Update 'Supportgpt/supportgpt_mistral.py'

Supportgpt/supportgpt_mistral.py

@@ -1,132 +1,132 @@
-import langchain
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.prompts import PromptTemplate
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import CTransformers
-from langchain.chains import RetrievalQA
-from flask import Flask, request, render_template
-from datetime import datetime
-from flask import Flask, render_template, request, jsonify, session
-
-
-app = Flask(__name__)
-
-
-@app.route('/')
-def home():
-
-    return render_template('index.html')
-
-
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-custom_prompt_template = """Given the following context and a question, generate an answer based on this context only.
-    In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
-    If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer.
-
-    CONTEXT: {context}
-
-    QUESTION: {question}
-
-
-    """
-
-def set_custom_prompt():
-    """
-    Prompt template for QA retrieval for each vectorstore
-    """
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-#Retrieval QA Chain
-def retrieval_qa_chain(llm, prompt, db):
-    qa_chain = RetrievalQA.from_chain_type(llm=llm,
-                                       chain_type='stuff',
-                                       retriever=db.as_retriever(search_kwargs={'k': 2}),
-                                       return_source_documents=True,
-                                       chain_type_kwargs={'prompt': prompt}
-                                       )
-    return qa_chain
-  
-#Loading the model
-def load_llm():
-    # Load the locally downloaded model here
-    # llm = CTransformers(
-    #     model = r"C:\Aiproject\Llama-2-7B-Chat-GGML\Llama-2-7B-Chat-GGML\llama-30b.ggmlv3.q8_0.bin",
-    #     model_type="llama",
-    #     max_new_tokens = 512,
-    #     temperature = 0.5
-    # )
-    llm = CTransformers(model=r"D:\Aiproject\models\mistral-7b-instruct-v0.1.Q4_K_M.gguf",gpu_layers=100,config={'max_new_tokens': 128, 'temperature': 0.01})
-    return llm
-
-#QA Model Function
-def qa_bot():
-    embeddings = HuggingFaceEmbeddings(model_name=r"D:\Aiproject\models\sentence_tranformer\all-MiniLM-L6-v2",
-                                       model_kwargs={'device': 'cpu'})
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings)
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, db)
-
-    return qa
-
-#output function
-def final_result(query):
-    qa_result = qa_bot()
-    response = qa_result({'query': query})
-    return response
-
-# Streamlit application
-# st.title("Medical Bot")
-
-# if "query" not in st.session_state:
-#     st.text("Hi, Welcome to Medical Bot. What is your query?")
-#     query = st.text_input("Enter your question")
-#     st.button("Ask")
-
-#     if query:
-#         st.text("Searching for relevant documents...")
-#         response = final_result(query)
-#         st.text(f"Sources: {response['source_documents']}")
-#         st.text(f"Answer: {response['result']}")
-# else:
-#     st.text("No query")
-
-
-
-
-@app.route('/user_input', methods=['GET', 'POST'])
-def index():
-    if request.method == 'POST':
-        query = request.form.get('user_input')
-        session_id= request.form.get('session_id')
-        #query = request.form['query']
-        start=datetime.now()
-        frmtstart=start.strftime('%Y-%m-%d %H:%M:%S')
-        print('started',frmtstart)
-        user_input=query.lower()
-        print(user_input)
-        if query:
-            #result = chain(question)['result']
-            response = final_result(user_input)
-            result=response['result']
-            print(result)
-            end=datetime.now()
-            endtime=end.strftime('%Y-%m-%d %H:%M:%S')
-            print('Ended',endtime)
-
-      
-        else:
-            result = None
-        #return render_template('index.html', output=result)
-        return jsonify({'output': result})
-    return render_template('index.html', result=None)
-
-
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0",port=8000,debug=False)
+import langchain
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.prompts import PromptTemplate
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import CTransformers
+from langchain.chains import RetrievalQA
+from flask import Flask, request, render_template
+from datetime import datetime
+from flask import Flask, render_template, request, jsonify, session
+
+
+app = Flask(__name__)
+
+
+@app.route('/')
+def home():
+
+    return render_template('index.html')
+
+
+
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+custom_prompt_template = """Given the following context and a question, generate an answer based on this context only.
+    In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
+    If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer.
+
+    CONTEXT: {context}
+
+    QUESTION: {question}
+
+
+    """
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for each vectorstore
+    """
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+#Retrieval QA Chain
+def retrieval_qa_chain(llm, prompt, db):
+    qa_chain = RetrievalQA.from_chain_type(llm=llm,
+                                       chain_type='stuff',
+                                       retriever=db.as_retriever(search_kwargs={'k': 2}),
+                                       return_source_documents=True,
+                                       chain_type_kwargs={'prompt': prompt}
+                                       )
+    return qa_chain
+  
+#Loading the model
+def load_llm():
+    # Load the locally downloaded model here
+    # llm = CTransformers(
+    #     model = r"C:\Aiproject\Llama-2-7B-Chat-GGML\Llama-2-7B-Chat-GGML\llama-30b.ggmlv3.q8_0.bin",
+    #     model_type="llama",
+    #     max_new_tokens = 512,
+    #     temperature = 0.5
+    # )
+    llm = CTransformers(model=r"TheBloke/Mistral-7B-Instruct-v0.1-GGUF",gpu_layers=100,config={'max_new_tokens': 128, 'temperature': 0.01})
+    return llm
+
+#QA Model Function
+def qa_bot():
+    embeddings = HuggingFaceEmbeddings(model_name=r"sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': 'cpu'})
+    db = FAISS.load_local(DB_FAISS_PATH, embeddings)
+    llm = load_llm()
+    qa_prompt = set_custom_prompt()
+    qa = retrieval_qa_chain(llm, qa_prompt, db)
+
+    return qa
+
+#output function
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query': query})
+    return response
+
+# Streamlit application
+# st.title("Medical Bot")
+
+# if "query" not in st.session_state:
+#     st.text("Hi, Welcome to Medical Bot. What is your query?")
+#     query = st.text_input("Enter your question")
+#     st.button("Ask")
+
+#     if query:
+#         st.text("Searching for relevant documents...")
+#         response = final_result(query)
+#         st.text(f"Sources: {response['source_documents']}")
+#         st.text(f"Answer: {response['result']}")
+# else:
+#     st.text("No query")
+
+
+
+
+@app.route('/user_input', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        query = request.form.get('user_input')
+        session_id= request.form.get('session_id')
+        #query = request.form['query']
+        start=datetime.now()
+        frmtstart=start.strftime('%Y-%m-%d %H:%M:%S')
+        print('started',frmtstart)
+        user_input=query.lower()
+        print(user_input)
+        if query:
+            #result = chain(question)['result']
+            response = final_result(user_input)
+            result=response['result']
+            print(result)
+            end=datetime.now()
+            endtime=end.strftime('%Y-%m-%d %H:%M:%S')
+            print('Ended',endtime)
+
+      
+        else:
+            result = None
+        #return render_template('index.html', output=result)
+        return jsonify({'output': result})
+    return render_template('index.html', result=None)
+
+
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0",port=8000,debug=False)