update app

app.py

@@ -1,54 +1,24 @@
 import gradio as gr
 from mistralai.client import MistralClient, ChatMessage
-import faiss
 import os
-import numpy as np
 from dotenv import load_dotenv
 
 # Load environment variables
 load_dotenv()
 api_key = os.getenv('API_KEY')
 
-# Initialize Mistral client
+# Initialize Mistral client with the API key
 client = MistralClient(api_key=api_key)
 
-# Assuming your embeddings and FAISS index are preloaded or initialized elsewhere
-# For demonstration, these steps are not included here
-# Please replace `index` and `chunks` with your actual data structures
-index = None  # Your FAISS index
-chunks = []   # Your preprocessed text chunks
-
-def get_text_embedding(input_text):
-    """Retrieve text embeddings from Mistral."""
-    embeddings_batch_response = client.embeddings(
-        model="mistral-embed",
-        input=[input_text]
-    )
-    return embeddings_batch_response.data[0].embedding
-
 def answer_question(question):
-    """Generate an answer to the agriculture-related question using Mistral."""
-    # Embed the question
-    question_embedding = np.array([get_text_embedding(question)])
-    
-    # Perform a search for the closest chunks
-    distances, indices = index.search(question_embedding, k=5)  # Adjust `k` as needed
-    
-    # Retrieve and format the relevant chunks as context
-    retrieved_chunks = " ".join([chunks[i] for i in indices.flatten()])
-    prompt = f"""
-    Context information is below.
-    ---------------------
-    {retrieved_chunks}
-    ---------------------
-    Given the context information and not prior knowledge, answer the query.
-    Query: {question}
-    Answer:
-    """
+    """Directly ask Mistral the question and return the answer."""
+    # Format the user's question for Mistral
+    user_message = question
+
+    # Use the run_mistral function to get an answer
+    answer = run_mistral(user_message)
     
-    # Generate response using Mistral with the formatted prompt
-    response = run_mistral(prompt)
-    return response
+    return answer
 
 def run_mistral(user_message, model="mistral-medium"):
     """Interact with Mistral using chat."""
@@ -57,10 +27,10 @@ def run_mistral(user_message, model="mistral-medium"):
     return chat_response.choices[0].message.content
 
 app = gr.Interface(fn=answer_question,
-                   inputs=gr.inputs.Textbox(lines=2, placeholder="Ask a question about agriculture..."),
+                   inputs=gr.inputs.Textbox(lines=2, placeholder="Ask a question..."),
                    outputs="text",
-                   title="Agriculture Assistant",
-                   description="Ask any question about agriculture, and I'll try to provide an informative answer.")
+                   title="Your Assistant",
+                   description="Ask any question, and I'll try to provide an informative answer.")
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)  # Set `share=True` to create a public link