Update app.py

app.py

@@ -1,93 +1,94 @@
-from sentence_transformers import SentenceTransformer
-import pandas as pd
-import numpy as np
-import faiss
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-import gradio as gr
-import os
-
-model = SentenceTransformer("all-MiniLM-L6-v2")
-df = pd.read_csv("Final_faqs.csv")
-df["question_plus_answer"] = "Question : " + df["Question"] + " , Answer : " + df["Answer"]
-
-def generate_embeddings(text):
-  embed = model.encode(text)
-  embed = embed / np.linalg.norm(embed)
-  return embed
-
-embeddings = df['question_plus_answer'].apply(generate_embeddings).tolist()
-embeddings_array = np.array(embeddings).astype(np.float32)
-dimensions = embeddings_array.shape[1]
-
-index = faiss.IndexFlatIP(dimensions)
-index.add(embeddings_array)
-
-faiss.write_index(index, "question_answer_embeddings_cosine.index")
-loaded_index = faiss.read_index("question_answer_embeddings_cosine.index")
-
-def search_similar_questions(query, k=3):
-
-    # Generate and normalize query embedding
-    query_embedding = generate_embeddings(query)
-    query_embedding = np.array([query_embedding]).astype('float32')
-
-    # Search in FAISS index (results will be in cosine similarity order)
-    similarities, indices = loaded_index.search(query_embedding, k)
-
-    # Return results (similarities will be between 0 and 1)
-    return df['question_plus_answer'][indices[0][0]]
-
-
-api_key = os.environ.get("GEMINI_API_KEY")
-if api_key is None:
-    raise ValueError("The GEMINI_API_KEY environment variable is not set.")
-
-gemini_model = ChatGoogleGenerativeAI(model='gemini-1.5-flash' , google_api_key=api_key)
-
-parser = StrOutputParser()
-
-from langchain.prompts import ChatPromptTemplate
-
-
-chat_template = ChatPromptTemplate.from_messages([
-    ("system", """You are a helpful and concise assistant trained on a set of Frequently Asked Questions (FAQs). 
-Your goal is to answer user questions based on the most relevant FAQ or let the user know when no direct match is found.
-
-Rules:
-1. If the user greets (e.g., "hi", "hello", "hey", "buddy", "bro", "how are you", etc.), reply with a warm, friendly message and DO NOT reference FAQ data.
-2. If a relevant FAQ is found, use it to answer the user’s question directly but not exactly same as retrieved query , add something by yourself.
-3. If no FAQ matches the query then show the closest retrieved content.
-
-{retrieved_context}
-"""),
-
-    ("human", """User Query:
-{query}
-
-Your Response:""")
-])
-chain = chat_template | gemini_model | parser
-
-import gradio as gr
-# Chatbot logic
-def chatbot_fn(message, history):
-    if message.lower().strip() in ["hi", "hello", "hey", "how are you", "bro", "buddy"]:
-        return "Hey there! 👋 I'm your friendly assistant. Ask me anything from the FAQs!"
-    retrieved = search_similar_questions(message)
-    response = chain.invoke({'retrieved_context': retrieved, 'query': message})
-    return response
-
-# Launch real chatbot UI
-chatbot = gr.ChatInterface(
-    fn=chatbot_fn,
-    title="🤖 FAQ Chatbot",
-    theme="soft",
-    examples=["tell me something about meesho .", "What is the price of i-phone 15 pro 128 gb?", "Can I open the FiftyOne App in a browser?.", "Hey !"],
-    chatbot=gr.Chatbot(show_label=False, avatar_images=("🧑", "🤖")),
-    description="Ask your questions and get instant answers based on FAQs. Try saying 'hi' or ask about a process!",
-)
-
-if __name__ == "__main__":
-    chatbot.launch(share = True)
+from sentence_transformers import SentenceTransformer
+import pandas as pd
+import numpy as np
+import faiss
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+import gradio as gr
+import os
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+df = pd.read_csv("Final_faqs.csv")
+df["question_plus_answer"] = "Question : " + df["Question"] + " , Answer : " + df["Answer"]
+
+def generate_embeddings(text):
+  embed = model.encode(text)
+  embed = embed / np.linalg.norm(embed)
+  return embed
+
+embeddings = df['question_plus_answer'].apply(generate_embeddings).tolist()
+embeddings_array = np.array(embeddings).astype(np.float32)
+dimensions = embeddings_array.shape[1]
+
+index = faiss.IndexFlatIP(dimensions)
+index.add(embeddings_array)
+
+faiss.write_index(index, "question_answer_embeddings_cosine.index")
+loaded_index = faiss.read_index("question_answer_embeddings_cosine.index")
+
+def search_similar_questions(query, k=3):
+
+    # Generate and normalize query embedding
+    query_embedding = generate_embeddings(query)
+    query_embedding = np.array([query_embedding]).astype('float32')
+
+    # Search in FAISS index (results will be in cosine similarity order)
+    similarities, indices = loaded_index.search(query_embedding, k)
+
+    # Return results (similarities will be between 0 and 1)
+    return df['question_plus_answer'][indices[0][0]]
+
+
+api_key = os.environ.get("GEMINI_API_KEY")
+if api_key is None:
+    raise ValueError("The GEMINI_API_KEY environment variable is not set.")
+
+gemini_model = ChatGoogleGenerativeAI(model='gemini-1.5-flash' , google_api_key=api_key)
+
+parser = StrOutputParser()
+
+from langchain.prompts import ChatPromptTemplate
+
+
+chat_template = ChatPromptTemplate.from_messages([
+    ("system", """You are a helpful and concise assistant trained on a set of Frequently Asked Questions (FAQs). 
+Your goal is to answer user questions based on the most relevant FAQ or let the user know when no direct match is found.
+
+Rules:
+1. If the user greets (e.g., "hi", "hello", "hey", "buddy", "bro", "how are you", etc.), reply with a warm, friendly message and DO NOT reference FAQ data.
+2. If a relevant FAQ is found, use it to answer the user’s question directly but not exactly same as retrieved query , add something by yourself.
+3. If no FAQ matches the query then show the closest retrieved content.
+
+{retrieved_context}
+"""),
+
+    ("human", """User Query:
+{query}
+
+Your Response:""")
+])
+chain = chat_template | gemini_model | parser
+
+# Chatbot logic
+def chatbot_fn(message, history):
+    if message.lower().strip() in ["hi", "hello", "hey", "how are you", "bro", "buddy"]:
+        return "Hey there! 👋 I'm your friendly assistant. Ask me anything from the FAQs!"
+    
+    retrieved = search_similar_questions(message)
+    response = chain.invoke({'retrieved_context': retrieved, 'query': message})
+    
+    return f"📄 **Retrieved FAQ Snippet:**\n{retrieved}\n\n🤖 **Answer:**\n{response}"
+
+# Launch real chatbot UI
+chatbot = gr.ChatInterface(
+    fn=chatbot_fn,
+    title="🤖 FAQ Chatbot",
+    theme="soft",
+    examples=["tell me something about meesho .", "What is the price of i-phone 15 pro 128 gb?", "Can I open the FiftyOne App in a browser?.", "Hey !"],
+    chatbot=gr.Chatbot(show_label=False, avatar_images=("🧑", "🤖")),
+    description="Ask your questions and get instant answers based on FAQs. Try saying 'hi' or ask about a process!",
+)
+
+if __name__ == "__main__":
+    chatbot.launch(share=True)