initial commit

app.py

@@ -1 +1,75 @@
-# sample file 
+import os
+import textwrap
+from dotenv import load_dotenv
+import gradio as gr
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+import pandas as pd
+
+load_dotenv()
+
+MODEL = "microsoft/Phi-3-mini-4k-instruct"
+
+# Load the CSV file
+df = pd.read_csv("dataset.csv")
+
+# Set up components
+prompt_template = """
+Based on the Indian Union Budget data for FY 21-22 to 23-24:
+
+{{budget_data}}
+
+Answer the given question: {{query}}
+Answer:
+"""
+prompt_builder = PromptBuilder(template=prompt_template)
+llm = OpenAIGenerator(
+    api_key=Secret.from_env_var("MONSTER_API_KEY"),
+    api_base_url="https://llm.monsterapi.ai/v1/", 
+    model=MODEL,
+    generation_kwargs={"max_tokens": 512}
+)
+pipeline = Pipeline()
+pipeline.add_component("prompt", prompt_builder)
+pipeline.add_component("llm", llm)
+
+pipeline.connect("prompt.prompt", "llm.prompt")
+
+# Function to handle the query
+# def answer_query(query):
+#     # Convert DataFrame to string representation
+#     budget_data = df.to_string()
+#     result = pipeline.run({"prompt": {"budget_data": budget_data, "query": query}})
+#     return result["llm"]["replies"][0]
+
+
+def answer_query(query):
+    try:
+        # Select a subset of the data (adjust as needed)
+        sample_data = df.sample(n=10).to_string()
+        
+        # Truncate the data if it's too long
+        budget_data = textwrap.shorten(sample_data, width=1000, placeholder="...")
+        
+        result = pipeline.run({"prompt": {"budget_data": budget_data, "query": query}})
+        return result["llm"]["replies"][0]
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Gradio interface
+def chat_interface(query):
+    return answer_query(query)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Indian 2024 Budget Chatbot")
+    query_input = gr.Textbox(label="Enter Your Question")
+    submit_button = gr.Button("Get Answer")
+    output_text = gr.Textbox(label="Answer", interactive=False)
+    
+    submit_button.click(fn=chat_interface, inputs=[query_input], outputs=output_text)
+
+# Run the app locally
+if __name__ == "__main__":
+    demo.launch()