Create app.py

app.py

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+import os
+import openai
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+# Load pre-trained Sentence Transformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Load questions and answers from the CSV file
+df = pd.read_csv('combined_questions_and_answers.csv')
+
+# Encode all questions in the dataset
+question_embeddings = model.encode(df['Question'].tolist())
+
+# OpenAI API key setup
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Function to call OpenAI API to refine and translate text
+def refine_text(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an assistant that refines text to make it conversational and natural. If the question is in Swahili, respond in Swahili."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=800,
+        n=1,
+        stop=None,
+        temperature=0.7
+    )
+    return response['choices'][0]['message']['content']
+
+# Function to find the most similar question and provide the answer
+def get_answer(user_question, threshold=0.80):
+    # Encode the user question
+    user_embedding = model.encode(user_question)
+
+    # Calculate cosine similarities
+    similarities = cosine_similarity([user_embedding], question_embeddings)
+
+    # Find the most similar question
+    max_similarity = np.max(similarities)
+    if max_similarity > threshold:
+        # Get the index of the most similar question
+        similar_question_idx = np.argmax(similarities)
+        # Retrieve the corresponding answer
+        answer = df.iloc[similar_question_idx]['Answer']
+        # Refine the answer using GPT-4
+        refined_answer = refine_text(f"Refine this answer: {answer}")
+        return refined_answer, max_similarity
+    else:
+        # Generate an answer using GPT-4 if no similar question is found
+        refined_answer = refine_text(f"Answer this question: {user_question}")
+        return refined_answer, max_similarity
+
+# Gradio app
+def gradio_app(user_question):
+    answer, similarity = get_answer(user_question)
+    return f"Similarity: {similarity}\nAnswer: {answer}"
+
+# Launch the Gradio app
+iface = gr.Interface(
+    fn=gradio_app,
+    inputs=gr.inputs.Textbox(label="Enter your question"),
+    outputs=gr.outputs.Textbox(label="Answer"),
+    title="Blood Donation Q&A",
+    description="Ask questions related to blood donation and get answers.",
+)
+
+iface.launch()