Uploaded app.py

app (1).py

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+import pandas as pd
+from langchain_groq import ChatGroq
+from langchain_experimental.agents.agent_toolkits import create_csv_agent
+import gradio as gr
+
+# Reading a CSV file
+csv_file_path = '/content/final_dataset.csv'
+df_csv = pd.read_csv(csv_file_path)
+
+# Initialize the Groq API and the LLM
+groq_api = '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'
+llm = ChatGroq(temperature=0, model="llama3-70b-8192", api_key=groq_api)
+
+# Create the CSV agent
+agent = create_csv_agent(llm, csv_file_path, verbose=True, allow_dangerous_code=True, handle_parsing_errors=True)
+
+# Function to query data
+def query_data(query):
+    response = agent.invoke(query)
+    return response
+
+# Function for Gradio interface
+def get_recommendations(user_query):
+    additional_query = '''If you can't find the exact response from the dataframe, you can give the responses similar to the query.
+    If you recommend some places, give their descriptions too in a paragraph.
+    While giving descriptions, give from the reviews of that place in more than 30 words.
+    If there are 'hotels' in the query, recommend hotels only not the restaurants and adventures. And same for other place types.
+    Give their revel ratings too.
+    Don't try give the descriptions that are not provided in the dataframe.
+    Try to minimize giving the same descriptions for more than one places. '''
+    
+    full_query = f"{user_query} {additional_query}"
+    response = query_data(full_query)
+    return response['output']
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=get_recommendations,
+    inputs=gr.Textbox(label="Ask your query about hotels:", placeholder="Type your query here..."),
+    outputs=gr.Textbox(label="Response will appear here."),
+    title="Hotel and Restaurant Recommendation System",
+    description="Ask for recommendations based on the data in the CSV file."
+)
+
+# Launch the Gradio app
+iface.launch(share=True)