update app.py

app.py

@@ -1,19 +1,60 @@
 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
+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):
-    # This is a placeholder function. You should implement your model inference logic here.
-    # For demonstration purposes, we'll return a generic answer.
-    answers = {
-        "how to detect crop disease": "To detect crop diseases, use image recognition models trained on datasets of diseased and healthy crops.",
-        "best time to plant wheat": "The best time to plant wheat depends on your region. In temperate regions, it's usually early autumn.",
-        "improving soil fertility": "Improving soil fertility can be achieved by rotating crops, using compost, and avoiding overuse of chemical fertilizers.",
-    }
-    # Find the closest question and return the answer
-    question = question.lower()
-    for key in answers:
-        if key in question:
-            return answers[key]
-    return "I'm not sure how to answer that. Can you ask something else?"
+    """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:
+    """
+    
+    # Generate response using Mistral with the formatted prompt
+    response = run_mistral(prompt)
+    return response
+
+def run_mistral(user_message, model="mistral-medium"):
+    """Interact with Mistral using chat."""
+    messages = [ChatMessage(role="user", content=user_message)]
+    chat_response = client.chat(model=model, messages=messages)
+    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..."),