "20 per hour"]
+)
+
+
+# Initialize embeddings directly    
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+prompt_template="""
+Use the following pieces of information to answer the user's question.
+If you don't know the answer say that you don't know it, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the correct answer in human readable text and avoide printing programming code!
+Make it short with no more text than needed and do not repeat your answers or the question!
+"""
+
+
+# Make sure the model path is correct for your system!
+llm = LlamaCpp(
+    model_path="model/phi-2.Q2_K.gguf",
+    temperature=0.1,
+    max_tokens=128,
+    repetition_penalty=1,
+    top_p=1,
+    verbose=True,  # Verbose is required to pass to the callback manager
+)
+
+
+
+PROMPT=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+docsearch = Pinecone.from_existing_index("medicalbot", embeddings)
+retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 2})
+
+qa = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    return_source_documents=True,
+    chain_type_kwargs={"prompt": PROMPT}
+)
+
+
+# chat history
+messages = []
+
+
+@app.route("/", methods=["GET"])
+@limiter.limit("10/minute")  
+def home():
+    return render_template("home.html", messages=messages)
+
+@app.route("/post_message", methods=["POST"])
+def post_message():
+    start_time = time.time()
+    try:
+        msg = request.form['message']
+        messages.append({"sender": "user", "text": msg})
+        if len(messages) > 10:
+            messages.clear()
+
+        bot_response = qa({"query": msg})
+        response_time = time.time() - start_time 
+        response_with_time = f"{bot_response['result']} (Response time: {response_time:.2f} seconds)"
+        messages.append({"sender": "bot", "text": response_with_time})
+    except Exception as e:
+        print(f"Error processing the message: {e}")
+        messages.append({"sender": "bot", "text": "Sorry, I couldn't process your request."})
+
+    return redirect(url_for('home'))
+
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=7860)
+