modified

app.py

@@ -4,7 +4,7 @@ from deep_translator import GoogleTranslator
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 import os
-from main import process,audio_process
+# from main import process,audio_process
 from dotenv import load_dotenv
 import base64
 from pathlib import Path
@@ -46,25 +46,27 @@ async def home():
 
 # Token verification function
 def verify_token(token: str):
+    print("token: ",token)
     if token != os.getenv("TOKEN"):
         raise HTTPException(status_code=401, detail="Token not matched")
 
 # Translate endpoint that accepts a query parameter 'text'
-@app.get("/translate")
-async def translate(text: str = "", token: str = ""):
-    if not text or not token:
-        raise HTTPException(status_code=400, detail="No text or token provided")
-    verify_token(token)
-    translator = GoogleTranslator(source="auto", target="mr")
-    result = translator.translate(text)
+# @app.get("/translate")
+# async def translate(text: str = "", token: str = ""):
+#     if not text or not token:
+#         raise HTTPException(status_code=400, detail="No text or token provided")
+#     verify_token(token)
+#     translator = GoogleTranslator(source="auto", target="mr")
+#     result = translator.translate(text)
 
-    return {"result": result}
+#     return {"result": result}
 
 
 @app.post("/chatbot")
 async def chatbot(req:ChatBot):
     query = req.text
     token = req.token
+    print("query : ",query)
     if not query or not token:
         raise HTTPException(status_code=400, detail="No text provided")
     verify_token(token=token)

main.py

@@ -1,58 +1,58 @@
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-from utils.convert_embedding import GetEmbedding
-import random
-import pickle
-import os 
-from utils.rag import RAG
-from faster_whisper import WhisperModel
-
-
-def process(user_query:str):
-    # dump_user_question(user_query)
-    user_embedding = GetEmbedding([user_query]).user_query_emb()
-    with open(r"all_mix_embedding.pkl","rb") as f:
-        load_embedding = pickle.load(f)
-
-    with open(r"all_answers.pkl","rb") as f:
-        ans = pickle.load(f)
-    similarity_scores = cosine_similarity(user_embedding, load_embedding)
-    index = np.argmax(similarity_scores)
-    answer = ans[index]
-    score = similarity_scores[0,index]
-    print(f"Index : {index}:\tscore:{score} \tquery: {user_query}")
-
-    if float(score) > 0.60 :
-        final_output = random.choice(answer)
-    else:
-        final_output = RAG().pipeline(query=user_query)
-
-    return final_output
-
-
-def audio_process(audio):
-    try:
-        model = WhisperModel(model_size_or_path="medium.en")
-        segments, info = model.transcribe(audio)
-        transcription = " ".join([seg.text for seg in segments])
-        result = process(user_query=transcription)
-        return result
-    except Exception as e:
-        print("Error:", e)
-        return str(e)
-
-
-
-
-if __name__ == "__main__":
-    res = audio_process(r"C:\Users\lalit\Documents\Sound recordings\who_is_lalit.m4a")
-    print(res)
-    # for _ in range(3):
+# import numpy as np
+# from sklearn.metrics.pairwise import cosine_similarity
+# from utils.convert_embedding import GetEmbedding
+# import random
+# import pickle
+# import os 
+# from utils.rag import RAG
+# from faster_whisper import WhisperModel
+
+
+# def process(user_query:str):
+#     # dump_user_question(user_query)
+#     user_embedding = GetEmbedding([user_query]).user_query_emb()
+#     with open(r"all_mix_embedding.pkl","rb") as f:
+#         load_embedding = pickle.load(f)
+
+#     with open(r"all_answers.pkl","rb") as f:
+#         ans = pickle.load(f)
+#     similarity_scores = cosine_similarity(user_embedding, load_embedding)
+#     index = np.argmax(similarity_scores)
+#     answer = ans[index]
+#     score = similarity_scores[0,index]
+#     print(f"Index : {index}:\tscore:{score} \tquery: {user_query}")
+
+#     if float(score) > 0.60 :
+#         final_output = random.choice(answer)
+#     else:
+#         final_output = RAG().pipeline(query=user_query)
+
+#     return final_output
+
+
+# def audio_process(audio):
+#     try:
+#         model = WhisperModel(model_size_or_path="medium.en")
+#         segments, info = model.transcribe(audio)
+#         transcription = " ".join([seg.text for seg in segments])
+#         result = process(user_query=transcription)
+#         return result
+#     except Exception as e:
+#         print("Error:", e)
+#         return str(e)
+
+
+
+
+# if __name__ == "__main__":
+#     res = audio_process(r"C:\Users\lalit\Documents\Sound recordings\who_is_lalit.m4a")
+#     print(res)
+#     # for _ in range(3):
     
-    #     user = input("How can i help you :? \n")
-    #     result = process(user)
-    #     print(result)
+#     #     user = input("How can i help you :? \n")
+#     #     result = process(user)
+#     #     print(result)
 
-    # with open(r"data\question_data.pkl","rb") as f:
-    #     que = pickle.load(f)
-    # print(que)
+#     # with open(r"data\question_data.pkl","rb") as f:
+#     #     que = pickle.load(f)
+#     # print(que)

utils/vector_store.py

@@ -1,50 +1,50 @@
-from faiss import IndexFlatL2,write_index,read_index
-import numpy as np
-from utils.convert_embedding import GetEmbedding
-
-
-class VectorStore:
-    def __init__(self):
-        pass
-
-    def store_vectors(self,data:list,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy' ):
-        try:
-            embeddings = GetEmbedding(data=data).convert_emb()
-            diamension = embeddings.shape[1]
-            print("Diamension",diamension)
-            # Create L2 distance index
-            index = IndexFlatL2(diamension)
-
-            index.add(embeddings)
-
-            write_index(index, embedding_space_name)
-
-            # Save embeddings to file
-            np.save(numpy_emb_space, embeddings)
-            return True
-        except Exception as e:
-            print(e)
-            return False
-
-    def get_similary_search(self,query,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy',K:int= 1):
-        # Load the FAISS index
-        index = read_index('faiss_index.index')
-
-        # Load the embeddings
-        embeddings_np = np.load('embeddings.npy')
-
-        # Now you can perform similarity searches on the index
-        query = "What is photosynthesis?"
-        query_embedding = GetEmbedding([query]).convert_emb()
-        query_embedding = query_embedding.detach().numpy()
-        # query_embedding = np.array(query_embedding)  # Convert to numpy array
-        # query_embedding = query_embedding.reshape(1, -1)
-        # print("shape")
-        # print(query_embedding.shape)
-        # Perform search
-        distances, indices = index.search(query_embedding, k = K)
-
-        return indices
+# from faiss import IndexFlatL2,write_index,read_index
+# import numpy as np
+# from utils.convert_embedding import GetEmbedding
+
+
+# class VectorStore:
+#     def __init__(self):
+#         pass
+
+#     def store_vectors(self,data:list,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy' ):
+#         try:
+#             embeddings = GetEmbedding(data=data).convert_emb()
+#             diamension = embeddings.shape[1]
+#             print("Diamension",diamension)
+#             # Create L2 distance index
+#             index = IndexFlatL2(diamension)
+
+#             index.add(embeddings)
+
+#             write_index(index, embedding_space_name)
+
+#             # Save embeddings to file
+#             np.save(numpy_emb_space, embeddings)
+#             return True
+#         except Exception as e:
+#             print(e)
+#             return False
+
+#     def get_similary_search(self,query,embedding_space_name:str = 'faiss_index.index',numpy_emb_space:str = 'embeddings.npy',K:int= 1):
+#         # Load the FAISS index
+#         index = read_index('faiss_index.index')
+
+#         # Load the embeddings
+#         embeddings_np = np.load('embeddings.npy')
+
+#         # Now you can perform similarity searches on the index
+#         query = "What is photosynthesis?"
+#         query_embedding = GetEmbedding([query]).convert_emb()
+#         query_embedding = query_embedding.detach().numpy()
+#         # query_embedding = np.array(query_embedding)  # Convert to numpy array
+#         # query_embedding = query_embedding.reshape(1, -1)
+#         # print("shape")
+#         # print(query_embedding.shape)
+#         # Perform search
+#         distances, indices = index.search(query_embedding, k = K)
+
+#         return indices